Clean imports to fix test_fetcher (#17531)

* Clean imports to fix test_fetcher

* Add dependencies printer

* Update utils/tests_fetcher.py

Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

* Fix Perceiver import

Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

src/transformers/modeling_tf_utils.py

@@ -1177,7 +1177,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
         dataset_args: Optional[Union[str, List[str]]] = None,
     ):
         # Avoids a circular import by doing this when necessary.
-        from .modelcard import TrainingSummary
+        from .modelcard import TrainingSummary  # tests_ignore
 
         training_summary = TrainingSummary.from_keras(
             self,

src/transformers/models/perceiver/__init__.py

@@ -27,7 +27,7 @@ from ...utils import (
 
 
 _import_structure = {
-    "configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig"],
+    "configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverOnnxConfig"],
     "tokenization_perceiver": ["PerceiverTokenizer"],
 }
 
@@ -61,7 +61,7 @@ else:
 
 
 if TYPE_CHECKING:
-    from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig
+    from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
     from .tokenization_perceiver import PerceiverTokenizer
 
     try:

src/transformers/onnx/features.py

@@ -1,39 +1,9 @@
 from functools import partial, reduce
 from typing import Callable, Dict, Optional, Tuple, Type, Union
 
+import transformers
+
 from .. import PretrainedConfig, PreTrainedModel, TFPreTrainedModel, is_tf_available, is_torch_available
-from ..models.albert import AlbertOnnxConfig
-from ..models.bart import BartOnnxConfig
-from ..models.beit import BeitOnnxConfig
-from ..models.bert import BertOnnxConfig
-from ..models.big_bird import BigBirdOnnxConfig
-from ..models.bigbird_pegasus import BigBirdPegasusOnnxConfig
-from ..models.blenderbot import BlenderbotOnnxConfig
-from ..models.blenderbot_small import BlenderbotSmallOnnxConfig
-from ..models.camembert import CamembertOnnxConfig
-from ..models.convbert import ConvBertOnnxConfig
-from ..models.data2vec import Data2VecTextOnnxConfig
-from ..models.deit import DeiTOnnxConfig
-from ..models.distilbert import DistilBertOnnxConfig
-from ..models.electra import ElectraOnnxConfig
-from ..models.flaubert import FlaubertOnnxConfig
-from ..models.gpt2 import GPT2OnnxConfig
-from ..models.gpt_neo import GPTNeoOnnxConfig
-from ..models.gptj import GPTJOnnxConfig
-from ..models.ibert import IBertOnnxConfig
-from ..models.layoutlm import LayoutLMOnnxConfig
-from ..models.m2m_100 import M2M100OnnxConfig
-from ..models.marian import MarianOnnxConfig
-from ..models.mbart import MBartOnnxConfig
-from ..models.mobilebert import MobileBertOnnxConfig
-from ..models.perceiver.configuration_perceiver import PerceiverOnnxConfig
-from ..models.roberta import RobertaOnnxConfig
-from ..models.roformer import RoFormerOnnxConfig
-from ..models.squeezebert import SqueezeBertOnnxConfig
-from ..models.t5 import T5OnnxConfig
-from ..models.vit import ViTOnnxConfig
-from ..models.xlm import XLMOnnxConfig
-from ..models.xlm_roberta import XLMRobertaOnnxConfig
 from ..utils import logging
 from .config import OnnxConfig
 
@@ -72,14 +42,14 @@ if not is_torch_available() and not is_tf_available():
 
 
 def supported_features_mapping(
-    *supported_features: str, onnx_config_cls: Type[OnnxConfig] = None
+    *supported_features: str, onnx_config_cls: str = None
 ) -> Dict[str, Callable[[PretrainedConfig], OnnxConfig]]:
     """
     Generate the mapping between supported the features and their corresponding OnnxConfig for a given model.
 
     Args:
         *supported_features: The names of the supported features.
-        onnx_config_cls: The OnnxConfig class corresponding to the model.
+        onnx_config_cls: The OnnxConfig full name corresponding to the model.
 
     Returns:
         The dictionary mapping a feature to an OnnxConfig constructor.
@@ -87,13 +57,16 @@ def supported_features_mapping(
     if onnx_config_cls is None:
         raise ValueError("A OnnxConfig class must be provided")
 
+    config_cls = transformers
+    for attr_name in onnx_config_cls.split("."):
+        config_cls = getattr(config_cls, attr_name)
     mapping = {}
     for feature in supported_features:
         if "-with-past" in feature:
             task = feature.replace("-with-past", "")
-            mapping[feature] = partial(onnx_config_cls.with_past, task=task)
+            mapping[feature] = partial(config_cls.with_past, task=task)
         else:
-            mapping[feature] = partial(onnx_config_cls.from_model_config, task=feature)
+            mapping[feature] = partial(config_cls.from_model_config, task=feature)
 
     return mapping
 
@@ -135,7 +108,7 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=AlbertOnnxConfig,
+            onnx_config_cls="models.albert.AlbertOnnxConfig",
         ),
         "bart": supported_features_mapping(
             "default",
@@ -146,10 +119,12 @@ class FeaturesManager:
             "seq2seq-lm-with-past",
             "sequence-classification",
             "question-answering",
-            onnx_config_cls=BartOnnxConfig,
+            onnx_config_cls="models.bart.BartOnnxConfig",
         ),
         # BEiT cannot be used with the masked image modeling autoclass, so this feature is excluded here
-        "beit": supported_features_mapping("default", "image-classification", onnx_config_cls=BeitOnnxConfig),
+        "beit": supported_features_mapping(
+            "default", "image-classification", onnx_config_cls="models.beit.BeitOnnxConfig"
+        ),
         "bert": supported_features_mapping(
             "default",
             "masked-lm",
@@ -158,7 +133,7 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=BertOnnxConfig,
+            onnx_config_cls="models.bert.BertOnnxConfig",
         ),
         "big-bird": supported_features_mapping(
             "default",
@@ -168,7 +143,7 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=BigBirdOnnxConfig,
+            onnx_config_cls="models.big_bird.BigBirdOnnxConfig",
         ),
         "bigbird-pegasus": supported_features_mapping(
             "default",
@@ -179,7 +154,7 @@ class FeaturesManager:
             "seq2seq-lm-with-past",
             "sequence-classification",
             "question-answering",
-            onnx_config_cls=BigBirdPegasusOnnxConfig,
+            onnx_config_cls="models.bigbird_pegasus.BigBirdPegasusOnnxConfig",
         ),
         "blenderbot": supported_features_mapping(
             "default",
@@ -188,7 +163,7 @@ class FeaturesManager:
             "causal-lm-with-past",
             "seq2seq-lm",
             "seq2seq-lm-with-past",
-            onnx_config_cls=BlenderbotOnnxConfig,
+            onnx_config_cls="models.blenderbot.BlenderbotOnnxConfig",
         ),
         "blenderbot-small": supported_features_mapping(
             "default",
@@ -197,7 +172,7 @@ class FeaturesManager:
             "causal-lm-with-past",
             "seq2seq-lm",
             "seq2seq-lm-with-past",
-            onnx_config_cls=BlenderbotSmallOnnxConfig,
+            onnx_config_cls="models.blenderbot_small.BlenderbotSmallOnnxConfig",
         ),
         "camembert": supported_features_mapping(
             "default",
@@ -207,7 +182,7 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=CamembertOnnxConfig,
+            onnx_config_cls="models.camembert.CamembertOnnxConfig",
         ),
         "convbert": supported_features_mapping(
             "default",
@@ -216,7 +191,7 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=ConvBertOnnxConfig,
+            onnx_config_cls="models.convbert.ConvBertOnnxConfig",
         ),
         "data2vec-text": supported_features_mapping(
             "default",
@@ -225,10 +200,10 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=Data2VecTextOnnxConfig,
+            onnx_config_cls="models.data2vec.Data2VecTextOnnxConfig",
         ),
         "deit": supported_features_mapping(
-            "default", "image-classification", "masked-im", onnx_config_cls=DeiTOnnxConfig
+            "default", "image-classification", "masked-im", onnx_config_cls="models.deit.DeiTOnnxConfig"
         ),
         "distilbert": supported_features_mapping(
             "default",
@@ -237,7 +212,7 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=DistilBertOnnxConfig,
+            onnx_config_cls="models.distilbert.DistilBertOnnxConfig",
         ),
         "electra": supported_features_mapping(
             "default",
@@ -247,7 +222,7 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=ElectraOnnxConfig,
+            onnx_config_cls="models.electra.ElectraOnnxConfig",
         ),
         "flaubert": supported_features_mapping(
             "default",
@@ -257,7 +232,7 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=FlaubertOnnxConfig,
+            onnx_config_cls="models.flaubert.FlaubertOnnxConfig",
         ),
         "gpt2": supported_features_mapping(
             "default",
@@ -266,7 +241,7 @@ class FeaturesManager:
             "causal-lm-with-past",
             "sequence-classification",
             "token-classification",
-            onnx_config_cls=GPT2OnnxConfig,
+            onnx_config_cls="models.gpt2.GPT2OnnxConfig",
         ),
         "gptj": supported_features_mapping(
             "default",
@@ -275,7 +250,7 @@ class FeaturesManager:
             "causal-lm-with-past",
             "question-answering",
             "sequence-classification",
-            onnx_config_cls=GPTJOnnxConfig,
+            onnx_config_cls="models.gptj.GPTJOnnxConfig",
         ),
         "gpt-neo": supported_features_mapping(
             "default",
@@ -283,7 +258,7 @@ class FeaturesManager:
             "causal-lm",
             "causal-lm-with-past",
             "sequence-classification",
-            onnx_config_cls=GPTNeoOnnxConfig,
+            onnx_config_cls="models.gpt_neo.GPTNeoOnnxConfig",
         ),
         "ibert": supported_features_mapping(
             "default",
@@ -292,14 +267,14 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=IBertOnnxConfig,
+            onnx_config_cls="models.ibert.IBertOnnxConfig",
         ),
         "layoutlm": supported_features_mapping(
             "default",
             "masked-lm",
             "sequence-classification",
             "token-classification",
-            onnx_config_cls=LayoutLMOnnxConfig,
+            onnx_config_cls="models.layoutlm.LayoutLMOnnxConfig",
         ),
         "marian": supported_features_mapping(
             "default",
@@ -308,7 +283,7 @@ class FeaturesManager:
             "seq2seq-lm-with-past",
             "causal-lm",
             "causal-lm-with-past",
-            onnx_config_cls=MarianOnnxConfig,
+            onnx_config_cls="models.marian.MarianOnnxConfig",
         ),
         "mbart": supported_features_mapping(
             "default",
@@ -319,7 +294,7 @@ class FeaturesManager:
             "seq2seq-lm-with-past",
             "sequence-classification",
             "question-answering",
-            onnx_config_cls=MBartOnnxConfig,
+            onnx_config_cls="models.mbart.MBartOnnxConfig",
         ),
         "mobilebert": supported_features_mapping(
             "default",
@@ -328,16 +303,20 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=MobileBertOnnxConfig,
+            onnx_config_cls="models.mobilebert.MobileBertOnnxConfig",
         ),
         "m2m-100": supported_features_mapping(
-            "default", "default-with-past", "seq2seq-lm", "seq2seq-lm-with-past", onnx_config_cls=M2M100OnnxConfig
+            "default",
+            "default-with-past",
+            "seq2seq-lm",
+            "seq2seq-lm-with-past",
+            onnx_config_cls="models.m2m_100.M2M100OnnxConfig",
         ),
         "perceiver": supported_features_mapping(
             "image-classification",
             "masked-lm",
             "sequence-classification",
-            onnx_config_cls=PerceiverOnnxConfig,
+            onnx_config_cls="models.perceiver.PerceiverOnnxConfig",
         ),
         "roberta": supported_features_mapping(
             "default",
@@ -347,7 +326,7 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=RobertaOnnxConfig,
+            onnx_config_cls="models.roberta.RobertaOnnxConfig",
         ),
         "roformer": supported_features_mapping(
             "default",
@@ -358,7 +337,7 @@ class FeaturesManager:
             "multiple-choice",
             "question-answering",
             "token-classification",
-            onnx_config_cls=RoFormerOnnxConfig,
+            onnx_config_cls="models.roformer.RoFormerOnnxConfig",
         ),
         "squeezebert": supported_features_mapping(
             "default",
@@ -367,13 +346,17 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=SqueezeBertOnnxConfig,
+            onnx_config_cls="models.squeezebert.SqueezeBertOnnxConfig",
         ),
         "t5": supported_features_mapping(
-            "default", "default-with-past", "seq2seq-lm", "seq2seq-lm-with-past", onnx_config_cls=T5OnnxConfig
+            "default",
+            "default-with-past",
+            "seq2seq-lm",
+            "seq2seq-lm-with-past",
+            onnx_config_cls="models.t5.T5OnnxConfig",
         ),
         "vit": supported_features_mapping(
-            "default", "image-classification", "masked-im", onnx_config_cls=ViTOnnxConfig
+            "default", "image-classification", "masked-im", onnx_config_cls="models.vit.ViTOnnxConfig"
         ),
         "xlm": supported_features_mapping(
             "default",
@@ -383,7 +366,7 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=XLMOnnxConfig,
+            onnx_config_cls="models.xlm.XLMOnnxConfig",
         ),
         "xlm-roberta": supported_features_mapping(
             "default",
@@ -393,7 +376,7 @@ class FeaturesManager:
             "multiple-choice",
             "token-classification",
             "question-answering",
-            onnx_config_cls=XLMRobertaOnnxConfig,
+            onnx_config_cls="models.xlm_roberta.XLMRobertaOnnxConfig",
         ),
     }
 

utils/tests_fetcher.py

@@ -242,6 +242,67 @@ def get_test_dependencies(test_fname):
     return [f for f in [*parent_imports, *current_dir_imports] if os.path.isfile(f)]
 
 
+def create_reverse_dependency_tree():
+    """
+    Create a list of all edges (a, b) which mean that modifying a impacts b with a going over all module and test files.
+    """
+    modules = [
+        str(f.relative_to(PATH_TO_TRANFORMERS))
+        for f in (Path(PATH_TO_TRANFORMERS) / "src/transformers").glob("**/*.py")
+    ]
+    module_edges = [(d, m) for m in modules for d in get_module_dependencies(m)]
+
+    tests = [str(f.relative_to(PATH_TO_TRANFORMERS)) for f in (Path(PATH_TO_TRANFORMERS) / "tests").glob("**/*.py")]
+    test_edges = [(d, t) for t in tests for d in get_test_dependencies(t)]
+
+    return module_edges + test_edges
+
+
+def get_tree_starting_at(module, edges):
+    """
+    Returns the tree starting at a given module following all edges in the following format: [module, [list of edges
+    starting at module], [list of edges starting at the preceding level], ...]
+    """
+    vertices_seen = [module]
+    new_edges = [edge for edge in edges if edge[0] == module and edge[1] != module]
+    tree = [module]
+    while len(new_edges) > 0:
+        tree.append(new_edges)
+        final_vertices = list(set(edge[1] for edge in new_edges))
+        vertices_seen.extend(final_vertices)
+        new_edges = [edge for edge in edges if edge[0] in final_vertices and edge[1] not in vertices_seen]
+
+    return tree
+
+
+def print_tree_deps_of(module, all_edges=None):
+    """
+    Prints the tree of modules depending on a given module.
+    """
+    if all_edges is None:
+        all_edges = create_reverse_dependency_tree()
+    tree = get_tree_starting_at(module, all_edges)
+
+    # The list of lines is a list of tuples (line_to_be_printed, module)
+    # Keeping the modules lets us know where to insert each new lines in the list.
+    lines = [(tree[0], tree[0])]
+    for index in range(1, len(tree)):
+        edges = tree[index]
+        start_edges = set([edge[0] for edge in edges])
+
+        for start in start_edges:
+            end_edges = set([edge[1] for edge in edges if edge[0] == start])
+            # We will insert all those edges just after the line showing start.
+            pos = 0
+            while lines[pos][1] != start:
+                pos += 1
+            lines = lines[: pos + 1] + [(" " * (2 * index) + end, end) for end in end_edges] + lines[pos + 1 :]
+
+    for line in lines:
+        # We don't print the refs that where just here to help build lines.
+        print(line[0])
+
+
 def create_reverse_dependency_map():
     """
     Create the dependency map from module/test filename to the list of modules/tests that depend on it (even
@@ -585,8 +646,16 @@ if __name__ == "__main__":
         default=["tests"],
         help="Only keep the test files matching one of those filters.",
     )
+    parser.add_argument(
+        "--print_dependencies_of",
+        type=str,
+        help="Will only print the tree of modules depending on the file passed.",
+        default=None,
+    )
     args = parser.parse_args()
-    if args.sanity_check:
+    if args.print_dependencies_of is not None:
+        print_tree_deps_of(args.print_dependencies_of)
+    elif args.sanity_check:
         sanity_check()
     else:
         repo = Repo(PATH_TO_TRANFORMERS)