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[WIP] Fix Pyright static type checking by replacing if-else imports with try-except (#16578)

Browse Source 
* rebase and isort

* modify cookiecutter init

* fix cookiecutter auto imports

* fix clean_frameworks_in_init

* fix add_model_to_main_init

* blackify

* replace unnecessary f-strings

* update yolos imports

* fix roberta import bug

* fix yolos missing dependency

* fix add_model_like and cookiecutter bug

* fix repository consistency error

* modify cookiecutter, fix add_new_model_like

* remove stale line

Co-authored-by: Dom Miketa <dmiketa@exscientia.co.uk>
This commit is contained in:
Dom Miketa 2022-05-09 16:28:53 +01:00 committed by GitHub
parent 7783fa6bb3
commit df735d1317
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GPG Key ID: 4AEE18F83AFDEB23
116 changed files with 3844 additions and 756 deletions
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272
src/transformers/__init__.py
View File

@ -29,6 +29,7 @@ from typing import TYPE_CHECKING
# Check the dependencies satisfy the minimal versions required.
from . import dependency_versions_check
from .utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_scatter_available,
@ -412,7 +413,16 @@ _import_structure = {
}
# sentencepiece-backed objects
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_sentencepiece_objects
_import_structure["utils.dummy_sentencepiece_objects"] = [
name for name in dir(dummy_sentencepiece_objects) if not name.startswith("_")
]
else:
_import_structure["models.albert"].append("AlbertTokenizer")
_import_structure["models.barthez"].append("BarthezTokenizer")
_import_structure["models.bartpho"].append("BartphoTokenizer")
@ -439,16 +449,19 @@ if is_sentencepiece_available():
_import_structure["models.xlm_prophetnet"].append("XLMProphetNetTokenizer")
_import_structure["models.xlm_roberta"].append("XLMRobertaTokenizer")
_import_structure["models.xlnet"].append("XLNetTokenizer")
else:
from .utils import dummy_sentencepiece_objects
_import_structure["utils.dummy_sentencepiece_objects"] = [
name for name in dir(dummy_sentencepiece_objects) if not name.startswith("_")
]
# tokenizers-backed objects
if is_tokenizers_available():
# Fast tokenizers
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_tokenizers_objects
_import_structure["utils.dummy_tokenizers_objects"] = [
name for name in dir(dummy_tokenizers_objects) if not name.startswith("_")
]
else:
# Fast tokenizers structure
_import_structure["models.albert"].append("AlbertTokenizerFast")
_import_structure["models.bart"].append("BartTokenizerFast")
_import_structure["models.barthez"].append("BarthezTokenizerFast")
@ -498,43 +511,55 @@ if is_tokenizers_available():
_import_structure["models.xlnet"].append("XLNetTokenizerFast")
_import_structure["tokenization_utils_fast"] = ["PreTrainedTokenizerFast"]
else:
from .utils import dummy_tokenizers_objects
_import_structure["utils.dummy_tokenizers_objects"] = [
name for name in dir(dummy_tokenizers_objects) if not name.startswith("_")
]
if is_sentencepiece_available() and is_tokenizers_available():
_import_structure["convert_slow_tokenizer"] = ["SLOW_TO_FAST_CONVERTERS", "convert_slow_tokenizer"]
else:
try:
if not (is_sentencepiece_available() and is_tokenizers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_sentencepiece_and_tokenizers_objects
_import_structure["utils.dummy_sentencepiece_and_tokenizers_objects"] = [
name for name in dir(dummy_sentencepiece_and_tokenizers_objects) if not name.startswith("_")
]
else:
_import_structure["convert_slow_tokenizer"] = ["SLOW_TO_FAST_CONVERTERS", "convert_slow_tokenizer"]
# Speech-specific objects
if is_speech_available():
_import_structure["models.speech_to_text"].append("Speech2TextFeatureExtractor")
else:
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_speech_objects
_import_structure["utils.dummy_speech_objects"] = [
name for name in dir(dummy_speech_objects) if not name.startswith("_")
]
if is_sentencepiece_available() and is_speech_available():
_import_structure["models.speech_to_text"].append("Speech2TextProcessor")
else:
_import_structure["models.speech_to_text"].append("Speech2TextFeatureExtractor")
try:
if not (is_sentencepiece_available() and is_speech_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_sentencepiece_and_speech_objects
_import_structure["utils.dummy_sentencepiece_and_speech_objects"] = [
name for name in dir(dummy_sentencepiece_and_speech_objects) if not name.startswith("_")
]
else:
_import_structure["models.speech_to_text"].append("Speech2TextProcessor")
# Vision-specific objects
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_vision_objects
_import_structure["utils.dummy_vision_objects"] = [
name for name in dir(dummy_vision_objects) if not name.startswith("_")
]
else:
_import_structure["image_utils"] = ["ImageFeatureExtractionMixin"]
_import_structure["models.beit"].append("BeitFeatureExtractor")
_import_structure["models.clip"].append("CLIPFeatureExtractor")
@ -556,15 +581,18 @@ if is_vision_available():
_import_structure["models.vilt"].append("ViltProcessor")
_import_structure["models.vit"].append("ViTFeatureExtractor")
_import_structure["models.yolos"].append("YolosFeatureExtractor")
else:
from .utils import dummy_vision_objects
_import_structure["utils.dummy_vision_objects"] = [
name for name in dir(dummy_vision_objects) if not name.startswith("_")
]
# Timm-backed objects
if is_timm_available() and is_vision_available():
try:
if not (is_timm_available() and is_vision_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_timm_objects
_import_structure["utils.dummy_timm_objects"] = [
name for name in dir(dummy_timm_objects) if not name.startswith("_")
]
else:
_import_structure["models.detr"].extend(
[
"DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
@ -574,14 +602,17 @@ if is_timm_available() and is_vision_available():
"DetrPreTrainedModel",
]
)
else:
from .utils import dummy_timm_objects
_import_structure["utils.dummy_timm_objects"] = [
name for name in dir(dummy_timm_objects) if not name.startswith("_")
try:
if not is_scatter_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_scatter_objects
_import_structure["utils.dummy_scatter_objects"] = [
name for name in dir(dummy_scatter_objects) if not name.startswith("_")
]
if is_scatter_available():
else:
_import_structure["models.tapas"].extend(
[
"TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
@ -593,16 +624,17 @@ if is_scatter_available():
"load_tf_weights_in_tapas",
]
)
else:
from .utils import dummy_scatter_objects
_import_structure["utils.dummy_scatter_objects"] = [
name for name in dir(dummy_scatter_objects) if not name.startswith("_")
]
# PyTorch-backed objects
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_pt_objects
_import_structure["utils.dummy_pt_objects"] = [name for name in dir(dummy_pt_objects) if not name.startswith("_")]
else:
_import_structure["activations"] = []
_import_structure["benchmark.benchmark"] = ["PyTorchBenchmark"]
_import_structure["benchmark.benchmark_args"] = ["PyTorchBenchmarkArguments"]
@ -1723,13 +1755,16 @@ if is_torch_available():
_import_structure["trainer"] = ["Trainer"]
_import_structure["trainer_pt_utils"] = ["torch_distributed_zero_first"]
_import_structure["trainer_seq2seq"] = ["Seq2SeqTrainer"]
else:
from .utils import dummy_pt_objects
_import_structure["utils.dummy_pt_objects"] = [name for name in dir(dummy_pt_objects) if not name.startswith("_")]
# TensorFlow-backed objects
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_tf_objects
_import_structure["utils.dummy_tf_objects"] = [name for name in dir(dummy_tf_objects) if not name.startswith("_")]
else:
_import_structure["activations_tf"] = []
_import_structure["benchmark.benchmark_args_tf"] = ["TensorFlowBenchmarkArguments"]
_import_structure["benchmark.benchmark_tf"] = ["TensorFlowBenchmark"]
@ -2236,13 +2271,18 @@ if is_tf_available():
_import_structure["tf_utils"] = []
_import_structure["trainer_tf"] = ["TFTrainer"]
else:
from .utils import dummy_tf_objects
_import_structure["utils.dummy_tf_objects"] = [name for name in dir(dummy_tf_objects) if not name.startswith("_")]
# FLAX-backed objects
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_flax_objects
_import_structure["utils.dummy_flax_objects"] = [
name for name in dir(dummy_flax_objects) if not name.startswith("_")
]
else:
_import_structure["generation_flax_logits_process"] = [
"FlaxForcedBOSTokenLogitsProcessor",
"FlaxForcedEOSTokenLogitsProcessor",
@ -2469,12 +2509,6 @@ if is_flax_available():
"FlaxXLMRobertaModel",
]
)
else:
from .utils import dummy_flax_objects
_import_structure["utils.dummy_flax_objects"] = [
name for name in dir(dummy_flax_objects) if not name.startswith("_")
]
# Direct imports for type-checking
@ -2816,7 +2850,12 @@ if TYPE_CHECKING:
logging,
)
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_sentencepiece_objects import *
else:
from .models.albert import AlbertTokenizer
from .models.barthez import BarthezTokenizer
from .models.bartpho import BartphoTokenizer
@ -2842,10 +2881,14 @@ if TYPE_CHECKING:
from .models.xlm_prophetnet import XLMProphetNetTokenizer
from .models.xlm_roberta import XLMRobertaTokenizer
from .models.xlnet import XLNetTokenizer
else:
from .utils.dummy_sentencepiece_objects import *
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_tokenizers_objects import *
else:
# Fast tokenizers imports
from .models.albert import AlbertTokenizerFast
from .models.bart import BartTokenizerFast
from .models.barthez import BarthezTokenizerFast
@ -2893,25 +2936,36 @@ if TYPE_CHECKING:
from .models.xlnet import XLNetTokenizerFast
from .tokenization_utils_fast import PreTrainedTokenizerFast
else:
from .utils.dummy_tokenizers_objects import *
if is_sentencepiece_available() and is_tokenizers_available():
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, convert_slow_tokenizer
else:
try:
if not (is_sentencepiece_available() and is_tokenizers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummies_sentencepiece_and_tokenizers_objects import *
if is_speech_available():
from .models.speech_to_text import Speech2TextFeatureExtractor
else:
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, convert_slow_tokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_speech_objects import *
if is_speech_available() and is_sentencepiece_available():
from .models.speech_to_text import Speech2TextProcessor
else:
from .utils.dummy_sentencepiece_and_speech_objects import *
from .models.speech_to_text import Speech2TextFeatureExtractor
if is_vision_available():
try:
if not (is_speech_available() and is_sentencepiece_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_sentencepiece_and_speech_objects import *
else:
from .models.speech_to_text import Speech2TextProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_vision_objects import *
else:
from .image_utils import ImageFeatureExtractionMixin
from .models.beit import BeitFeatureExtractor
from .models.clip import CLIPFeatureExtractor, CLIPProcessor
@ -2930,11 +2984,14 @@ if TYPE_CHECKING:
from .models.vilt import ViltFeatureExtractor, ViltProcessor
from .models.vit import ViTFeatureExtractor
from .models.yolos import YolosFeatureExtractor
else:
from .utils.dummy_vision_objects import *
# Modeling
if is_timm_available() and is_vision_available():
try:
if not (is_timm_available() and is_vision_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_timm_objects import *
else:
from .models.detr import (
DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
DetrForObjectDetection,
@ -2942,10 +2999,13 @@ if TYPE_CHECKING:
DetrModel,
DetrPreTrainedModel,
)
else:
from .utils.dummy_timm_objects import *
if is_scatter_available():
try:
if not is_scatter_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_scatter_objects import *
else:
from .models.tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
@ -2955,10 +3015,13 @@ if TYPE_CHECKING:
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
else:
from .utils.dummy_scatter_objects import *
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import *
else:
# Benchmarks
from .benchmark.benchmark import PyTorchBenchmark
from .benchmark.benchmark_args import PyTorchBenchmarkArguments
@ -3005,6 +3068,8 @@ if TYPE_CHECKING:
)
from .generation_utils import top_k_top_p_filtering
from .modeling_utils import PreTrainedModel
# PyTorch model imports
from .models.albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
@ -3896,12 +3961,16 @@ if TYPE_CHECKING:
from .trainer import Trainer
from .trainer_pt_utils import torch_distributed_zero_first
from .trainer_seq2seq import Seq2SeqTrainer
else:
from .utils.dummy_pt_objects import *
# TensorFlow
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
# Import the same objects as dummies to get them in the namespace.
# They will raise an import error if the user tries to instantiate / use them.
from .utils.dummy_tf_objects import *
else:
from .benchmark.benchmark_args_tf import TensorFlowBenchmarkArguments
# Benchmarks
@ -3932,6 +4001,8 @@ if TYPE_CHECKING:
TFLayoutLMPreTrainedModel,
)
from .modeling_tf_utils import TFPreTrainedModel, TFSequenceSummary, TFSharedEmbeddings, shape_list
# TensorFlow model imports
from .models.albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
@ -4310,13 +4381,14 @@ if TYPE_CHECKING:
# Trainer
from .trainer_tf import TFTrainer
else:
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
# Import the same objects as dummies to get them in the namespace.
# They will raise an import error if the user tries to instantiate / use them.
from .utils.dummy_tf_objects import *
if is_flax_available():
from .utils.dummy_flax_objects import *
else:
from .generation_flax_logits_process import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
@ -4329,6 +4401,8 @@ if TYPE_CHECKING:
FlaxTopPLogitsWarper,
)
from .modeling_flax_utils import FlaxPreTrainedModel
# Flax model imports
from .models.albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
@ -4494,10 +4568,6 @@ if TYPE_CHECKING:
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
)
else:
# Import the same objects as dummies to get them in the namespace.
# They will raise an import error if the user tries to instantiate / use them.
from .utils.dummy_flax_objects import *
else:
import sys

21
src/transformers/commands/add_new_model_like.py
View File

@ -766,7 +766,9 @@ def clean_frameworks_in_init(
return
remove_pattern = "|".join(to_remove)
re_conditional_imports = re.compile(rf"^\s*if is_({remove_pattern})_available\(\):\s*$")
re_conditional_imports = re.compile(rf"^\s*if not is_({remove_pattern})_available\(\):\s*$")
re_try = re.compile(r"\s*try:")
re_else = re.compile(r"\s*else:")
re_is_xxx_available = re.compile(rf"is_({remove_pattern})_available")
with open(init_file, "r", encoding="utf-8") as f:
@ -776,11 +778,15 @@ def clean_frameworks_in_init(
new_lines = []
idx = 0
while idx < len(lines):
# Conditional imports
if re_conditional_imports.search(lines[idx]) is not None:
# Conditional imports in try-except-else blocks
if (re_conditional_imports.search(lines[idx]) is not None) and (re_try.search(lines[idx - 1]) is not None):
# Remove the preceding `try:`
new_lines.pop()
idx += 1
while is_empty_line(lines[idx]):
# Iterate until `else:`
while is_empty_line(lines[idx]) or re_else.search(lines[idx]) is None:
idx += 1
idx += 1
indent = find_indent(lines[idx])
while find_indent(lines[idx]) >= indent or is_empty_line(lines[idx]):
idx += 1
@ -790,6 +796,7 @@ def clean_frameworks_in_init(
for framework in to_remove:
line = line.replace(f", is_{framework}_available", "")
line = line.replace(f"is_{framework}_available, ", "")
line = line.replace(f"is_{framework}_available,", "")
line = line.replace(f"is_{framework}_available", "")
if len(line.strip()) > 0:
@ -836,11 +843,11 @@ def add_model_to_main_init(
while idx < len(lines):
if not is_empty_line(lines[idx]) and find_indent(lines[idx]) == 0:
framework = None
elif lines[idx].lstrip().startswith("if is_torch_available"):
elif lines[idx].lstrip().startswith("if not is_torch_available"):
framework = "pt"
elif lines[idx].lstrip().startswith("if is_tf_available"):
elif lines[idx].lstrip().startswith("if not is_tf_available"):
framework = "tf"
elif lines[idx].lstrip().startswith("if is_flax_available"):
elif lines[idx].lstrip().startswith("if not is_flax_available"):
framework = "flax"
# Skip if we are in a framework not wanted.

71
src/transformers/models/albert/__init__.py
View File

@ -19,6 +19,7 @@
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
@ -32,13 +33,28 @@ _import_structure = {
"configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_albert"] = ["AlbertTokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_albert_fast"] = ["AlbertTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_albert"] = [
"ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"AlbertForMaskedLM",
@ -52,7 +68,12 @@ if is_torch_available():
"load_tf_weights_in_albert",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_albert"] = [
"TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFAlbertForMaskedLM",
@ -66,7 +87,12 @@ if is_tf_available():
"TFAlbertPreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_albert"] = [
"FlaxAlbertForMaskedLM",
"FlaxAlbertForMultipleChoice",
@ -81,13 +107,28 @@ if is_flax_available():
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
@ -101,7 +142,12 @@ if TYPE_CHECKING:
load_tf_weights_in_albert,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
@ -115,7 +161,12 @@ if TYPE_CHECKING:
TFAlbertPreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,

50
src/transformers/models/auto/__init__.py
View File

@ -18,7 +18,13 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {
@ -29,7 +35,12 @@ _import_structure = {
"tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_auto"] = [
"MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
"MODEL_FOR_AUDIO_XVECTOR_MAPPING",
@ -81,7 +92,12 @@ if is_torch_available():
"AutoModelWithLMHead",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_auto"] = [
"TF_MODEL_FOR_CAUSAL_LM_MAPPING",
"TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
@ -115,7 +131,12 @@ if is_tf_available():
"TFAutoModelWithLMHead",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_auto"] = [
"FLAX_MODEL_FOR_CAUSAL_LM_MAPPING",
"FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
@ -151,7 +172,12 @@ if TYPE_CHECKING:
from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
MODEL_FOR_AUDIO_XVECTOR_MAPPING,
@ -203,7 +229,12 @@ if TYPE_CHECKING:
AutoModelWithLMHead,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
@ -237,7 +268,12 @@ if TYPE_CHECKING:
TFAutoModelWithLMHead,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_auto import (
FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,

65
src/transformers/models/bart/__init__.py
View File

@ -17,7 +17,14 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -25,10 +32,20 @@ _import_structure = {
"tokenization_bart": ["BartTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_bart_fast"] = ["BartTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_bart"] = [
"BART_PRETRAINED_MODEL_ARCHIVE_LIST",
"BartForCausalLM",
@ -40,10 +57,20 @@ if is_torch_available():
"PretrainedBartModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_bart"] = ["TFBartForConditionalGeneration", "TFBartModel", "TFBartPretrainedModel"]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_bart"] = [
"FlaxBartDecoderPreTrainedModel",
"FlaxBartForCausalLM",
@ -58,10 +85,20 @@ if TYPE_CHECKING:
from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
from .tokenization_bart import BartTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bart_fast import BartTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bart import (
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BartForCausalLM,
@ -73,10 +110,20 @@ if TYPE_CHECKING:
PretrainedBartModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bart import TFBartForConditionalGeneration, TFBartModel, TFBartPretrainedModel
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bart import (
FlaxBartDecoderPreTrainedModel,
FlaxBartForCausalLM,

30
src/transformers/models/barthez/__init__.py
View File

@ -18,24 +18,44 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_sentencepiece_available, is_tokenizers_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
_import_structure = {}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_barthez"] = ["BarthezTokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_barthez_fast"] = ["BarthezTokenizerFast"]
if TYPE_CHECKING:
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_barthez import BarthezTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_barthez_fast import BarthezTokenizerFast
else:

16
src/transformers/models/bartpho/__init__.py
View File

@ -18,16 +18,26 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_sentencepiece_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_import_structure = {}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_bartpho"] = ["BartphoTokenizer"]
if TYPE_CHECKING:
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:

50
src/transformers/models/beit/__init__.py
View File

@ -18,17 +18,33 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_torch_available, is_vision_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
_import_structure = {
"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_beit"] = ["BeitFeatureExtractor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_beit"] = [
"BEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BeitForImageClassification",
@ -39,7 +55,12 @@ if is_torch_available():
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_beit"] = [
"FlaxBeitForImageClassification",
"FlaxBeitForMaskedImageModeling",
@ -50,10 +71,20 @@ if is_flax_available():
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
@ -63,7 +94,12 @@ if TYPE_CHECKING:
BeitPreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,

65
src/transformers/models/bert/__init__.py
View File

@ -18,7 +18,14 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -26,10 +33,20 @@ _import_structure = {
"tokenization_bert": ["BasicTokenizer", "BertTokenizer", "WordpieceTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_bert_fast"] = ["BertTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_bert"] = [
"BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BertForMaskedLM",
@ -46,7 +63,12 @@ if is_torch_available():
"load_tf_weights_in_bert",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_bert"] = [
"TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBertEmbeddings",
@ -63,7 +85,12 @@ if is_tf_available():
"TFBertPreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_bert"] = [
"FlaxBertForCausalLM",
"FlaxBertForMaskedLM",
@ -81,10 +108,20 @@ if TYPE_CHECKING:
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_fast import BertTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
BertForMaskedLM,
@ -101,7 +138,12 @@ if TYPE_CHECKING:
load_tf_weights_in_bert,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bert import (
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBertEmbeddings,
@ -118,7 +160,12 @@ if TYPE_CHECKING:
TFBertPreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,

30
src/transformers/models/bert_generation/__init__.py
View File

@ -18,17 +18,27 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_sentencepiece_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
_import_structure = {
"configuration_bert_generation": ["BertGenerationConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_bert_generation"] = ["BertGenerationTokenizer"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_bert_generation"] = [
"BertGenerationDecoder",
"BertGenerationEncoder",
@ -40,10 +50,20 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_bert_generation import BertGenerationConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_generation import BertGenerationTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert_generation import (
BertGenerationDecoder,
BertGenerationEncoder,

57
src/transformers/models/big_bird/__init__.py
View File

@ -18,6 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
@ -31,13 +32,28 @@ _import_structure = {
"configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_big_bird"] = ["BigBirdTokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_big_bird_fast"] = ["BigBirdTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_big_bird"] = [
"BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
"BigBirdForCausalLM",
@ -53,7 +69,12 @@ if is_torch_available():
"load_tf_weights_in_big_bird",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_big_bird"] = [
"FlaxBigBirdForCausalLM",
"FlaxBigBirdForMaskedLM",
@ -69,13 +90,28 @@ if is_flax_available():
if TYPE_CHECKING:
from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_big_bird import BigBirdTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_big_bird_fast import BigBirdTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_big_bird import (
BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdForCausalLM,
@ -91,7 +127,12 @@ if TYPE_CHECKING:
load_tf_weights_in_big_bird,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,

16
src/transformers/models/bigbird_pegasus/__init__.py
View File

@ -17,7 +17,7 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
@ -28,7 +28,12 @@ _import_structure = {
],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_bigbird_pegasus"] = [
"BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
"BigBirdPegasusForCausalLM",
@ -47,7 +52,12 @@ if TYPE_CHECKING:
BigBirdPegasusOnnxConfig,
)
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,

65
src/transformers/models/blenderbot/__init__.py
View File

@ -18,7 +18,14 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -30,10 +37,20 @@ _import_structure = {
"tokenization_blenderbot": ["BlenderbotTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_blenderbot_fast"] = ["BlenderbotTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_blenderbot"] = [
"BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotForCausalLM",
@ -43,7 +60,12 @@ if is_torch_available():
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_blenderbot"] = [
"TFBlenderbotForConditionalGeneration",
"TFBlenderbotModel",
@ -51,7 +73,12 @@ if is_tf_available():
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_blenderbot"] = [
"FlaxBlenderbotForConditionalGeneration",
"FlaxBlenderbotModel",
@ -67,10 +94,20 @@ if TYPE_CHECKING:
)
from .tokenization_blenderbot import BlenderbotTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
@ -79,14 +116,24 @@ if TYPE_CHECKING:
BlenderbotPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,

65
src/transformers/models/blenderbot_small/__init__.py
View File

@ -17,7 +17,14 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -29,10 +36,20 @@ _import_structure = {
"tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_blenderbot_small_fast"] = ["BlenderbotSmallTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_blenderbot_small"] = [
"BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotSmallForCausalLM",
@ -41,14 +58,24 @@ if is_torch_available():
"BlenderbotSmallPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_blenderbot_small"] = [
"TFBlenderbotSmallForConditionalGeneration",
"TFBlenderbotSmallModel",
"TFBlenderbotSmallPreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_blenderbot_small"] = [
"FlaxBlenderbotSmallForConditionalGeneration",
"FlaxBlenderbotSmallModel",
@ -63,10 +90,20 @@ if TYPE_CHECKING:
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
@ -75,14 +112,24 @@ if TYPE_CHECKING:
BlenderbotSmallPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,

57
src/transformers/models/camembert/__init__.py
View File

@ -19,6 +19,7 @@
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
@ -31,13 +32,28 @@ _import_structure = {
"configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_camembert"] = ["CamembertTokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_camembert_fast"] = ["CamembertTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_camembert"] = [
"CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"CamembertForCausalLM",
@ -49,7 +65,12 @@ if is_torch_available():
"CamembertModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_camembert"] = [
"TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFCamembertForCausalLM",
@ -65,13 +86,28 @@ if is_tf_available():
if TYPE_CHECKING:
from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_camembert import CamembertTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_camembert_fast import CamembertTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_camembert import (
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
CamembertForCausalLM,
@ -83,7 +119,12 @@ if TYPE_CHECKING:
CamembertModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_camembert import (
TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCamembertForCausalLM,

16
src/transformers/models/canine/__init__.py
View File

@ -17,7 +17,7 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
@ -25,7 +25,12 @@ _import_structure = {
"tokenization_canine": ["CanineTokenizer"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_canine"] = [
"CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
"CanineForMultipleChoice",
@ -43,7 +48,12 @@ if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,

71
src/transformers/models/clip/__init__.py
View File

@ -18,6 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
@ -32,14 +33,29 @@ _import_structure = {
"tokenization_clip": ["CLIPTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_clip_fast"] = ["CLIPTokenizerFast"]
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_clip"] = ["CLIPFeatureExtractor"]
_import_structure["processing_clip"] = ["CLIPProcessor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_clip"] = [
"CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"CLIPModel",
@ -48,7 +64,12 @@ if is_torch_available():
"CLIPVisionModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_clip"] = [
"TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFCLIPModel",
@ -57,7 +78,12 @@ if is_tf_available():
"TFCLIPVisionModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_clip"] = [
"FlaxCLIPModel",
"FlaxCLIPPreTrainedModel",
@ -72,14 +98,29 @@ if TYPE_CHECKING:
from .configuration_clip import CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIPConfig, CLIPTextConfig, CLIPVisionConfig
from .tokenization_clip import CLIPTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .processing_clip import CLIPProcessor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
@ -88,7 +129,12 @@ if TYPE_CHECKING:
CLIPVisionModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
@ -97,7 +143,12 @@ if TYPE_CHECKING:
TFCLIPVisionModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,

50
src/transformers/models/convbert/__init__.py
View File

@ -17,7 +17,13 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -25,10 +31,20 @@ _import_structure = {
"tokenization_convbert": ["ConvBertTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_convbert_fast"] = ["ConvBertTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_convbert"] = [
"CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConvBertForMaskedLM",
@ -43,7 +59,12 @@ if is_torch_available():
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_convbert"] = [
"TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFConvBertForMaskedLM",
@ -61,10 +82,20 @@ if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
@ -78,7 +109,12 @@ if TYPE_CHECKING:
load_tf_weights_in_convbert,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,

50
src/transformers/models/convnext/__init__.py
View File

@ -18,17 +18,33 @@
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import _LazyModule, is_tf_available, is_torch_available, is_vision_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_import_structure = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_convnext"] = ["ConvNextFeatureExtractor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_convnext"] = [
"CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConvNextForImageClassification",
@ -36,7 +52,12 @@ if is_torch_available():
"ConvNextPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_convnext"] = [
"TFConvNextForImageClassification",
"TFConvNextModel",
@ -46,10 +67,20 @@ if is_tf_available():
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextForImageClassification,
@ -57,7 +88,12 @@ if TYPE_CHECKING:
ConvNextPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel

30
src/transformers/models/cpm/__init__.py
View File

@ -18,23 +18,43 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_sentencepiece_available, is_tokenizers_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
_import_structure = {}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_cpm"] = ["CpmTokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_cpm_fast"] = ["CpmTokenizerFast"]
if TYPE_CHECKING:
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_cpm import CpmTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_cpm_fast import CpmTokenizerFast
else:

30
src/transformers/models/ctrl/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_import_structure = {
@ -26,7 +26,12 @@ _import_structure = {
"tokenization_ctrl": ["CTRLTokenizer"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_ctrl"] = [
"CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"CTRLForSequenceClassification",
@ -35,7 +40,12 @@ if is_torch_available():
"CTRLPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_ctrl"] = [
"TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFCTRLForSequenceClassification",
@ -49,7 +59,12 @@ if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
@ -58,7 +73,12 @@ if TYPE_CHECKING:
CTRLPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,

18
src/transformers/models/data2vec/__init__.py
View File

@ -18,9 +18,7 @@
from typing import TYPE_CHECKING
from transformers.utils.import_utils import is_tf_available
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_import_structure = {
@ -40,7 +38,12 @@ _import_structure = {
],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_data2vec_audio"] = [
"DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
"Data2VecAudioForAudioFrameClassification",
@ -90,7 +93,12 @@ if TYPE_CHECKING:
Data2VecVisionOnnxConfig,
)
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_data2vec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
Data2VecAudioForAudioFrameClassification,

50
src/transformers/models/deberta/__init__.py
View File

@ -18,7 +18,13 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -26,10 +32,20 @@ _import_structure = {
"tokenization_deberta": ["DebertaTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_deberta_fast"] = ["DebertaTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_deberta"] = [
"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"DebertaForMaskedLM",
@ -40,7 +56,12 @@ if is_torch_available():
"DebertaPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_deberta"] = [
"TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDebertaForMaskedLM",
@ -56,10 +77,20 @@ if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig
from .tokenization_deberta import DebertaTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
@ -70,7 +101,12 @@ if TYPE_CHECKING:
DebertaPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,

50
src/transformers/models/deberta_v2/__init__.py
View File

@ -18,7 +18,13 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -26,10 +32,20 @@ _import_structure = {
"tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_deberta_v2_fast"] = ["DebertaV2TokenizerFast"]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_deberta_v2"] = [
"TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDebertaV2ForMaskedLM",
@ -40,7 +56,12 @@ if is_tf_available():
"TFDebertaV2PreTrainedModel",
]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_deberta_v2"] = [
"DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
"DebertaV2ForMaskedLM",
@ -56,10 +77,20 @@ if TYPE_CHECKING:
from .configuration_deberta_v2 import DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaV2Config
from .tokenization_deberta_v2 import DebertaV2Tokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_v2_fast import DebertaV2TokenizerFast
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta_v2 import (
TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaV2ForMaskedLM,
@ -70,7 +101,12 @@ if TYPE_CHECKING:
TFDebertaV2PreTrainedModel,
)
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta_v2 import (
DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaV2ForMaskedLM,

16
src/transformers/models/decision_transformer/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
@ -28,7 +28,12 @@ _import_structure = {
],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_decision_transformer"] = [
"DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"DecisionTransformerGPT2Model",
@ -44,7 +49,12 @@ if TYPE_CHECKING:
DecisionTransformerConfig,
)
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
DecisionTransformerGPT2Model,

30
src/transformers/models/deit/__init__.py
View File

@ -17,17 +17,27 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_deit"] = ["DeiTFeatureExtractor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_deit"] = [
"DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DeiTForImageClassification",
@ -41,10 +51,20 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,

30
src/transformers/models/detr/__init__.py
View File

@ -18,17 +18,27 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_timm_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_timm_available, is_vision_available
_import_structure = {
"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_detr"] = ["DetrFeatureExtractor"]
if is_timm_available():
try:
if not is_timm_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_detr"] = [
"DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
"DetrForObjectDetection",
@ -41,10 +51,20 @@ if is_timm_available():
if TYPE_CHECKING:
from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_detr import DetrFeatureExtractor
if is_timm_available():
try:
if not is_timm_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_detr import (
DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
DetrForObjectDetection,

65
src/transformers/models/distilbert/__init__.py
View File

@ -18,7 +18,14 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -30,10 +37,20 @@ _import_structure = {
"tokenization_distilbert": ["DistilBertTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_distilbert_fast"] = ["DistilBertTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_distilbert"] = [
"DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DistilBertForMaskedLM",
@ -45,7 +62,12 @@ if is_torch_available():
"DistilBertPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_distilbert"] = [
"TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDistilBertForMaskedLM",
@ -58,7 +80,12 @@ if is_tf_available():
"TFDistilBertPreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_distilbert"] = [
"FlaxDistilBertForMaskedLM",
"FlaxDistilBertForMultipleChoice",
@ -78,10 +105,20 @@ if TYPE_CHECKING:
)
from .tokenization_distilbert import DistilBertTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
@ -93,7 +130,12 @@ if TYPE_CHECKING:
DistilBertPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
@ -106,7 +148,12 @@ if TYPE_CHECKING:
TFDistilBertPreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,

50
src/transformers/models/dpr/__init__.py
View File

@ -18,7 +18,13 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -32,14 +38,24 @@ _import_structure = {
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_dpr_fast"] = [
"DPRContextEncoderTokenizerFast",
"DPRQuestionEncoderTokenizerFast",
"DPRReaderTokenizerFast",
]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_dpr"] = [
"DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
"DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
@ -53,7 +69,12 @@ if is_torch_available():
"DPRReader",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_dpr"] = [
"TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
@ -76,14 +97,24 @@ if TYPE_CHECKING:
DPRReaderTokenizer,
)
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_dpr_fast import (
DPRContextEncoderTokenizerFast,
DPRQuestionEncoderTokenizerFast,
DPRReaderTokenizerFast,
)
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpr import (
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
@ -97,7 +128,12 @@ if TYPE_CHECKING:
DPRReader,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_dpr import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,

29
src/transformers/models/dpt/__init__.py
View File

@ -18,16 +18,27 @@
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_import_structure = {
"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_dpt"] = ["DPTFeatureExtractor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_dpt"] = [
"DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DPTForDepthEstimation",
@ -40,10 +51,20 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,

65
src/transformers/models/electra/__init__.py
View File

@ -18,7 +18,14 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -26,10 +33,20 @@ _import_structure = {
"tokenization_electra": ["ElectraTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_electra_fast"] = ["ElectraTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_electra"] = [
"ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"ElectraForCausalLM",
@ -44,7 +61,12 @@ if is_torch_available():
"load_tf_weights_in_electra",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_electra"] = [
"TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFElectraForMaskedLM",
@ -57,7 +79,12 @@ if is_tf_available():
"TFElectraPreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_electra"] = [
"FlaxElectraForCausalLM",
"FlaxElectraForMaskedLM",
@ -75,10 +102,20 @@ if TYPE_CHECKING:
from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
from .tokenization_electra import ElectraTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_electra_fast import ElectraTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_electra import (
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
ElectraForCausalLM,
@ -93,7 +130,12 @@ if TYPE_CHECKING:
load_tf_weights_in_electra,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_electra import (
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFElectraForMaskedLM,
@ -106,7 +148,12 @@ if TYPE_CHECKING:
TFElectraPreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,

50
src/transformers/models/encoder_decoder/__init__.py
View File

@ -18,32 +18,68 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {
"configuration_encoder_decoder": ["EncoderDecoderConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_encoder_decoder"] = ["EncoderDecoderModel"]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_encoder_decoder"] = ["TFEncoderDecoderModel"]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_encoder_decoder"] = ["FlaxEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:

30
src/transformers/models/flaubert/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_import_structure = {
@ -26,7 +26,12 @@ _import_structure = {
"tokenization_flaubert": ["FlaubertTokenizer"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flaubert"] = [
"FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaubertForMultipleChoice",
@ -38,7 +43,12 @@ if is_torch_available():
"FlaubertWithLMHeadModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_flaubert"] = [
"TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFFlaubertForMultipleChoice",
@ -55,7 +65,12 @@ if TYPE_CHECKING:
from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
from .tokenization_flaubert import FlaubertTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flaubert import (
FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaubertForMultipleChoice,
@ -67,7 +82,12 @@ if TYPE_CHECKING:
FlaubertWithLMHeadModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_flaubert import (
TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFlaubertForMultipleChoice,

52
src/transformers/models/fnet/__init__.py
View File

@ -17,22 +17,41 @@
# limitations under the License.
from typing import TYPE_CHECKING
from transformers import is_sentencepiece_available
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_fnet"] = ["FNetTokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_fnet_fast"] = ["FNetTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_fnet"] = [
"FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"FNetForMaskedLM",
@ -51,13 +70,28 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,

16
src/transformers/models/fsmt/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
@ -26,7 +26,12 @@ _import_structure = {
"tokenization_fsmt": ["FSMTTokenizer"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_fsmt"] = ["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]
@ -34,7 +39,12 @@ if TYPE_CHECKING:
from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
from .tokenization_fsmt import FSMTTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel
else:

50
src/transformers/models/funnel/__init__.py
View File

@ -18,7 +18,13 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -27,10 +33,20 @@ _import_structure = {
"tokenization_funnel": ["FunnelTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_funnel_fast"] = ["FunnelTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_funnel"] = [
"FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
"FunnelBaseModel",
@ -45,7 +61,12 @@ if is_torch_available():
"load_tf_weights_in_funnel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_funnel"] = [
"TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFFunnelBaseModel",
@ -64,10 +85,20 @@ if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
@ -82,7 +113,12 @@ if TYPE_CHECKING:
load_tf_weights_in_funnel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,

30
src/transformers/models/glpn/__init__.py
View File

@ -18,17 +18,27 @@
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import _LazyModule, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_glpn"] = ["GLPNFeatureExtractor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_glpn"] = [
"GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
"GLPNForDepthEstimation",
@ -41,10 +51,20 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,

65
src/transformers/models/gpt2/__init__.py
View File

@ -18,7 +18,14 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -26,10 +33,20 @@ _import_structure = {
"tokenization_gpt2": ["GPT2Tokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_gpt2_fast"] = ["GPT2TokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_gpt2"] = [
"GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPT2DoubleHeadsModel",
@ -41,7 +58,12 @@ if is_torch_available():
"load_tf_weights_in_gpt2",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_gpt2"] = [
"TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFGPT2DoubleHeadsModel",
@ -52,17 +74,32 @@ if is_tf_available():
"TFGPT2PreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_gpt2"] = ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]
if TYPE_CHECKING:
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
from .tokenization_gpt2 import GPT2Tokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt2_fast import GPT2TokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt2 import (
GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
GPT2DoubleHeadsModel,
@ -74,7 +111,12 @@ if TYPE_CHECKING:
load_tf_weights_in_gpt2,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_gpt2 import (
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGPT2DoubleHeadsModel,
@ -85,7 +127,12 @@ if TYPE_CHECKING:
TFGPT2PreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model, FlaxGPT2PreTrainedModel
else:

30
src/transformers/models/gpt_neo/__init__.py
View File

@ -17,14 +17,19 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_import_structure = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_gpt_neo"] = [
"GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoForCausalLM",
@ -34,7 +39,12 @@ if is_torch_available():
"load_tf_weights_in_gpt_neo",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_gpt_neo"] = [
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
@ -45,7 +55,12 @@ if is_flax_available():
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
@ -55,7 +70,12 @@ if TYPE_CHECKING:
load_tf_weights_in_gpt_neo,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel

50
src/transformers/models/gptj/__init__.py
View File

@ -17,14 +17,25 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {
"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_gptj"] = [
"GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTJForCausalLM",
@ -34,7 +45,12 @@ if is_torch_available():
"GPTJPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_gptj"] = [
"TFGPTJForCausalLM",
"TFGPTJForQuestionAnswering",
@ -43,7 +59,12 @@ if is_tf_available():
"TFGPTJPreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_gptj"] = [
"FlaxGPTJForCausalLM",
"FlaxGPTJModel",
@ -54,7 +75,12 @@ if is_flax_available():
if TYPE_CHECKING:
from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gptj import (
GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTJForCausalLM,
@ -64,7 +90,12 @@ if TYPE_CHECKING:
GPTJPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_gptj import (
TFGPTJForCausalLM,
TFGPTJForQuestionAnswering,
@ -73,7 +104,12 @@ if TYPE_CHECKING:
TFGPTJPreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
else:

16
src/transformers/models/herbert/__init__.py
View File

@ -18,21 +18,31 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tokenizers_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_import_structure = {
"tokenization_herbert": ["HerbertTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_herbert_fast"] = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:

30
src/transformers/models/hubert/__init__.py
View File

@ -17,7 +17,7 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_import_structure = {
@ -25,7 +25,12 @@ _import_structure = {
"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_hubert"] = [
"HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"HubertForCTC",
@ -35,7 +40,12 @@ if is_torch_available():
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_hubert"] = [
"TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFHubertForCTC",
@ -47,7 +57,12 @@ if TYPE_CHECKING:
from ..wav2vec2.feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_hubert import (
HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
HubertForCTC,
@ -56,7 +71,12 @@ if TYPE_CHECKING:
HubertPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_hubert import (
TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFHubertForCTC,

16
src/transformers/models/ibert/__init__.py
View File

@ -18,14 +18,19 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_ibert"] = [
"IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"IBertForMaskedLM",
@ -40,7 +45,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,

30
src/transformers/models/imagegpt/__init__.py
View File

@ -18,17 +18,27 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_imagegpt"] = ["ImageGPTFeatureExtractor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_imagegpt"] = [
"IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ImageGPTForCausalImageModeling",
@ -42,10 +52,20 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_imagegpt import (
IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
ImageGPTForCausalImageModeling,

50
src/transformers/models/layoutlm/__init__.py
View File

@ -18,7 +18,13 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig
from .tokenization_layoutlm import LayoutLMTokenizer
@ -28,10 +34,20 @@ _import_structure = {
"tokenization_layoutlm": ["LayoutLMTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_layoutlm_fast"] = ["LayoutLMTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_layoutlm"] = [
"LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"LayoutLMForMaskedLM",
@ -41,7 +57,12 @@ if is_torch_available():
"LayoutLMPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_layoutlm"] = [
"TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLayoutLMForMaskedLM",
@ -57,10 +78,20 @@ if TYPE_CHECKING:
from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
from .tokenization_layoutlm import LayoutLMTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlm import (
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMForMaskedLM,
@ -69,7 +100,12 @@ if TYPE_CHECKING:
LayoutLMModel,
LayoutLMPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,

50
src/transformers/models/layoutlmv2/__init__.py
View File

@ -18,7 +18,13 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_import_structure = {
@ -26,14 +32,29 @@ _import_structure = {
"tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_layoutlmv2_fast"] = ["LayoutLMv2TokenizerFast"]
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_layoutlmv2"] = ["LayoutLMv2FeatureExtractor"]
_import_structure["processing_layoutlmv2"] = ["LayoutLMv2Processor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_layoutlmv2"] = [
"LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
"LayoutLMv2ForQuestionAnswering",
@ -48,14 +69,29 @@ if TYPE_CHECKING:
from .configuration_layoutlmv2 import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMv2Config
from .tokenization_layoutlmv2 import LayoutLMv2Tokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmv2_fast import LayoutLMv2TokenizerFast
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmv2 import LayoutLMv2FeatureExtractor
from .processing_layoutlmv2 import LayoutLMv2Processor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmv2 import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMv2ForQuestionAnswering,

43
src/transformers/models/layoutxlm/__init__.py
View File

@ -19,6 +19,7 @@
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
@ -29,23 +30,53 @@ from ...utils import (
_import_structure = {}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_layoutxlm"] = ["LayoutXLMTokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_layoutxlm_fast"] = ["LayoutXLMTokenizerFast"]
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["processing_layoutxlm"] = ["LayoutXLMProcessor"]
if TYPE_CHECKING:
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm import LayoutXLMTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .processing_layoutlmv2 import LayoutXLMProcessor
else:

50
src/transformers/models/led/__init__.py
View File

@ -17,7 +17,13 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -25,10 +31,20 @@ _import_structure = {
"tokenization_led": ["LEDTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_led_fast"] = ["LEDTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_led"] = [
"LED_PRETRAINED_MODEL_ARCHIVE_LIST",
"LEDForConditionalGeneration",
@ -39,7 +55,12 @@ if is_torch_available():
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_led"] = ["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]
@ -47,10 +68,20 @@ if TYPE_CHECKING:
from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
from .tokenization_led import LEDTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_led_fast import LEDTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_led import (
LED_PRETRAINED_MODEL_ARCHIVE_LIST,
LEDForConditionalGeneration,
@ -60,7 +91,12 @@ if TYPE_CHECKING:
LEDPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel
else:

50
src/transformers/models/longformer/__init__.py
View File

@ -18,7 +18,13 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -30,10 +36,20 @@ _import_structure = {
"tokenization_longformer": ["LongformerTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_longformer_fast"] = ["LongformerTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_longformer"] = [
"LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"LongformerForMaskedLM",
@ -46,7 +62,12 @@ if is_torch_available():
"LongformerSelfAttention",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_longformer"] = [
"TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLongformerForMaskedLM",
@ -68,10 +89,20 @@ if TYPE_CHECKING:
)
from .tokenization_longformer import LongformerTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
@ -84,7 +115,12 @@ if TYPE_CHECKING:
LongformerSelfAttention,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,

16
src/transformers/models/luke/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
@ -26,7 +26,12 @@ _import_structure = {
"tokenization_luke": ["LukeTokenizer"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_luke"] = [
"LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
"LukeForEntityClassification",
@ -42,7 +47,12 @@ if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,

50
src/transformers/models/lxmert/__init__.py
View File

@ -18,7 +18,13 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -26,10 +32,20 @@ _import_structure = {
"tokenization_lxmert": ["LxmertTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_lxmert_fast"] = ["LxmertTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_lxmert"] = [
"LxmertEncoder",
"LxmertForPreTraining",
@ -40,7 +56,12 @@ if is_torch_available():
"LxmertXLayer",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_lxmert"] = [
"TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLxmertForPreTraining",
@ -55,10 +76,20 @@ if TYPE_CHECKING:
from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
from .tokenization_lxmert import LxmertTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_lxmert_fast import LxmertTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lxmert import (
LxmertEncoder,
LxmertForPreTraining,
@ -69,7 +100,12 @@ if TYPE_CHECKING:
LxmertXLayer,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_lxmert import (
TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLxmertForPreTraining,

16
src/transformers/models/m2m_100/__init__.py
View File

@ -17,7 +17,7 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
@ -26,7 +26,12 @@ _import_structure = {
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_m2m_100"] = [
"M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
"M2M100ForConditionalGeneration",
@ -39,7 +44,12 @@ if TYPE_CHECKING:
from .configuration_m2m_100 import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, M2M100Config, M2M100OnnxConfig
from .tokenization_m2m_100 import M2M100Tokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_m2m_100 import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
M2M100ForConditionalGeneration,

57
src/transformers/models/marian/__init__.py
View File

@ -18,6 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
@ -31,10 +32,20 @@ _import_structure = {
"configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_marian"] = ["MarianTokenizer"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_marian"] = [
"MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
"MarianForCausalLM",
@ -43,19 +54,39 @@ if is_torch_available():
"MarianPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_marian"] = ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_marian"] = ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]
if TYPE_CHECKING:
from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_marian import MarianTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_marian import (
MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
MarianForCausalLM,
@ -64,10 +95,20 @@ if TYPE_CHECKING:
MarianPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
else:

30
src/transformers/models/maskformer/__init__.py
View File

@ -17,18 +17,28 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_maskformer"] = ["MaskFormerFeatureExtractor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_maskformer"] = [
"MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"MaskFormerForInstanceSegmentation",
@ -39,9 +49,19 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskformer import (
MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskFormerForInstanceSegmentation,

71
src/transformers/models/mbart/__init__.py
View File

@ -18,6 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
@ -31,13 +32,28 @@ _import_structure = {
"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mbart"] = ["MBartTokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mbart_fast"] = ["MBartTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_mbart"] = [
"MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
"MBartForCausalLM",
@ -48,14 +64,24 @@ if is_torch_available():
"MBartPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_mbart"] = [
"TFMBartForConditionalGeneration",
"TFMBartModel",
"TFMBartPreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_mbart"] = [
"FlaxMBartForConditionalGeneration",
"FlaxMBartForQuestionAnswering",
@ -68,13 +94,28 @@ if is_flax_available():
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
@ -85,10 +126,20 @@ if TYPE_CHECKING:
MBartPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,

30
src/transformers/models/mbart50/__init__.py
View File

@ -17,23 +17,43 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_sentencepiece_available, is_tokenizers_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
_import_structure = {}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mbart50"] = ["MBart50Tokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mbart50_fast"] = ["MBart50TokenizerFast"]
if TYPE_CHECKING:
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart50 import MBart50Tokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart50_fast import MBart50TokenizerFast
else:

16
src/transformers/models/megatron_bert/__init__.py
View File

@ -17,14 +17,19 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_megatron_bert"] = [
"MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegatronBertForCausalLM",
@ -42,7 +47,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,

16
src/transformers/models/mluke/__init__.py
View File

@ -18,17 +18,27 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_sentencepiece_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_import_structure = {}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mluke"] = ["MLukeTokenizer"]
if TYPE_CHECKING:
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer

16
src/transformers/models/mmbt/__init__.py
View File

@ -18,21 +18,31 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_mmbt": ["MMBTConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_mmbt"] = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:

50
src/transformers/models/mobilebert/__init__.py
View File

@ -18,7 +18,13 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -30,10 +36,20 @@ _import_structure = {
"tokenization_mobilebert": ["MobileBertTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mobilebert_fast"] = ["MobileBertTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_mobilebert"] = [
"MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MobileBertForMaskedLM",
@ -49,7 +65,12 @@ if is_torch_available():
"load_tf_weights_in_mobilebert",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_mobilebert"] = [
"TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFMobileBertForMaskedLM",
@ -73,10 +94,20 @@ if TYPE_CHECKING:
)
from .tokenization_mobilebert import MobileBertTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
@ -92,7 +123,12 @@ if TYPE_CHECKING:
load_tf_weights_in_mobilebert,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,

51
src/transformers/models/mpnet/__init__.py
View File

@ -18,7 +18,14 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -26,10 +33,20 @@ _import_structure = {
"tokenization_mpnet": ["MPNetTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mpnet_fast"] = ["MPNetTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_mpnet"] = [
"MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"MPNetForMaskedLM",
@ -42,7 +59,12 @@ if is_torch_available():
"MPNetPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_mpnet"] = [
"TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFMPNetEmbeddings",
@ -61,10 +83,20 @@ if TYPE_CHECKING:
from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
from .tokenization_mpnet import MPNetTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mpnet_fast import MPNetTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mpnet import (
MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
MPNetForMaskedLM,
@ -77,7 +109,12 @@ if TYPE_CHECKING:
MPNetPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mpnet import (
TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMPNetEmbeddings,

43
src/transformers/models/mt5/__init__.py
View File

@ -19,6 +19,7 @@
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
@ -46,26 +47,56 @@ _import_structure = {
"configuration_mt5": ["MT5Config"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_mt5"] = ["MT5EncoderModel", "MT5ForConditionalGeneration", "MT5Model"]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_mt5"] = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_mt5"] = ["FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mt5 import MT5Config
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mt5 import MT5EncoderModel, MT5ForConditionalGeneration, MT5Model
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mt5 import TFMT5EncoderModel, TFMT5ForConditionalGeneration, TFMT5Model
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mt5 import FlaxMT5ForConditionalGeneration, FlaxMT5Model
else:

16
src/transformers/models/nystromformer/__init__.py
View File

@ -18,14 +18,19 @@
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
"configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_nystromformer"] = [
"NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"NystromformerForMaskedLM",
@ -42,7 +47,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nystromformer import (
NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
NystromformerForMaskedLM,

50
src/transformers/models/openai/__init__.py
View File

@ -18,7 +18,13 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -26,10 +32,20 @@ _import_structure = {
"tokenization_openai": ["OpenAIGPTTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_openai_fast"] = ["OpenAIGPTTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_openai"] = [
"OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"OpenAIGPTDoubleHeadsModel",
@ -40,7 +56,12 @@ if is_torch_available():
"load_tf_weights_in_openai_gpt",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_openai"] = [
"TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFOpenAIGPTDoubleHeadsModel",
@ -56,10 +77,20 @@ if TYPE_CHECKING:
from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
from .tokenization_openai import OpenAIGPTTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_openai_fast import OpenAIGPTTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_openai import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTDoubleHeadsModel,
@ -70,7 +101,12 @@ if TYPE_CHECKING:
load_tf_weights_in_openai_gpt,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_openai import (
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFOpenAIGPTDoubleHeadsModel,

71
src/transformers/models/pegasus/__init__.py
View File

@ -18,6 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
@ -31,13 +32,28 @@ _import_structure = {
"configuration_pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_pegasus"] = ["PegasusTokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_pegasus_fast"] = ["PegasusTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_pegasus"] = [
"PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
"PegasusForCausalLM",
@ -46,14 +62,24 @@ if is_torch_available():
"PegasusPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_pegasus"] = [
"TFPegasusForConditionalGeneration",
"TFPegasusModel",
"TFPegasusPreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_pegasus"] = [
"FlaxPegasusForConditionalGeneration",
"FlaxPegasusModel",
@ -64,13 +90,28 @@ if is_flax_available():
if TYPE_CHECKING:
from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_pegasus import PegasusTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_pegasus_fast import PegasusTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus import (
PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusForCausalLM,
@ -79,10 +120,20 @@ if TYPE_CHECKING:
PegasusPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_pegasus import (
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,

36
src/transformers/models/perceiver/__init__.py
View File

@ -17,7 +17,13 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_import_structure = {
@ -25,10 +31,20 @@ _import_structure = {
"tokenization_perceiver": ["PerceiverTokenizer"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_perceiver"] = ["PerceiverFeatureExtractor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_perceiver"] = [
"PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST",
"PerceiverForImageClassificationConvProcessing",
@ -48,10 +64,20 @@ if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig
from .tokenization_perceiver import PerceiverTokenizer
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,

36
src/transformers/models/plbart/__init__.py
View File

@ -17,17 +17,33 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_sentencepiece_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_plbart"] = ["PLBartTokenizer"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_plbart"] = [
"PLBART_PRETRAINED_MODEL_ARCHIVE_LIST",
"PLBartForCausalLM",
@ -41,10 +57,20 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,

30
src/transformers/models/poolformer/__init__.py
View File

@ -18,17 +18,27 @@
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import _LazyModule, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_poolformer": ["POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PoolFormerConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_poolformer"] = ["PoolFormerFeatureExtractor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_poolformer"] = [
"POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"PoolFormerForImageClassification",
@ -40,10 +50,20 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_poolformer import POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, PoolFormerConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_poolformer import PoolFormerFeatureExtractor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_poolformer import (
POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
PoolFormerForImageClassification,

16
src/transformers/models/prophetnet/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
@ -26,7 +26,12 @@ _import_structure = {
"tokenization_prophetnet": ["ProphetNetTokenizer"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_prophetnet"] = [
"PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"ProphetNetDecoder",
@ -42,7 +47,12 @@ if TYPE_CHECKING:
from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
from .tokenization_prophetnet import ProphetNetTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_prophetnet import (
PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ProphetNetDecoder,

16
src/transformers/models/qdqbert/__init__.py
View File

@ -17,14 +17,19 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_qdqbert"] = [
"QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"QDQBertForMaskedLM",
@ -44,7 +49,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_qdqbert import (
QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
QDQBertForMaskedLM,

30
src/transformers/models/rag/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_import_structure = {
@ -27,7 +27,12 @@ _import_structure = {
"tokenization_rag": ["RagTokenizer"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_rag"] = [
"RagModel",
"RagPreTrainedModel",
@ -35,7 +40,12 @@ if is_torch_available():
"RagTokenForGeneration",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_rag"] = [
"TFRagModel",
"TFRagPreTrainedModel",
@ -49,10 +59,20 @@ if TYPE_CHECKING:
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,

30
src/transformers/models/realm/__init__.py
View File

@ -17,7 +17,7 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
@ -25,10 +25,20 @@ _import_structure = {
"tokenization_realm": ["RealmTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_realm_fast"] = ["RealmTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_realm"] = [
"REALM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RealmEmbedder",
@ -46,10 +56,20 @@ if TYPE_CHECKING:
from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
from .tokenization_realm import RealmTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_realm import RealmTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_realm import (
REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
RealmEmbedder,

50
src/transformers/models/reformer/__init__.py
View File

@ -18,20 +18,41 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_sentencepiece_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_reformer"] = ["ReformerTokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_reformer_fast"] = ["ReformerTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_reformer"] = [
"REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"ReformerAttention",
@ -48,13 +69,28 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,

15
src/transformers/models/regnet/__init__.py
View File

@ -19,13 +19,19 @@ from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_import_structure = {
"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_regnet"] = [
"REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"RegNetForImageClassification",
@ -37,7 +43,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_regnet import (
REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
RegNetForImageClassification,

57
src/transformers/models/rembert/__init__.py
View File

@ -19,6 +19,7 @@
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
@ -31,13 +32,28 @@ _import_structure = {
"configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_rembert"] = ["RemBertTokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_rembert_fast"] = ["RemBertTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_rembert"] = [
"REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RemBertForCausalLM",
@ -53,7 +69,12 @@ if is_torch_available():
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_rembert"] = [
"TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRemBertForCausalLM",
@ -71,13 +92,28 @@ if is_tf_available():
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
@ -92,7 +128,12 @@ if TYPE_CHECKING:
load_tf_weights_in_rembert,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,

16
src/transformers/models/resnet/__init__.py
View File

@ -18,14 +18,19 @@
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_resnet"] = [
"RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"ResNetForImageClassification",
@ -37,7 +42,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetForImageClassification,

30
src/transformers/models/retribert/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
@ -26,10 +26,20 @@ _import_structure = {
"tokenization_retribert": ["RetriBertTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_retribert_fast"] = ["RetriBertTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_retribert"] = [
"RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RetriBertModel",
@ -41,10 +51,20 @@ if TYPE_CHECKING:
from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
from .tokenization_retribert import RetriBertTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_retribert_fast import RetriBertTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_retribert import (
RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RetriBertModel,

65
src/transformers/models/roberta/__init__.py
View File

@ -18,7 +18,14 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -26,10 +33,20 @@ _import_structure = {
"tokenization_roberta": ["RobertaTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_roberta_fast"] = ["RobertaTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_roberta"] = [
"ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaForCausalLM",
@ -42,7 +59,12 @@ if is_torch_available():
"RobertaPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_roberta"] = [
"TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaForCausalLM",
@ -56,7 +78,12 @@ if is_tf_available():
"TFRobertaPreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_roberta"] = [
"FlaxRobertaForCausalLM",
"FlaxRobertaForMaskedLM",
@ -73,10 +100,20 @@ if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
@ -89,7 +126,12 @@ if TYPE_CHECKING:
RobertaPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
@ -103,7 +145,12 @@ if TYPE_CHECKING:
TFRobertaPreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,

65
src/transformers/models/roformer/__init__.py
View File

@ -17,7 +17,14 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
@ -25,10 +32,20 @@ _import_structure = {
"tokenization_roformer": ["RoFormerTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_roformer_fast"] = ["RoFormerTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_roformer"] = [
"ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoFormerForCausalLM",
@ -44,7 +61,12 @@ if is_torch_available():
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_roformer"] = [
"TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRoFormerForCausalLM",
@ -59,7 +81,12 @@ if is_tf_available():
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_roformer"] = [
"FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxRoFormerForMaskedLM",
@ -76,10 +103,20 @@ if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
@ -94,7 +131,12 @@ if TYPE_CHECKING:
load_tf_weights_in_roformer,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
@ -108,7 +150,12 @@ if TYPE_CHECKING:
TFRoFormerPreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,

30
src/transformers/models/segformer/__init__.py
View File

@ -17,17 +17,27 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_segformer"] = ["SegformerFeatureExtractor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_segformer"] = [
"SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"SegformerDecodeHead",
@ -42,10 +52,20 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_segformer import SegformerFeatureExtractor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_segformer import (
SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SegformerDecodeHead,

16
src/transformers/models/sew/__init__.py
View File

@ -17,14 +17,19 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_sew"] = [
"SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
"SEWForCTC",
@ -36,7 +41,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,

16
src/transformers/models/sew_d/__init__.py
View File

@ -17,14 +17,19 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_sew_d"] = [
"SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST",
"SEWDForCTC",
@ -36,7 +41,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew_d import (
SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWDForCTC,

30
src/transformers/models/speech_encoder_decoder/__init__.py
View File

@ -18,26 +18,46 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_import_structure = {
"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_speech_encoder_decoder"] = ["SpeechEncoderDecoderModel"]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_speech_encoder_decoder"] = ["FlaxSpeechEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:

65
src/transformers/models/speech_to_text/__init__.py
View File

@ -17,7 +17,14 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_sentencepiece_available, is_speech_available, is_tf_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_import_structure = {
@ -27,16 +34,31 @@ _import_structure = {
],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_speech_to_text"] = ["Speech2TextTokenizer"]
if is_speech_available():
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_speech_to_text"] = ["Speech2TextFeatureExtractor"]
if is_sentencepiece_available():
_import_structure["processing_speech_to_text"] = ["Speech2TextProcessor"]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_speech_to_text"] = [
"TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFSpeech2TextForConditionalGeneration",
@ -44,7 +66,12 @@ if is_tf_available():
"TFSpeech2TextPreTrainedModel",
]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_speech_to_text"] = [
"SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Speech2TextForConditionalGeneration",
@ -56,16 +83,31 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2TextConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import Speech2TextTokenizer
if is_speech_available():
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import Speech2TextFeatureExtractor
if is_sentencepiece_available():
from .processing_speech_to_text import Speech2TextProcessor
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeech2TextForConditionalGeneration,
@ -73,7 +115,12 @@ if TYPE_CHECKING:
TFSpeech2TextPreTrainedModel,
)
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
Speech2TextForConditionalGeneration,

22
src/transformers/models/speech_to_text_2/__init__.py
View File

@ -17,7 +17,13 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_sentencepiece_available, is_speech_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_import_structure = {
@ -30,7 +36,12 @@ _import_structure = {
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_speech_to_text_2"] = [
"SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Speech2Text2ForCausalLM",
@ -43,7 +54,12 @@ if TYPE_CHECKING:
from .processing_speech_to_text_2 import Speech2Text2Processor
from .tokenization_speech_to_text_2 import Speech2Text2Tokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text_2 import (
SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
Speech2Text2ForCausalLM,

30
src/transformers/models/splinter/__init__.py
View File

@ -17,7 +17,7 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
@ -25,10 +25,20 @@ _import_structure = {
"tokenization_splinter": ["SplinterTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_splinter_fast"] = ["SplinterTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_splinter"] = [
"SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
"SplinterForQuestionAnswering",
@ -42,10 +52,20 @@ if TYPE_CHECKING:
from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
from .tokenization_splinter import SplinterTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_splinter_fast import SplinterTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_splinter import (
SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
SplinterForQuestionAnswering,

30
src/transformers/models/squeezebert/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
@ -26,10 +26,20 @@ _import_structure = {
"tokenization_squeezebert": ["SqueezeBertTokenizer"],
}
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_squeezebert_fast"] = ["SqueezeBertTokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_squeezebert"] = [
"SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"SqueezeBertForMaskedLM",
@ -47,10 +57,20 @@ if TYPE_CHECKING:
from .configuration_squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig
from .tokenization_squeezebert import SqueezeBertTokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,

16
src/transformers/models/swin/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
@ -26,7 +26,12 @@ _import_structure = {
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_swin"] = [
"SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
"SwinForImageClassification",
@ -39,7 +44,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinForImageClassification,

71
src/transformers/models/t5/__init__.py
View File

@ -19,6 +19,7 @@
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
@ -32,13 +33,28 @@ _import_structure = {
"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"],
}
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_t5"] = ["T5Tokenizer"]
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_t5_fast"] = ["T5TokenizerFast"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_t5"] = [
"T5_PRETRAINED_MODEL_ARCHIVE_LIST",
"T5EncoderModel",
@ -48,7 +64,12 @@ if is_torch_available():
"load_tf_weights_in_t5",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_t5"] = [
"TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFT5EncoderModel",
@ -57,7 +78,12 @@ if is_tf_available():
"TFT5PreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_t5"] = [
"FlaxT5ForConditionalGeneration",
"FlaxT5Model",
@ -68,13 +94,28 @@ if is_flax_available():
if TYPE_CHECKING:
from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config, T5OnnxConfig
if is_sentencepiece_available():
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_t5 import T5Tokenizer
if is_tokenizers_available():
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_t5_fast import T5TokenizerFast
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_t5 import (
T5_PRETRAINED_MODEL_ARCHIVE_LIST,
T5EncoderModel,
@ -84,7 +125,12 @@ if TYPE_CHECKING:
load_tf_weights_in_t5,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_t5 import (
TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
TFT5EncoderModel,
@ -93,7 +139,12 @@ if TYPE_CHECKING:
TFT5PreTrainedModel,
)
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_t5 import FlaxT5ForConditionalGeneration, FlaxT5Model, FlaxT5PreTrainedModel

30
src/transformers/models/tapas/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_import_structure = {
@ -26,7 +26,12 @@ _import_structure = {
"tokenization_tapas": ["TapasTokenizer"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tapas"] = [
"TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TapasForMaskedLM",
@ -36,7 +41,12 @@ if is_torch_available():
"TapasPreTrainedModel",
"load_tf_weights_in_tapas",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_tapas"] = [
"TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFTapasForMaskedLM",
@ -51,7 +61,12 @@ if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
@ -62,7 +77,12 @@ if TYPE_CHECKING:
load_tf_weights_in_tapas,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,

30
src/transformers/models/transfo_xl/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tf_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_import_structure = {
@ -26,7 +26,12 @@ _import_structure = {
"tokenization_transfo_xl": ["TransfoXLCorpus", "TransfoXLTokenizer"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_transfo_xl"] = [
"TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
"AdaptiveEmbedding",
@ -37,7 +42,12 @@ if is_torch_available():
"load_tf_weights_in_transfo_xl",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_transfo_xl"] = [
"TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFAdaptiveEmbedding",
@ -53,7 +63,12 @@ if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
@ -64,7 +79,12 @@ if TYPE_CHECKING:
load_tf_weights_in_transfo_xl,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,

22
src/transformers/models/trocr/__init__.py
View File

@ -17,7 +17,13 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_sentencepiece_available, is_speech_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_import_structure = {
@ -29,7 +35,12 @@ _import_structure = {
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_trocr"] = [
"TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrOCRForCausalLM",
@ -41,7 +52,12 @@ if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:

22
src/transformers/models/unispeech/__init__.py
View File

@ -17,14 +17,25 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {
"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_unispeech"] = [
"UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
"UniSpeechForCTC",
@ -37,7 +48,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,

22
src/transformers/models/unispeech_sat/__init__.py
View File

@ -17,14 +17,25 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {
"configuration_unispeech_sat": ["UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_unispeech_sat"] = [
"UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST",
"UniSpeechSatForAudioFrameClassification",
@ -39,7 +50,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech_sat import (
UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechSatForAudioFrameClassification,

16
src/transformers/models/van/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import _LazyModule, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
@ -26,7 +26,12 @@ _import_structure = {
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_van"] = [
"VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
"VanForImageClassification",
@ -37,7 +42,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,

30
src/transformers/models/vilt/__init__.py
View File

@ -18,18 +18,28 @@
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import _LazyModule, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_vilt"] = ["ViltFeatureExtractor"]
_import_structure["processing_vilt"] = ["ViltProcessor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_vilt"] = [
"VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViltForImageAndTextRetrieval",
@ -45,11 +55,21 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vilt import ViltFeatureExtractor
from .processing_vilt import ViltProcessor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vilt import (
VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViltForImageAndTextRetrieval,

50
src/transformers/models/vision_encoder_decoder/__init__.py
View File

@ -18,32 +18,68 @@
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_vision_encoder_decoder"] = ["VisionEncoderDecoderModel"]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_vision_encoder_decoder"] = ["TFVisionEncoderDecoderModel"]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_vision_encoder_decoder"] = ["FlaxVisionEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:

30
src/transformers/models/vision_text_dual_encoder/__init__.py
View File

@ -18,7 +18,7 @@
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import _LazyModule, is_flax_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_import_structure = {
@ -27,11 +27,21 @@ _import_structure = {
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_vision_text_dual_encoder"] = ["VisionTextDualEncoderModel"]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_vision_text_dual_encoder"] = ["FlaxVisionTextDualEncoderModel"]
@ -39,10 +49,20 @@ if TYPE_CHECKING:
from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
from .processing_visiotn_text_dual_encoder import VisionTextDualEncoderProcessor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

16
src/transformers/models/visual_bert/__init__.py
View File

@ -17,14 +17,19 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"],
}
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_visual_bert"] = [
"VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"VisualBertForMultipleChoice",
@ -41,7 +46,12 @@ if is_torch_available():
if TYPE_CHECKING:
from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_visual_bert import (
VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
VisualBertForMultipleChoice,

65
src/transformers/models/vit/__init__.py
View File

@ -17,17 +17,34 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available, is_vision_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_import_structure = {
"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"],
}
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_vit"] = ["ViTFeatureExtractor"]
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_vit"] = [
"VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTForImageClassification",
@ -36,14 +53,24 @@ if is_torch_available():
"ViTPreTrainedModel",
]
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_vit"] = [
"TFViTForImageClassification",
"TFViTModel",
"TFViTPreTrainedModel",
]
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_vit"] = [
"FlaxViTForImageClassification",
"FlaxViTModel",
@ -53,10 +80,20 @@ if is_flax_available():
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
if is_vision_available():
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
if is_torch_available():
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
@ -65,10 +102,20 @@ if TYPE_CHECKING:
ViTPreTrainedModel,
)
if is_tf_available():
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
if is_flax_available():
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel

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