Use Python 3.9 syntax in examples (#37279)

Signed-off-by: cyy <cyyever@outlook.com>

examples/flax/image-captioning/create_model_from_encoder_decoder_models.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2022 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/flax/image-captioning/run_image_captioning_flax.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2022 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -833,8 +832,7 @@ def main():
             # No need to shuffle here
             loader = data_loader(rng, _ds, batch_size=batch_size, shuffle=False)
 
-            for batch in loader:
+            yield from loader
-                yield batch
 
     # Metric
     metric = evaluate.load("rouge", cache_dir=model_args.cache_dir)

examples/flax/language-modeling/run_bart_dlm_flax.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -30,7 +29,7 @@ from dataclasses import asdict, dataclass, field
 from enum import Enum
 from itertools import chain
 from pathlib import Path
-from typing import Dict, List, Optional
+from typing import Optional
 
 import flax
 import jax
@@ -294,7 +293,7 @@ class FlaxDataCollatorForBartDenoisingLM:
                 " language modeling. "
             )
 
-    def __call__(self, examples: List[Dict[str, List[int]]]) -> BatchEncoding:
+    def __call__(self, examples: list[dict[str, list[int]]]) -> BatchEncoding:
         # convert list to dict and tensorize input
         batch = BatchEncoding(
             {k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()}

examples/flax/language-modeling/run_clm_flax.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/flax/language-modeling/run_mlm_flax.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -33,7 +32,7 @@ from itertools import chain
 
 # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple
+from typing import Optional
 
 import flax
 import jax
@@ -302,7 +301,7 @@ class FlaxDataCollatorForLanguageModeling:
                 "You should pass `mlm=False` to train on causal language modeling instead."
             )
 
-    def __call__(self, examples: List[Dict[str, np.ndarray]], pad_to_multiple_of: int) -> Dict[str, np.ndarray]:
+    def __call__(self, examples: list[dict[str, np.ndarray]], pad_to_multiple_of: int) -> dict[str, np.ndarray]:
         # Handle dict or lists with proper padding and conversion to tensor.
         batch = self.tokenizer.pad(examples, pad_to_multiple_of=pad_to_multiple_of, return_tensors=TensorType.NUMPY)
 
@@ -316,7 +315,7 @@ class FlaxDataCollatorForLanguageModeling:
 
     def mask_tokens(
         self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
-    ) -> Tuple[np.ndarray, np.ndarray]:
+    ) -> tuple[np.ndarray, np.ndarray]:
         """
         Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
         """

examples/flax/language-modeling/run_t5_mlm_flax.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -32,7 +31,7 @@ from dataclasses import asdict, dataclass, field
 from enum import Enum
 from itertools import chain
 from pathlib import Path
-from typing import Dict, List, Optional
+from typing import Optional
 
 import flax
 import jax
@@ -338,7 +337,7 @@ class FlaxDataCollatorForT5MLM:
     pad_token_id: int
     decoder_start_token_id: int
 
-    def __call__(self, examples: List[Dict[str, np.ndarray]]) -> BatchEncoding:
+    def __call__(self, examples: list[dict[str, np.ndarray]]) -> BatchEncoding:
         # convert list to dict and tensorize input
         batch = BatchEncoding(
             {k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()}

examples/flax/language-modeling/t5_tokenizer_model.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 import json
-from typing import Iterator, List, Union
+from collections.abc import Iterator
+from typing import Union
 
 from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
 from tokenizers.implementations.base_tokenizer import BaseTokenizer
@@ -72,7 +73,7 @@ class SentencePieceUnigramTokenizer(BaseTokenizer):
 
     def train(
         self,
-        files: Union[str, List[str]],
+        files: Union[str, list[str]],
         vocab_size: int = 8000,
         show_progress: bool = True,
     ):

examples/flax/question-answering/run_qa.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -28,7 +27,7 @@ import time
 from dataclasses import asdict, dataclass, field
 from enum import Enum
 from pathlib import Path
-from typing import Any, Callable, Dict, Optional, Tuple
+from typing import Any, Callable, Optional
 
 import datasets
 import evaluate
@@ -908,8 +907,8 @@ def main():
 
     # region Define train step functions
     def train_step(
-        state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey
+        state: train_state.TrainState, batch: dict[str, Array], dropout_rng: PRNGKey
-    ) -> Tuple[train_state.TrainState, float]:
+    ) -> tuple[train_state.TrainState, float]:
         """Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
         dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
         start_positions = batch.pop("start_positions")

examples/flax/question-answering/utils_qa.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2020 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -20,7 +19,7 @@ import collections
 import json
 import logging
 import os
-from typing import Optional, Tuple
+from typing import Optional
 
 import numpy as np
 from tqdm.auto import tqdm
@@ -32,7 +31,7 @@ logger = logging.getLogger(__name__)
 def postprocess_qa_predictions(
     examples,
     features,
-    predictions: Tuple[np.ndarray, np.ndarray],
+    predictions: tuple[np.ndarray, np.ndarray],
     version_2_with_negative: bool = False,
     n_best_size: int = 20,
     max_answer_length: int = 30,
@@ -223,7 +222,7 @@ def postprocess_qa_predictions(
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
         if not os.path.isdir(output_dir):
-            raise EnvironmentError(f"{output_dir} is not a directory.")
+            raise OSError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
@@ -253,7 +252,7 @@ def postprocess_qa_predictions(
 def postprocess_qa_predictions_with_beam_search(
     examples,
     features,
-    predictions: Tuple[np.ndarray, np.ndarray],
+    predictions: tuple[np.ndarray, np.ndarray],
     version_2_with_negative: bool = False,
     n_best_size: int = 20,
     max_answer_length: int = 30,
@@ -417,7 +416,7 @@ def postprocess_qa_predictions_with_beam_search(
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
         if not os.path.isdir(output_dir):
-            raise EnvironmentError(f"{output_dir} is not a directory.")
+            raise OSError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"

examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -25,7 +24,7 @@ import time
 from dataclasses import field
 from functools import partial
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 import datasets
 import evaluate
@@ -303,7 +302,7 @@ class FlaxDataCollatorSpeechSeq2SeqWithPadding:
     pad_input_to_multiple_of: Optional[int] = None
     pad_target_to_multiple_of: Optional[int] = None
 
-    def __call__(self, features: List[Dict[str, Union[List[int], np.ndarray]]]) -> Dict[str, np.ndarray]:
+    def __call__(self, features: list[dict[str, Union[list[int], np.ndarray]]]) -> dict[str, np.ndarray]:
         # split inputs and labels since they have to be of different lengths and need
         # different padding methods
         model_input_name = self.processor.model_input_names[0]

examples/flax/summarization/run_summarization_flax.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/flax/test_flax_examples.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 HuggingFace Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -64,7 +63,7 @@ def get_setup_file():
 def get_results(output_dir, split="eval"):
     path = os.path.join(output_dir, f"{split}_results.json")
     if os.path.exists(path):
-        with open(path, "r") as f:
+        with open(path) as f:
             return json.load(f)
     raise ValueError(f"can't find {path}")
 

examples/flax/text-classification/run_flax_glue.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -25,7 +24,7 @@ import time
 import warnings
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Callable, Dict, Optional, Tuple
+from typing import Any, Callable, Optional
 
 import datasets
 import evaluate
@@ -572,8 +571,8 @@ def main():
 
     # define step functions
     def train_step(
-        state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey
+        state: train_state.TrainState, batch: dict[str, Array], dropout_rng: PRNGKey
-    ) -> Tuple[train_state.TrainState, float]:
+    ) -> tuple[train_state.TrainState, float]:
         """Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
         dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
         targets = batch.pop("labels")

examples/flax/token-classification/run_flax_ner.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -27,7 +26,7 @@ from dataclasses import asdict, dataclass, field
 from enum import Enum
 from itertools import chain
 from pathlib import Path
-from typing import Any, Callable, Dict, Optional, Tuple
+from typing import Any, Callable, Optional
 
 import datasets
 import evaluate
@@ -651,8 +650,8 @@ def main():
 
     # define step functions
     def train_step(
-        state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey
+        state: train_state.TrainState, batch: dict[str, Array], dropout_rng: PRNGKey
-    ) -> Tuple[train_state.TrainState, float]:
+    ) -> tuple[train_state.TrainState, float]:
         """Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
         dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
         targets = batch.pop("labels")

examples/flax/vision/run_image_classification.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/legacy/benchmarking/plot_csv_file.py

@@ -15,7 +15,7 @@
 import csv
 from collections import defaultdict
 from dataclasses import dataclass, field
-from typing import List, Optional
+from typing import Optional
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -59,7 +59,7 @@ class PlotArguments:
         default=None,
         metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."},
     )
-    short_model_names: Optional[List[str]] = list_field(
+    short_model_names: Optional[list[str]] = list_field(
         default=None, metadata={"help": "List of model names that are used instead of the ones in the csv file."}
     )
 

examples/legacy/benchmarking/run_benchmark.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2020 The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #

examples/legacy/multiple_choice/run_multiple_choice.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
@@ -18,7 +17,7 @@
 import logging
 import os
 from dataclasses import dataclass, field
-from typing import Dict, Optional
+from typing import Optional
 
 import numpy as np
 from utils_multiple_choice import MultipleChoiceDataset, Split, processors
@@ -187,7 +186,7 @@ def main():
         else None
     )
 
-    def compute_metrics(p: EvalPrediction) -> Dict:
+    def compute_metrics(p: EvalPrediction) -> dict:
         preds = np.argmax(p.predictions, axis=1)
         return {"acc": simple_accuracy(preds, p.label_ids)}
 
@@ -228,7 +227,7 @@ def main():
                 logger.info("***** Eval results *****")
                 for key, value in result.items():
                     logger.info("  %s = %s", key, value)
-                    writer.write("%s = %s\n" % (key, value))
+                    writer.write("{} = {}\n".format(key, value))
 
                 results.update(result)
 

examples/legacy/multiple_choice/utils_multiple_choice.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
@@ -22,7 +21,7 @@ import logging
 import os
 from dataclasses import dataclass
 from enum import Enum
-from typing import List, Optional
+from typing import Optional
 
 import tqdm
 from filelock import FileLock
@@ -49,8 +48,8 @@ class InputExample:
 
     example_id: str
     question: str
-    contexts: List[str]
+    contexts: list[str]
-    endings: List[str]
+    endings: list[str]
     label: Optional[str]
 
 
@@ -62,9 +61,9 @@ class InputFeatures:
     """
 
     example_id: str
-    input_ids: List[List[int]]
+    input_ids: list[list[int]]
-    attention_mask: Optional[List[List[int]]]
+    attention_mask: Optional[list[list[int]]]
-    token_type_ids: Optional[List[List[int]]]
+    token_type_ids: Optional[list[list[int]]]
     label: Optional[int]
 
 
@@ -84,7 +83,7 @@ if is_torch_available():
         soon.
         """
 
-        features: List[InputFeatures]
+        features: list[InputFeatures]
 
         def __init__(
             self,
@@ -149,7 +148,7 @@ if is_tf_available():
         soon.
         """
 
-        features: List[InputFeatures]
+        features: list[InputFeatures]
 
         def __init__(
             self,
@@ -253,7 +252,7 @@ class RaceProcessor(DataProcessor):
 
     def get_train_examples(self, data_dir):
         """See base class."""
-        logger.info("LOOKING AT {} train".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} train")
         high = os.path.join(data_dir, "train/high")
         middle = os.path.join(data_dir, "train/middle")
         high = self._read_txt(high)
@@ -262,7 +261,7 @@ class RaceProcessor(DataProcessor):
 
     def get_dev_examples(self, data_dir):
         """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")
         high = os.path.join(data_dir, "dev/high")
         middle = os.path.join(data_dir, "dev/middle")
         high = self._read_txt(high)
@@ -271,7 +270,7 @@ class RaceProcessor(DataProcessor):
 
     def get_test_examples(self, data_dir):
         """See base class."""
-        logger.info("LOOKING AT {} test".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} test")
         high = os.path.join(data_dir, "test/high")
         middle = os.path.join(data_dir, "test/middle")
         high = self._read_txt(high)
@@ -286,7 +285,7 @@ class RaceProcessor(DataProcessor):
         lines = []
         files = glob.glob(input_dir + "/*txt")
         for file in tqdm.tqdm(files, desc="read files"):
-            with open(file, "r", encoding="utf-8") as fin:
+            with open(file, encoding="utf-8") as fin:
                 data_raw = json.load(fin)
                 data_raw["race_id"] = file
                 lines.append(data_raw)
@@ -296,7 +295,7 @@ class RaceProcessor(DataProcessor):
         """Creates examples for the training and dev sets."""
         examples = []
         for _, data_raw in enumerate(lines):
-            race_id = "%s-%s" % (set_type, data_raw["race_id"])
+            race_id = "{}-{}".format(set_type, data_raw["race_id"])
             article = data_raw["article"]
             for i in range(len(data_raw["answers"])):
                 truth = str(ord(data_raw["answers"][i]) - ord("A"))
@@ -320,17 +319,17 @@ class SynonymProcessor(DataProcessor):
 
     def get_train_examples(self, data_dir):
         """See base class."""
-        logger.info("LOOKING AT {} train".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} train")
         return self._create_examples(self._read_csv(os.path.join(data_dir, "mctrain.csv")), "train")
 
     def get_dev_examples(self, data_dir):
         """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")
         return self._create_examples(self._read_csv(os.path.join(data_dir, "mchp.csv")), "dev")
 
     def get_test_examples(self, data_dir):
         """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")
 
         return self._create_examples(self._read_csv(os.path.join(data_dir, "mctest.csv")), "test")
 
@@ -339,10 +338,10 @@ class SynonymProcessor(DataProcessor):
         return ["0", "1", "2", "3", "4"]
 
     def _read_csv(self, input_file):
-        with open(input_file, "r", encoding="utf-8") as f:
+        with open(input_file, encoding="utf-8") as f:
             return list(csv.reader(f))
 
-    def _create_examples(self, lines: List[List[str]], type: str):
+    def _create_examples(self, lines: list[list[str]], type: str):
         """Creates examples for the training and dev sets."""
 
         examples = [
@@ -366,17 +365,17 @@ class SwagProcessor(DataProcessor):
 
     def get_train_examples(self, data_dir):
         """See base class."""
-        logger.info("LOOKING AT {} train".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} train")
         return self._create_examples(self._read_csv(os.path.join(data_dir, "train.csv")), "train")
 
     def get_dev_examples(self, data_dir):
         """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")
         return self._create_examples(self._read_csv(os.path.join(data_dir, "val.csv")), "dev")
 
     def get_test_examples(self, data_dir):
         """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")
         raise ValueError(
             "For swag testing, the input file does not contain a label column. It can not be tested in current code "
             "setting!"
@@ -388,10 +387,10 @@ class SwagProcessor(DataProcessor):
         return ["0", "1", "2", "3"]
 
     def _read_csv(self, input_file):
-        with open(input_file, "r", encoding="utf-8") as f:
+        with open(input_file, encoding="utf-8") as f:
             return list(csv.reader(f))
 
-    def _create_examples(self, lines: List[List[str]], type: str):
+    def _create_examples(self, lines: list[list[str]], type: str):
         """Creates examples for the training and dev sets."""
         if type == "train" and lines[0][-1] != "label":
             raise ValueError("For training, the input file must contain a label column.")
@@ -417,16 +416,16 @@ class ArcProcessor(DataProcessor):
 
     def get_train_examples(self, data_dir):
         """See base class."""
-        logger.info("LOOKING AT {} train".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} train")
         return self._create_examples(self._read_json(os.path.join(data_dir, "train.jsonl")), "train")
 
     def get_dev_examples(self, data_dir):
         """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")
         return self._create_examples(self._read_json(os.path.join(data_dir, "dev.jsonl")), "dev")
 
     def get_test_examples(self, data_dir):
-        logger.info("LOOKING AT {} test".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} test")
         return self._create_examples(self._read_json(os.path.join(data_dir, "test.jsonl")), "test")
 
     def get_labels(self):
@@ -434,7 +433,7 @@ class ArcProcessor(DataProcessor):
         return ["0", "1", "2", "3"]
 
     def _read_json(self, input_file):
-        with open(input_file, "r", encoding="utf-8") as fin:
+        with open(input_file, encoding="utf-8") as fin:
             lines = fin.readlines()
             return lines
 
@@ -504,11 +503,11 @@ class ArcProcessor(DataProcessor):
 
 
 def convert_examples_to_features(
-    examples: List[InputExample],
+    examples: list[InputExample],
-    label_list: List[str],
+    label_list: list[str],
     max_length: int,
     tokenizer: PreTrainedTokenizer,
-) -> List[InputFeatures]:
+) -> list[InputFeatures]:
     """
     Loads a data file into a list of `InputFeatures`
     """

examples/legacy/pytorch-lightning/lightning_base.py

@@ -2,7 +2,7 @@ import argparse
 import logging
 import os
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any
 
 import pytorch_lightning as pl
 from pytorch_lightning.utilities import rank_zero_info
@@ -201,7 +201,7 @@ class BaseTransformer(pl.LightningModule):
         )
 
     @pl.utilities.rank_zero_only
-    def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
+    def on_save_checkpoint(self, checkpoint: dict[str, Any]) -> None:
         save_path = self.output_dir.joinpath("best_tfmr")
         self.model.config.save_step = self.step_count
         self.model.save_pretrained(save_path)
@@ -282,7 +282,7 @@ class LoggingCallback(pl.Callback):
         # Log results
         for key in sorted(metrics):
             if key not in ["log", "progress_bar"]:
-                rank_zero_info("{} = {}\n".format(key, str(metrics[key])))
+                rank_zero_info(f"{key} = {str(metrics[key])}\n")
 
     def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
         rank_zero_info("***** Test results *****")
@@ -292,8 +292,8 @@ class LoggingCallback(pl.Callback):
         with open(output_test_results_file, "w") as writer:
             for key in sorted(metrics):
                 if key not in ["log", "progress_bar"]:
-                    rank_zero_info("{} = {}\n".format(key, str(metrics[key])))
+                    rank_zero_info(f"{key} = {str(metrics[key])}\n")
-                    writer.write("{} = {}\n".format(key, str(metrics[key])))
+                    writer.write(f"{key} = {str(metrics[key])}\n")
 
 
 def add_generic_args(parser, root_dir) -> None:

examples/legacy/question-answering/run_squad.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
@@ -231,14 +230,14 @@ def train(args, train_dataset, model, tokenizer):
                     if args.local_rank == -1 and args.evaluate_during_training:
                         results = evaluate(args, model, tokenizer)
                         for key, value in results.items():
-                            tb_writer.add_scalar("eval_{}".format(key), value, global_step)
+                            tb_writer.add_scalar(f"eval_{key}", value, global_step)
                     tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
                     tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
                     logging_loss = tr_loss
 
                 # Save model checkpoint
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
-                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
+                    output_dir = os.path.join(args.output_dir, f"checkpoint-{global_step}")
                     # Take care of distributed/parallel training
                     model_to_save = model.module if hasattr(model, "module") else model
                     model_to_save.save_pretrained(output_dir)
@@ -281,7 +280,7 @@ def evaluate(args, model, tokenizer, prefix=""):
         model = torch.nn.DataParallel(model)
 
     # Eval!
-    logger.info("***** Running evaluation {} *****".format(prefix))
+    logger.info(f"***** Running evaluation {prefix} *****")
     logger.info("  Num examples = %d", len(dataset))
     logger.info("  Batch size = %d", args.eval_batch_size)
 
@@ -348,11 +347,11 @@ def evaluate(args, model, tokenizer, prefix=""):
     logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))
 
     # Compute predictions
-    output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
+    output_prediction_file = os.path.join(args.output_dir, f"predictions_{prefix}.json")
-    output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
+    output_nbest_file = os.path.join(args.output_dir, f"nbest_predictions_{prefix}.json")
 
     if args.version_2_with_negative:
-        output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
+        output_null_log_odds_file = os.path.join(args.output_dir, f"null_odds_{prefix}.json")
     else:
         output_null_log_odds_file = None
 
@@ -828,10 +827,10 @@ def main():
             # Evaluate
             result = evaluate(args, model, tokenizer, prefix=global_step)
 
-            result = {k + ("_{}".format(global_step) if global_step else ""): v for k, v in result.items()}
+            result = {k + (f"_{global_step}" if global_step else ""): v for k, v in result.items()}
             results.update(result)
 
-    logger.info("Results: {}".format(results))
+    logger.info(f"Results: {results}")
 
     return results
 

examples/legacy/question-answering/run_squad_trainer.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #

examples/legacy/run_camembert.py

@@ -20,10 +20,10 @@ def fill_mask(masked_input, model, tokenizer, topk=5):
     topk_filled_outputs = []
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" ")):
         predicted_token = predicted_token_bpe.replace("\u2581", " ")
-        if " {0}".format(masked_token) in masked_input:
+        if f" {masked_token}" in masked_input:
             topk_filled_outputs.append(
                 (
-                    masked_input.replace(" {0}".format(masked_token), predicted_token),
+                    masked_input.replace(f" {masked_token}", predicted_token),
                     values[index].item(),
                     predicted_token,
                 )

examples/legacy/run_chinese_ref.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
 import argparse
 import json
-from typing import List
 
 from ltp import LTP
 
@@ -42,7 +41,7 @@ def is_chinese(word: str):
     return 1
 
 
-def get_chinese_word(tokens: List[str]):
+def get_chinese_word(tokens: list[str]):
     word_set = set()
 
     for token in tokens:
@@ -53,7 +52,7 @@ def get_chinese_word(tokens: List[str]):
     return word_list
 
 
-def add_sub_symbol(bert_tokens: List[str], chinese_word_set: set()):
+def add_sub_symbol(bert_tokens: list[str], chinese_word_set: set()):
     if not chinese_word_set:
         return bert_tokens
     max_word_len = max([len(w) for w in chinese_word_set])
@@ -77,7 +76,7 @@ def add_sub_symbol(bert_tokens: List[str], chinese_word_set: set()):
     return bert_word
 
 
-def prepare_ref(lines: List[str], ltp_tokenizer: LTP, bert_tokenizer: BertTokenizer):
+def prepare_ref(lines: list[str], ltp_tokenizer: LTP, bert_tokenizer: BertTokenizer):
     ltp_res = []
 
     for i in range(0, len(lines), 100):
@@ -117,7 +116,7 @@ def prepare_ref(lines: List[str], ltp_tokenizer: LTP, bert_tokenizer: BertTokeni
 def main(args):
     # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
     # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
-    with open(args.file_name, "r", encoding="utf-8") as f:
+    with open(args.file_name, encoding="utf-8") as f:
         data = f.readlines()
     data = [line.strip() for line in data if len(line) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
     ltp_tokenizer = LTP(args.ltp)  # faster in GPU device

examples/legacy/run_language_modeling.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
@@ -358,7 +357,7 @@ def main():
                 logger.info("***** Eval results *****")
                 for key in sorted(result.keys()):
                     logger.info("  %s = %s", key, str(result[key]))
-                    writer.write("%s = %s\n" % (key, str(result[key])))
+                    writer.write("{} = {}\n".format(key, str(result[key])))
 
         results.update(result)
 

examples/legacy/run_openai_gpt.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
@@ -163,7 +162,7 @@ def main():
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     n_gpu = torch.cuda.device_count()
-    logger.info("device: {}, n_gpu {}".format(device, n_gpu))
+    logger.info(f"device: {device}, n_gpu {n_gpu}")
 
     if not args.do_train and not args.do_eval:
         raise ValueError("At least one of `do_train` or `do_eval` must be True.")
@@ -261,7 +260,7 @@ def main():
                     loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                 )
                 nb_tr_steps += 1
-                tqdm_bar.desc = "Training loss: {:.2e} lr: {:.2e}".format(exp_average_loss, scheduler.get_lr()[0])
+                tqdm_bar.desc = f"Training loss: {exp_average_loss:.2e} lr: {scheduler.get_lr()[0]:.2e}"
 
     # Save a trained model
     if args.do_train:
@@ -313,7 +312,7 @@ def main():
             logger.info("***** Eval results *****")
             for key in sorted(result.keys()):
                 logger.info("  %s = %s", key, str(result[key]))
-                writer.write("%s = %s\n" % (key, str(result[key])))
+                writer.write("{} = {}\n".format(key, str(result[key])))
 
 
 if __name__ == "__main__":

examples/legacy/run_swag.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
@@ -51,7 +50,7 @@ except ImportError:
 logger = logging.getLogger(__name__)
 
 
-class SwagExample(object):
+class SwagExample:
     """A single training/test example for the SWAG dataset."""
 
     def __init__(self, swag_id, context_sentence, start_ending, ending_0, ending_1, ending_2, ending_3, label=None):
@@ -71,22 +70,22 @@ class SwagExample(object):
 
     def __repr__(self):
         attributes = [
-            "swag_id: {}".format(self.swag_id),
+            f"swag_id: {self.swag_id}",
-            "context_sentence: {}".format(self.context_sentence),
+            f"context_sentence: {self.context_sentence}",
-            "start_ending: {}".format(self.start_ending),
+            f"start_ending: {self.start_ending}",
-            "ending_0: {}".format(self.endings[0]),
+            f"ending_0: {self.endings[0]}",
-            "ending_1: {}".format(self.endings[1]),
+            f"ending_1: {self.endings[1]}",
-            "ending_2: {}".format(self.endings[2]),
+            f"ending_2: {self.endings[2]}",
-            "ending_3: {}".format(self.endings[3]),
+            f"ending_3: {self.endings[3]}",
         ]
 
         if self.label is not None:
-            attributes.append("label: {}".format(self.label))
+            attributes.append(f"label: {self.label}")
 
         return ", ".join(attributes)
 
 
-class InputFeatures(object):
+class InputFeatures:
     def __init__(self, example_id, choices_features, label):
         self.example_id = example_id
         self.choices_features = [
@@ -97,7 +96,7 @@ class InputFeatures(object):
 
 
 def read_swag_examples(input_file, is_training=True):
-    with open(input_file, "r", encoding="utf-8") as f:
+    with open(input_file, encoding="utf-8") as f:
         lines = list(csv.reader(f))
 
     if is_training and lines[0][-1] != "label":
@@ -179,15 +178,15 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length, is_trainin
         label = example.label
         if example_index < 5:
             logger.info("*** Example ***")
-            logger.info("swag_id: {}".format(example.swag_id))
+            logger.info(f"swag_id: {example.swag_id}")
             for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
-                logger.info("choice: {}".format(choice_idx))
+                logger.info(f"choice: {choice_idx}")
                 logger.info("tokens: {}".format(" ".join(tokens)))
                 logger.info("input_ids: {}".format(" ".join(map(str, input_ids))))
                 logger.info("input_mask: {}".format(" ".join(map(str, input_mask))))
                 logger.info("segment_ids: {}".format(" ".join(map(str, segment_ids))))
             if is_training:
-                logger.info("label: {}".format(label))
+                logger.info(f"label: {label}")
 
         features.append(InputFeatures(example_id=example.swag_id, choices_features=choices_features, label=label))
 
@@ -382,14 +381,14 @@ def train(args, train_dataset, model, tokenizer):
                     ):  # Only evaluate when single GPU otherwise metrics may not average well
                         results = evaluate(args, model, tokenizer)
                         for key, value in results.items():
-                            tb_writer.add_scalar("eval_{}".format(key), value, global_step)
+                            tb_writer.add_scalar(f"eval_{key}", value, global_step)
                     tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
                     tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
                     logging_loss = tr_loss
 
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                     # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
+                    output_dir = os.path.join(args.output_dir, f"checkpoint-{global_step}")
                     model_to_save = (
                         model.module if hasattr(model, "module") else model
                     )  # Take care of distributed/parallel training
@@ -423,7 +422,7 @@ def evaluate(args, model, tokenizer, prefix=""):
     eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
     # Eval!
-    logger.info("***** Running evaluation {} *****".format(prefix))
+    logger.info(f"***** Running evaluation {prefix} *****")
     logger.info("  Num examples = %d", len(dataset))
     logger.info("  Batch size = %d", args.eval_batch_size)
 
@@ -466,7 +465,7 @@ def evaluate(args, model, tokenizer, prefix=""):
         logger.info("***** Eval results *****")
         for key in sorted(result.keys()):
             logger.info("%s = %s", key, str(result[key]))
-            writer.write("%s = %s\n" % (key, str(result[key])))
+            writer.write("{} = {}\n".format(key, str(result[key])))
 
     return result
 
@@ -710,10 +709,10 @@ def main():
             # Evaluate
             result = evaluate(args, model, tokenizer, prefix=global_step)
 
-            result = {k + ("_{}".format(global_step) if global_step else ""): v for k, v in result.items()}
+            result = {k + (f"_{global_step}" if global_step else ""): v for k, v in result.items()}
             results.update(result)
 
-    logger.info("Results: {}".format(results))
+    logger.info(f"Results: {results}")
 
     return results
 

examples/legacy/run_transfo_xl.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
@@ -66,7 +65,7 @@ def main():
         ptvsd.wait_for_attach()
 
     device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-    logger.info("device: {}".format(device))
+    logger.info(f"device: {device}")
 
     # Load a pre-processed dataset
     # You can also build the corpus yourself using TransfoXLCorpus methods
@@ -111,7 +110,7 @@ def main():
                 total_loss += seq_len * loss.item()
                 total_len += seq_len
             total_time = time.time() - start_time
-        logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(total_time, 1000 * total_time / (idx + 1)))
+        logger.info(f"Time : {total_time:.2f}s, {1000 * total_time / (idx + 1):.2f}ms/segment")
         return total_loss / total_len
 
     # Run on test data.

examples/legacy/seq2seq/old_test_fsmt_bleu_score.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2020 Huggingface
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import io
 import json
 import unittest
 
@@ -25,7 +23,7 @@ from utils import calculate_bleu
 
 
 filename = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
-with io.open(filename, "r", encoding="utf-8") as f:
+with open(filename, encoding="utf-8") as f:
     bleu_data = json.load(f)
 
 

examples/legacy/seq2seq/run_distributed_eval.py

@@ -19,7 +19,6 @@ import time
 from json import JSONDecodeError
 from logging import getLogger
 from pathlib import Path
-from typing import Dict, List
 
 import torch
 from torch.utils.data import DataLoader
@@ -55,10 +54,10 @@ def eval_data_dir(
     task="summarization",
     local_rank=None,
     num_return_sequences=1,
-    dataset_kwargs: Dict = None,
+    dataset_kwargs: dict = None,
     prefix="",
     **generate_kwargs,
-) -> Dict:
+) -> dict:
     """Run evaluation on part of the data for one gpu and save to {save_dir}/rank_{rank}_output.json"""
     model_name = str(model_name)
     assert local_rank is not None
@@ -211,7 +210,7 @@ def run_generate():
         calc_bleu = "translation" in args.task
         score_fn = calculate_bleu if calc_bleu else calculate_rouge
         metric_name = "bleu" if calc_bleu else "rouge"
-        metrics: Dict = score_fn(preds, labels)
+        metrics: dict = score_fn(preds, labels)
         metrics["n_obs"] = len(preds)
         runtime = time.time() - start_time
         metrics["seconds_per_sample"] = round(runtime / metrics["n_obs"], 4)
@@ -227,7 +226,7 @@ def run_generate():
             shutil.rmtree(json_save_dir)
 
 
-def combine_partial_results(partial_results) -> List:
+def combine_partial_results(partial_results) -> list:
     """Concatenate partial results into one file, then sort it by id."""
     records = []
     for partial_result in partial_results:
@@ -237,7 +236,7 @@ def combine_partial_results(partial_results) -> List:
     return preds
 
 
-def gather_results_from_each_node(num_replicas, save_dir, timeout) -> List[Dict[str, List]]:
+def gather_results_from_each_node(num_replicas, save_dir, timeout) -> list[dict[str, list]]:
     # WAIT FOR lots of .json files
     start_wait = time.time()
     logger.info("waiting for all nodes to finish")

examples/legacy/seq2seq/run_eval.py

@@ -20,7 +20,6 @@ import time
 import warnings
 from logging import getLogger
 from pathlib import Path
-from typing import Dict, List
 
 import torch
 from tqdm import tqdm
@@ -36,7 +35,7 @@ DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
 
 def generate_summaries_or_translations(
-    examples: List[str],
+    examples: list[str],
     out_file: str,
     model_name: str,
     batch_size: int = 8,
@@ -45,7 +44,7 @@ def generate_summaries_or_translations(
     task="summarization",
     prefix=None,
     **generate_kwargs,
-) -> Dict:
+) -> dict:
     """Save model.generate results to <out_file>, and return how long it took."""
     fout = Path(out_file).open("w", encoding="utf-8")
     model_name = str(model_name)

examples/legacy/seq2seq/run_eval_search.py

@@ -34,7 +34,7 @@ task_score_names = {
 
 def parse_search_arg(search):
     groups = search.split()
-    entries = dict((g.split("=") for g in groups))
+    entries = dict(g.split("=") for g in groups)
     entry_names = list(entries.keys())
     sets = [[f"--{k} {v}" for v in vs.split(":")] for k, vs in entries.items()]
     matrix = [list(x) for x in itertools.product(*sets)]
@@ -105,7 +105,7 @@ def run_search():
     col_widths = {col: len(str(col)) for col in col_names}
     results = []
     for r in matrix:
-        hparams = dict((x.replace("--", "").split() for x in r))
+        hparams = dict(x.replace("--", "").split() for x in r)
         args_exp = " ".join(r).split()
         args_exp.extend(["--bs", str(args.bs)])  # in case we need to reduce its size due to CUDA OOM
         sys.argv = args_normal + args_exp

examples/legacy/seq2seq/seq2seq_trainer.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Optional, Union
 
 import torch
 from torch import nn
@@ -172,10 +172,10 @@ class Seq2SeqTrainer(Trainer):
     def prediction_step(
         self,
         model: nn.Module,
-        inputs: Dict[str, Union[torch.Tensor, Any]],
+        inputs: dict[str, Union[torch.Tensor, Any]],
         prediction_loss_only: bool,
-        ignore_keys: Optional[List[str]] = None,
+        ignore_keys: Optional[list[str]] = None,
-    ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
+    ) -> tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
         """
         Perform an evaluation step on :obj:`model` using obj:`inputs`.
 

examples/legacy/seq2seq/test_data/fsmt/build-eval-data.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python
 
-import io
 import json
 import subprocess
 
@@ -29,5 +28,5 @@ def get_all_data(pairs, n_objs):
 
 text = get_all_data(pairs, n_objs)
 filename = "./fsmt_val_data.json"
-with io.open(filename, "w", encoding="utf-8") as f:
+with open(filename, "w", encoding="utf-8") as f:
     bleu_data = json.dump(text, f, indent=2, ensure_ascii=False)

examples/legacy/seq2seq/utils.py

@@ -19,9 +19,10 @@ import math
 import os
 import pickle
 import socket
+from collections.abc import Iterable
 from logging import getLogger
 from pathlib import Path
-from typing import Callable, Dict, Iterable, List, Tuple, Union
+from typing import Callable, Union
 
 import git
 import numpy as np
@@ -67,7 +68,7 @@ def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=-100):
     return loss, nll_loss
 
 
-def lmap(f: Callable, x: Iterable) -> List:
+def lmap(f: Callable, x: Iterable) -> list:
     """list(map(f, x))"""
     return list(map(f, x))
 
@@ -77,11 +78,11 @@ def calculate_bleu(output_lns, refs_lns, **kwargs) -> dict:
     return {"bleu": round(corpus_bleu(output_lns, [refs_lns], **kwargs).score, 4)}
 
 
-def build_compute_metrics_fn(task_name: str, tokenizer: PreTrainedTokenizer) -> Callable[[EvalPrediction], Dict]:
+def build_compute_metrics_fn(task_name: str, tokenizer: PreTrainedTokenizer) -> Callable[[EvalPrediction], dict]:
     def non_pad_len(tokens: np.ndarray) -> int:
         return np.count_nonzero(tokens != tokenizer.pad_token_id)
 
-    def decode_pred(pred: EvalPrediction) -> Tuple[List[str], List[str]]:
+    def decode_pred(pred: EvalPrediction) -> tuple[list[str], list[str]]:
         pred_ids = pred.predictions
         label_ids = pred.label_ids
         pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
@@ -91,16 +92,16 @@ def build_compute_metrics_fn(task_name: str, tokenizer: PreTrainedTokenizer) ->
         label_str = lmap(str.strip, label_str)
         return pred_str, label_str
 
-    def summarization_metrics(pred: EvalPrediction) -> Dict:
+    def summarization_metrics(pred: EvalPrediction) -> dict:
         pred_str, label_str = decode_pred(pred)
-        rouge: Dict = calculate_rouge(pred_str, label_str)
+        rouge: dict = calculate_rouge(pred_str, label_str)
         summ_len = np.round(np.mean(lmap(non_pad_len, pred.predictions)), 1)
         rouge.update({"gen_len": summ_len})
         return rouge
 
-    def translation_metrics(pred: EvalPrediction) -> Dict:
+    def translation_metrics(pred: EvalPrediction) -> dict:
         pred_str, label_str = decode_pred(pred)
-        bleu: Dict = calculate_bleu(pred_str, label_str)
+        bleu: dict = calculate_bleu(pred_str, label_str)
         gen_len = np.round(np.mean(lmap(non_pad_len, pred.predictions)), 1)
         bleu.update({"gen_len": gen_len})
         return bleu
@@ -183,7 +184,7 @@ class AbstractSeq2SeqDataset(Dataset):
             return min(self.src_lens[i], self.max_target_length)
 
         # call fairseq cython function
-        batch_sampler: List[List[int]] = batch_by_size(
+        batch_sampler: list[list[int]] = batch_by_size(
             sorted_indices,
             num_tokens_fn=num_tokens_in_example,
             max_tokens=max_tokens_per_batch,
@@ -207,7 +208,7 @@ class AbstractSeq2SeqDataset(Dataset):
 
 
 class LegacySeq2SeqDataset(AbstractSeq2SeqDataset):
-    def __getitem__(self, index) -> Dict[str, torch.Tensor]:
+    def __getitem__(self, index) -> dict[str, torch.Tensor]:
         """Call tokenizer on src and tgt_lines"""
         index = index + 1  # linecache starts at 1
         source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
@@ -237,7 +238,7 @@ class LegacySeq2SeqDataset(AbstractSeq2SeqDataset):
             **self.dataset_kwargs,
         )
 
-    def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
+    def collate_fn(self, batch) -> dict[str, torch.Tensor]:
         input_ids = torch.stack([x["input_ids"] for x in batch])
         masks = torch.stack([x["attention_mask"] for x in batch])
         target_ids = torch.stack([x["labels"] for x in batch])
@@ -255,7 +256,7 @@ class LegacySeq2SeqDataset(AbstractSeq2SeqDataset):
 class Seq2SeqDataset(AbstractSeq2SeqDataset):
     """A dataset that calls prepare_seq2seq_batch."""
 
-    def __getitem__(self, index) -> Dict[str, str]:
+    def __getitem__(self, index) -> dict[str, str]:
         index = index + 1  # linecache starts at 1
         source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
         tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n")
@@ -263,9 +264,9 @@ class Seq2SeqDataset(AbstractSeq2SeqDataset):
         assert tgt_line, f"empty tgt line for index {index}"
         return {"tgt_texts": tgt_line, "src_texts": source_line, "id": index - 1}
 
-    def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
+    def collate_fn(self, batch) -> dict[str, torch.Tensor]:
         """Call prepare_seq2seq_batch."""
-        batch_encoding: Dict[str, torch.Tensor] = self.tokenizer.prepare_seq2seq_batch(
+        batch_encoding: dict[str, torch.Tensor] = self.tokenizer.prepare_seq2seq_batch(
             [x["src_texts"] for x in batch],
             tgt_texts=[x["tgt_texts"] for x in batch],
             max_length=self.max_source_length,
@@ -293,7 +294,7 @@ class Seq2SeqDataCollator:
         if data_args.tgt_lang is not None:
             self.dataset_kwargs["tgt_lang"] = data_args.tgt_lang
 
-    def __call__(self, batch) -> Dict[str, torch.Tensor]:
+    def __call__(self, batch) -> dict[str, torch.Tensor]:
         if hasattr(self.tokenizer, "prepare_seq2seq_batch"):
             batch = self._encode(batch)
             input_ids, attention_mask, labels = (
@@ -329,7 +330,7 @@ class Seq2SeqDataCollator:
         shifted_input_ids[..., 0] = self.pad_token_id
         return shifted_input_ids
 
-    def _encode(self, batch) -> Dict[str, torch.Tensor]:
+    def _encode(self, batch) -> dict[str, torch.Tensor]:
         batch_encoding = self.tokenizer.prepare_seq2seq_batch(
             [x["src_texts"] for x in batch],
             tgt_texts=[x["tgt_texts"] for x in batch],
@@ -355,7 +356,7 @@ class SortishSampler(Sampler):
         return iter(sortish_sampler_indices(self.data, self.bs, shuffle=self.shuffle))
 
 
-def sortish_sampler_indices(data: List, bs: int, shuffle=True) -> np.array:
+def sortish_sampler_indices(data: list, bs: int, shuffle=True) -> np.array:
     "Go through the text data by order of src length with a bit of randomness. From fastai repo."
     if not shuffle:
         return np.argsort(np.array(data) * -1)
@@ -455,7 +456,7 @@ def pickle_save(obj, path):
         return pickle.dump(obj, f)
 
 
-def flatten_list(summary_ids: List[List]):
+def flatten_list(summary_ids: list[list]):
     return list(itertools.chain.from_iterable(summary_ids))
 
 
@@ -506,14 +507,14 @@ def extract_rouge_mid_statistics(dct):
 
 
 def calculate_rouge(
-    pred_lns: List[str],
+    pred_lns: list[str],
-    tgt_lns: List[str],
+    tgt_lns: list[str],
     use_stemmer=True,
     rouge_keys=ROUGE_KEYS,
     return_precision_and_recall=False,
     bootstrap_aggregation=True,
     newline_sep=True,
-) -> Dict:
+) -> dict:
     """Calculate rouge using rouge_scorer package.
 
     Args:
@@ -590,19 +591,19 @@ def any_requires_grad(model: nn.Module) -> bool:
 
 
 def assert_all_frozen(model):
-    model_grads: List[bool] = list(grad_status(model))
+    model_grads: list[bool] = list(grad_status(model))
     n_require_grad = sum(lmap(int, model_grads))
     npars = len(model_grads)
     assert not any(model_grads), f"{n_require_grad / npars:.1%} of {npars} weights require grad"
 
 
 def assert_not_all_frozen(model):
-    model_grads: List[bool] = list(grad_status(model))
+    model_grads: list[bool] = list(grad_status(model))
     npars = len(model_grads)
     assert any(model_grads), f"none of {npars} weights require grad"
 
 
-def parse_numeric_n_bool_cl_kwargs(unparsed_args: List[str]) -> Dict[str, Union[int, float, bool]]:
+def parse_numeric_n_bool_cl_kwargs(unparsed_args: list[str]) -> dict[str, Union[int, float, bool]]:
     """
     Parse an argv list of unspecified command line args to a dict.
     Assumes all values are either numeric or boolean in the form of true/false.

examples/legacy/token-classification/run_ner.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
@@ -20,7 +19,7 @@ import os
 import sys
 from dataclasses import dataclass, field
 from importlib import import_module
-from typing import Dict, List, Optional, Tuple
+from typing import Optional
 
 import numpy as np
 from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
@@ -159,7 +158,7 @@ def main():
 
     # Prepare CONLL-2003 task
     labels = token_classification_task.get_labels(data_args.labels)
-    label_map: Dict[int, str] = dict(enumerate(labels))
+    label_map: dict[int, str] = dict(enumerate(labels))
     num_labels = len(labels)
 
     # Load pretrained model and tokenizer
@@ -217,7 +216,7 @@ def main():
         else None
     )
 
-    def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:
+    def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> tuple[list[int], list[int]]:
         preds = np.argmax(predictions, axis=2)
 
         batch_size, seq_len = preds.shape
@@ -233,7 +232,7 @@ def main():
 
         return preds_list, out_label_list
 
-    def compute_metrics(p: EvalPrediction) -> Dict:
+    def compute_metrics(p: EvalPrediction) -> dict:
         preds_list, out_label_list = align_predictions(p.predictions, p.label_ids)
         return {
             "accuracy_score": accuracy_score(out_label_list, preds_list),
@@ -279,7 +278,7 @@ def main():
                 logger.info("***** Eval results *****")
                 for key, value in result.items():
                     logger.info("  %s = %s", key, value)
-                    writer.write("%s = %s\n" % (key, value))
+                    writer.write("{} = {}\n".format(key, value))
 
             results.update(result)
 
@@ -304,13 +303,13 @@ def main():
             with open(output_test_results_file, "w") as writer:
                 for key, value in metrics.items():
                     logger.info("  %s = %s", key, value)
-                    writer.write("%s = %s\n" % (key, value))
+                    writer.write("{} = {}\n".format(key, value))
 
         # Save predictions
         output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt")
         if trainer.is_world_process_zero():
             with open(output_test_predictions_file, "w") as writer:
-                with open(os.path.join(data_args.data_dir, "test.txt"), "r") as f:
+                with open(os.path.join(data_args.data_dir, "test.txt")) as f:
                     token_classification_task.write_predictions_to_file(writer, f, preds_list)
 
     return results

examples/legacy/token-classification/scripts/preprocess.py

@@ -12,7 +12,7 @@ subword_len_counter = 0
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
 max_len -= tokenizer.num_special_tokens_to_add()
 
-with open(dataset, "rt") as f_p:
+with open(dataset) as f_p:
     for line in f_p:
         line = line.rstrip()
 

examples/legacy/token-classification/tasks.py

@@ -1,6 +1,6 @@
 import logging
 import os
-from typing import List, TextIO, Union
+from typing import TextIO, Union
 
 from conllu import parse_incr
 from utils_ner import InputExample, Split, TokenClassificationTask
@@ -14,7 +14,7 @@ class NER(TokenClassificationTask):
         # in NER datasets, the last column is usually reserved for NER label
         self.label_idx = label_idx
 
-    def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]:
+    def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> list[InputExample]:
         if isinstance(mode, Split):
             mode = mode.value
         file_path = os.path.join(data_dir, f"{mode}.txt")
@@ -42,7 +42,7 @@ class NER(TokenClassificationTask):
                 examples.append(InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels))
         return examples
 
-    def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List):
+    def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: list):
         example_id = 0
         for line in test_input_reader:
             if line.startswith("-DOCSTART-") or line == "" or line == "\n":
@@ -55,9 +55,9 @@ class NER(TokenClassificationTask):
             else:
                 logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0])
 
-    def get_labels(self, path: str) -> List[str]:
+    def get_labels(self, path: str) -> list[str]:
         if path:
-            with open(path, "r") as f:
+            with open(path) as f:
                 labels = f.read().splitlines()
             if "O" not in labels:
                 labels = ["O"] + labels
@@ -71,9 +71,9 @@ class Chunk(NER):
         # in CONLL2003 dataset chunk column is second-to-last
         super().__init__(label_idx=-2)
 
-    def get_labels(self, path: str) -> List[str]:
+    def get_labels(self, path: str) -> list[str]:
         if path:
-            with open(path, "r") as f:
+            with open(path) as f:
                 labels = f.read().splitlines()
             if "O" not in labels:
                 labels = ["O"] + labels
@@ -105,7 +105,7 @@ class Chunk(NER):
 
 
 class POS(TokenClassificationTask):
-    def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]:
+    def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> list[InputExample]:
         if isinstance(mode, Split):
             mode = mode.value
         file_path = os.path.join(data_dir, f"{mode}.txt")
@@ -125,7 +125,7 @@ class POS(TokenClassificationTask):
                     guid_index += 1
         return examples
 
-    def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List):
+    def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: list):
         example_id = 0
         for sentence in parse_incr(test_input_reader):
             s_p = preds_list[example_id]
@@ -136,9 +136,9 @@ class POS(TokenClassificationTask):
             writer.write(out)
             example_id += 1
 
-    def get_labels(self, path: str) -> List[str]:
+    def get_labels(self, path: str) -> list[str]:
         if path:
-            with open(path, "r") as f:
+            with open(path) as f:
                 return f.read().splitlines()
         else:
             return [

examples/legacy/token-classification/utils_ner.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
@@ -19,7 +18,7 @@ import logging
 import os
 from dataclasses import dataclass
 from enum import Enum
-from typing import List, Optional, Union
+from typing import Optional, Union
 
 from filelock import FileLock
 
@@ -42,8 +41,8 @@ class InputExample:
     """
 
     guid: str
-    words: List[str]
+    words: list[str]
-    labels: Optional[List[str]]
+    labels: Optional[list[str]]
 
 
 @dataclass
@@ -53,10 +52,10 @@ class InputFeatures:
     Property names are the same names as the corresponding inputs to a model.
     """
 
-    input_ids: List[int]
+    input_ids: list[int]
-    attention_mask: List[int]
+    attention_mask: list[int]
-    token_type_ids: Optional[List[int]] = None
+    token_type_ids: Optional[list[int]] = None
-    label_ids: Optional[List[int]] = None
+    label_ids: Optional[list[int]] = None
 
 
 class Split(Enum):
@@ -67,17 +66,17 @@ class Split(Enum):
 
 class TokenClassificationTask:
     @staticmethod
-    def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]:
+    def read_examples_from_file(data_dir, mode: Union[Split, str]) -> list[InputExample]:
         raise NotImplementedError
 
     @staticmethod
-    def get_labels(path: str) -> List[str]:
+    def get_labels(path: str) -> list[str]:
         raise NotImplementedError
 
     @staticmethod
     def convert_examples_to_features(
-        examples: List[InputExample],
+        examples: list[InputExample],
-        label_list: List[str],
+        label_list: list[str],
         max_seq_length: int,
         tokenizer: PreTrainedTokenizer,
         cls_token_at_end=False,
@@ -91,7 +90,7 @@ class TokenClassificationTask:
         pad_token_label_id=-100,
         sequence_a_segment_id=0,
         mask_padding_with_zero=True,
-    ) -> List[InputFeatures]:
+    ) -> list[InputFeatures]:
         """Loads a data file into a list of `InputFeatures`
         `cls_token_at_end` define the location of the CLS token:
             - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
@@ -214,7 +213,7 @@ if is_torch_available():
         soon.
         """
 
-        features: List[InputFeatures]
+        features: list[InputFeatures]
         pad_token_label_id: int = nn.CrossEntropyLoss().ignore_index
         # Use cross entropy ignore_index as padding label id so that only
         # real label ids contribute to the loss later.
@@ -224,7 +223,7 @@ if is_torch_available():
             token_classification_task: TokenClassificationTask,
             data_dir: str,
             tokenizer: PreTrainedTokenizer,
-            labels: List[str],
+            labels: list[str],
             model_type: str,
             max_seq_length: Optional[int] = None,
             overwrite_cache=False,
@@ -233,7 +232,7 @@ if is_torch_available():
             # Load data features from cache or dataset file
             cached_features_file = os.path.join(
                 data_dir,
-                "cached_{}_{}_{}".format(mode.value, tokenizer.__class__.__name__, str(max_seq_length)),
+                f"cached_{mode.value}_{tokenizer.__class__.__name__}_{str(max_seq_length)}",
             )
 
             # Make sure only the first process in distributed training processes the dataset,
@@ -283,7 +282,7 @@ if is_tf_available():
         soon.
         """
 
-        features: List[InputFeatures]
+        features: list[InputFeatures]
         pad_token_label_id: int = -100
         # Use cross entropy ignore_index as padding label id so that only
         # real label ids contribute to the loss later.
@@ -293,7 +292,7 @@ if is_tf_available():
             token_classification_task: TokenClassificationTask,
             data_dir: str,
             tokenizer: PreTrainedTokenizer,
-            labels: List[str],
+            labels: list[str],
             model_type: str,
             max_seq_length: Optional[int] = None,
             overwrite_cache=False,

examples/modular-transformers/image_processing_new_imgproc_model.py

@@ -4,7 +4,7 @@
 #             the file from the modular. If any change should be done, please apply the change to the
 #                          modular_new_imgproc_model.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-from typing import Dict, List, Optional, Union
+from typing import Optional, Union
 
 import numpy as np
 import torch
@@ -74,13 +74,13 @@ class ImgprocModelImageProcessor(BaseImageProcessor):
     def __init__(
         self,
         do_resize: bool = True,
-        size: Dict[str, int] = None,
+        size: dict[str, int] = None,
         resample: PILImageResampling = PILImageResampling.BICUBIC,
         do_rescale: bool = True,
         rescale_factor: Union[int, float] = 1 / 255,
         do_normalize: bool = True,
-        image_mean: Optional[Union[float, List[float]]] = None,
+        image_mean: Optional[Union[float, list[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, list[float]]] = None,
         do_convert_rgb: bool = True,
         **kwargs,
     ) -> None:
@@ -101,7 +101,7 @@ class ImgprocModelImageProcessor(BaseImageProcessor):
     def resize(
         self,
         image: np.ndarray,
-        size: Dict[str, int],
+        size: dict[str, int],
         resample: PILImageResampling = PILImageResampling.BICUBIC,
         data_format: Optional[Union[str, ChannelDimension]] = None,
         input_data_format: Optional[Union[str, ChannelDimension]] = None,
@@ -151,13 +151,13 @@ class ImgprocModelImageProcessor(BaseImageProcessor):
         self,
         images: ImageInput,
         do_resize: Optional[bool] = None,
-        size: Optional[Dict[str, int]] = None,
+        size: Optional[dict[str, int]] = None,
         resample: PILImageResampling = None,
         do_rescale: Optional[bool] = None,
         rescale_factor: Optional[float] = None,
         do_normalize: Optional[bool] = None,
-        image_mean: Optional[Union[float, List[float]]] = None,
+        image_mean: Optional[Union[float, list[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, list[float]]] = None,
         return_tensors: Optional[Union[str, TensorType]] = None,
         do_convert_rgb: bool = None,
         data_format: ChannelDimension = ChannelDimension.FIRST,

examples/modular-transformers/modeling_add_function.py

@@ -5,7 +5,7 @@
 #                          modular_add_function.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 # Note that zamba does not have the `apply_rotary_pos_emb` function!
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
 from torch import nn
@@ -62,5 +62,5 @@ class TestAttention(nn.Module):
     def __init__(self):
         pass
 
-    def forward(self) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    def forward(self) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
         _ = apply_rotary_pos_emb(1, 1, 1, 1)

examples/modular-transformers/modeling_dummy.py

@@ -5,7 +5,7 @@
 #                          modular_dummy.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 from functools import partial
-from typing import Callable, Optional, Tuple, Union
+from typing import Callable, Optional, Union
 
 import torch
 from torch import nn
@@ -223,12 +223,12 @@ class DummyAttention(nn.Module):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
         attention_mask: Optional[torch.Tensor],
         past_key_value: Optional[Cache] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, self.head_dim)
 
@@ -290,9 +290,9 @@ class DummyDecoderLayer(nn.Module):
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
         **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
         residual = hidden_states
 
         hidden_states = self.input_layernorm(hidden_states)
@@ -494,7 +494,7 @@ class DummyModel(DummyPreTrainedModel):
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
+    ) -> Union[tuple, BaseModelOutputWithPast]:
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

examples/modular-transformers/modeling_dummy_bert.py

@@ -6,7 +6,7 @@
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 import math
 import os
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 
 import torch
 from packaging import version
@@ -136,9 +136,9 @@ class DummyBertSelfAttention(nn.Module):
         head_mask: Optional[torch.FloatTensor] = None,
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
+    ) -> tuple[torch.Tensor]:
         mixed_query_layer = self.query(hidden_states)
 
         # If this is instantiated as a cross-attention module, the keys
@@ -245,9 +245,9 @@ class DummyBertSdpaSelfAttention(DummyBertSelfAttention):
         head_mask: Optional[torch.FloatTensor] = None,
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
+    ) -> tuple[torch.Tensor]:
         if self.position_embedding_type != "absolute" or output_attentions or head_mask is not None:
             # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once implemented.
             logger.warning_once(
@@ -386,9 +386,9 @@ class DummyBertAttention(nn.Module):
         head_mask: Optional[torch.FloatTensor] = None,
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
+    ) -> tuple[torch.Tensor]:
         self_outputs = self.self(
             hidden_states,
             attention_mask,
@@ -454,9 +454,9 @@ class DummyBertLayer(nn.Module):
         head_mask: Optional[torch.FloatTensor] = None,
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
+    ) -> tuple[torch.Tensor]:
         # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
         self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
         self_attention_outputs = self.attention(
@@ -532,12 +532,12 @@ class DummyBertEncoder(nn.Module):
         head_mask: Optional[torch.FloatTensor] = None,
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = False,
         output_hidden_states: Optional[bool] = False,
         return_dict: Optional[bool] = True,
-    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
+    ) -> Union[tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
         all_hidden_states = () if output_hidden_states else None
         all_self_attentions = () if output_attentions else None
         all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
@@ -858,12 +858,12 @@ class DummyBertModel(DummyBertPreTrainedModel):
         inputs_embeds: Optional[torch.Tensor] = None,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         encoder_attention_mask: Optional[torch.Tensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        past_key_values: Optional[list[torch.FloatTensor]] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+    ) -> Union[tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
         r"""
         encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
             Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if

examples/modular-transformers/modeling_from_uppercase_model.py

@@ -4,7 +4,7 @@
 #             the file from the modular. If any change should be done, please apply the change to the
 #                          modular_from_uppercase_model.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
 from torch import nn
@@ -53,7 +53,7 @@ class FromUppercaseModelAttention(nn.Module):
         attention_mask: Optional[torch.Tensor] = None,
         causal_attention_mask: Optional[torch.Tensor] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         """Input shape: Batch x Time x Channel"""
 
         bsz, tgt_len, embed_dim = hidden_states.size()
@@ -148,7 +148,7 @@ class FromUppercaseModelFlashAttention2(FromUppercaseModelAttention):
         attention_mask: Optional[torch.Tensor] = None,
         causal_attention_mask: Optional[torch.Tensor] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         output_attentions = False
 
         batch_size, q_len, _ = hidden_states.size()
@@ -226,7 +226,7 @@ class FromUppercaseModelSdpaAttention(FromUppercaseModelAttention):
         attention_mask: Optional[torch.Tensor] = None,
         causal_attention_mask: Optional[torch.Tensor] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         if output_attentions:
             # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
             logger.warning_once(
@@ -322,7 +322,7 @@ class FromUppercaseModelEncoderLayer(nn.Module):
         attention_mask: torch.Tensor,
         causal_attention_mask: torch.Tensor,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor]:
+    ) -> tuple[torch.FloatTensor]:
         """
         Args:
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`

examples/modular-transformers/modeling_multimodal1.py

@@ -5,7 +5,7 @@
 #                          modular_multimodal1.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 from functools import partial
-from typing import Callable, Optional, Tuple, Union
+from typing import Callable, Optional, Union
 
 import torch
 from torch import nn
@@ -223,12 +223,12 @@ class Multimodal1TextAttention(nn.Module):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
         attention_mask: Optional[torch.Tensor],
         past_key_value: Optional[Cache] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, self.head_dim)
 
@@ -290,9 +290,9 @@ class Multimodal1TextDecoderLayer(nn.Module):
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
         **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
         residual = hidden_states
 
         hidden_states = self.input_layernorm(hidden_states)
@@ -494,7 +494,7 @@ class Multimodal1TextModel(Multimodal1TextPreTrainedModel):
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
+    ) -> Union[tuple, BaseModelOutputWithPast]:
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

examples/modular-transformers/modeling_multimodal2.py

@@ -5,7 +5,7 @@
 #                          modular_multimodal2.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 
-from typing import Optional, Tuple, Union
+from typing import Optional, Union
 
 import torch
 from torch import nn
@@ -65,7 +65,7 @@ class Multimodal2VisionAttention(nn.Module):
         attention_mask: Optional[torch.Tensor] = None,
         causal_attention_mask: Optional[torch.Tensor] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         """Input shape: Batch x Time x Channel"""
 
         bsz, tgt_len, embed_dim = hidden_states.size()
@@ -152,7 +152,7 @@ class Multimodal2VisionSdpaAttention(Multimodal2VisionAttention):
         attention_mask: Optional[torch.Tensor] = None,
         causal_attention_mask: Optional[torch.Tensor] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         if output_attentions:
             # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
             logger.warning_once(
@@ -233,7 +233,7 @@ class Multimodal2VisionFlashAttention2(Multimodal2VisionAttention):
         attention_mask: Optional[torch.Tensor] = None,
         causal_attention_mask: Optional[torch.Tensor] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         output_attentions = False
 
         batch_size, q_len, _ = hidden_states.size()
@@ -334,7 +334,7 @@ class Multimodal2VisionEncoderLayer(nn.Module):
         attention_mask: torch.Tensor,
         causal_attention_mask: torch.Tensor,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor]:
+    ) -> tuple[torch.FloatTensor]:
         """
         Args:
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
@@ -392,7 +392,7 @@ class Multimodal2VisionEncoder(nn.Module):
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutput]:
+    ) -> Union[tuple, BaseModelOutput]:
         r"""
         Args:
             inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -587,7 +587,7 @@ class Multimodal2VisionTransformer(nn.Module):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         interpolate_pos_encoding: Optional[bool] = False,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+    ) -> Union[tuple, BaseModelOutputWithPooling]:
         r"""
         Returns:
 
@@ -671,7 +671,7 @@ class Multimodal2VisionModel(Multimodal2VisionPreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         interpolate_pos_encoding: bool = False,
         return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+    ) -> Union[tuple, BaseModelOutputWithPooling]:
         r"""
         Returns:
 

examples/modular-transformers/modeling_my_new_model2.py

@@ -4,7 +4,7 @@
 #             the file from the modular. If any change should be done, please apply the change to the
 #                          modular_my_new_model2.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-from typing import Callable, List, Optional, Tuple, Union
+from typing import Callable, Optional, Union
 
 import torch
 from torch import nn
@@ -222,12 +222,12 @@ class MyNewModel2Attention(nn.Module):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
         attention_mask: Optional[torch.Tensor],
         past_key_value: Optional[Cache] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, self.head_dim)
 
@@ -289,9 +289,9 @@ class MyNewModel2DecoderLayer(nn.Module):
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
         **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
         residual = hidden_states
 
         hidden_states = self.input_layernorm(hidden_states)
@@ -485,7 +485,7 @@ class MyNewModel2Model(MyNewModel2PreTrainedModel):
         input_ids: torch.LongTensor = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
@@ -493,7 +493,7 @@ class MyNewModel2Model(MyNewModel2PreTrainedModel):
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs,  # NOOP kwarg for now
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
+    ) -> Union[tuple, BaseModelOutputWithPast]:
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -753,14 +753,14 @@ class MyNewModel2ForSequenceClassification(MyNewModel2PreTrainedModel):
         input_ids: Optional[torch.LongTensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.LongTensor] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+    ) -> Union[tuple, SequenceClassifierOutputWithPast]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,

examples/modular-transformers/modeling_new_task_model.py

@@ -5,7 +5,7 @@
 #                          modular_new_task_model.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 from dataclasses import dataclass
-from typing import ClassVar, List, Optional, Tuple, Union
+from typing import ClassVar, Optional, Union
 
 import torch
 from torch import nn
@@ -61,9 +61,9 @@ class NewTaskModelCausalLMOutputWithPast(ModelOutput):
 
     loss: Optional[torch.FloatTensor] = None
     logits: torch.FloatTensor = None
-    past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None
+    past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    hidden_states: Optional[tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[tuple[torch.FloatTensor]] = None
     image_hidden_states: Optional[torch.FloatTensor] = None
 
 
@@ -337,7 +337,7 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
         pixel_values: torch.FloatTensor = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None,
+        past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
         token_type_ids: Optional[torch.LongTensor] = None,
         cache_position: Optional[torch.LongTensor] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
@@ -347,7 +347,7 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         num_logits_to_keep: int = 0,
-    ) -> Union[Tuple, NewTaskModelCausalLMOutputWithPast]:
+    ) -> Union[tuple, NewTaskModelCausalLMOutputWithPast]:
         r"""
             labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                 Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,

examples/modular-transformers/modeling_roberta.py

@@ -6,7 +6,7 @@
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 import math
 import os
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 
 import torch
 import torch.nn as nn
@@ -139,9 +139,9 @@ class RobertaSelfAttention(nn.Module):
         head_mask: Optional[torch.FloatTensor] = None,
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
+    ) -> tuple[torch.Tensor]:
         mixed_query_layer = self.query(hidden_states)
 
         # If this is instantiated as a cross-attention module, the keys
@@ -248,9 +248,9 @@ class RobertaSdpaSelfAttention(RobertaSelfAttention):
         head_mask: Optional[torch.FloatTensor] = None,
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
+    ) -> tuple[torch.Tensor]:
         if self.position_embedding_type != "absolute" or output_attentions or head_mask is not None:
             # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once implemented.
             logger.warning_once(
@@ -389,9 +389,9 @@ class RobertaAttention(nn.Module):
         head_mask: Optional[torch.FloatTensor] = None,
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
+    ) -> tuple[torch.Tensor]:
         self_outputs = self.self(
             hidden_states,
             attention_mask,
@@ -457,9 +457,9 @@ class RobertaLayer(nn.Module):
         head_mask: Optional[torch.FloatTensor] = None,
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
         output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
+    ) -> tuple[torch.Tensor]:
         # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
         self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
         self_attention_outputs = self.attention(
@@ -535,12 +535,12 @@ class RobertaEncoder(nn.Module):
         head_mask: Optional[torch.FloatTensor] = None,
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = False,
         output_hidden_states: Optional[bool] = False,
         return_dict: Optional[bool] = True,
-    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
+    ) -> Union[tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
         all_hidden_states = () if output_hidden_states else None
         all_self_attentions = () if output_attentions else None
         all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
@@ -861,12 +861,12 @@ class RobertaModel(RobertaPreTrainedModel):
         inputs_embeds: Optional[torch.Tensor] = None,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         encoder_attention_mask: Optional[torch.Tensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        past_key_values: Optional[list[torch.FloatTensor]] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+    ) -> Union[tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
         r"""
         encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
             Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if

examples/modular-transformers/modeling_super.py

@@ -4,7 +4,7 @@
 #             the file from the modular. If any change should be done, please apply the change to the
 #                          modular_super.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-from typing import Callable, List, Optional, Tuple, Union
+from typing import Callable, Optional, Union
 
 import torch
 from torch import nn
@@ -222,12 +222,12 @@ class SuperAttention(nn.Module):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
         attention_mask: Optional[torch.Tensor],
         past_key_value: Optional[Cache] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, self.head_dim)
 
@@ -289,9 +289,9 @@ class SuperDecoderLayer(nn.Module):
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
         **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
         residual = hidden_states
 
         hidden_states = self.input_layernorm(hidden_states)
@@ -485,14 +485,14 @@ class SuperModel(SuperPreTrainedModel):
         input_ids: torch.LongTensor = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
+    ) -> Union[tuple, BaseModelOutputWithPast]:
         out = super().forward(
             input_ids,
             attention_mask,

examples/modular-transformers/modeling_switch_function.py

@@ -5,7 +5,7 @@
 #                          modular_switch_function.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 # Note that llama and cohere have different definitions for rotate_half
-from typing import Callable, Optional, Tuple
+from typing import Callable, Optional
 
 import torch
 from torch import nn
@@ -123,12 +123,12 @@ class SwitchFunctionAttention(nn.Module):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
         attention_mask: Optional[torch.Tensor],
         past_key_value: Optional[Cache] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, self.head_dim)
 

examples/modular-transformers/modular_dummy_bert.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 
 import torch
 
@@ -18,10 +18,10 @@ class DummyBertModel(BertModel):
         inputs_embeds: Optional[torch.Tensor] = None,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         encoder_attention_mask: Optional[torch.Tensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        past_key_values: Optional[list[torch.FloatTensor]] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+    ) -> Union[tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
         return super().forward(input_ids)

examples/modular-transformers/modular_new_task_model.py

@@ -1,4 +1,4 @@
-from typing import ClassVar, List, Optional, Union
+from typing import ClassVar, Optional, Union
 
 import torch
 import torch.utils.checkpoint
@@ -29,7 +29,7 @@ class NewTaskModelForNewTask(PaliGemmaForConditionalGeneration):
         pixel_values: torch.FloatTensor = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None,
+        past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
         token_type_ids: Optional[torch.LongTensor] = None,
         cache_position: Optional[torch.LongTensor] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,

examples/modular-transformers/modular_super.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 
 import torch
 
@@ -15,14 +15,14 @@ class SuperModel(LlamaModel):
         input_ids: torch.LongTensor = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
+    ) -> Union[tuple, CausalLMOutputWithPast]:
         out = super().forward(
             input_ids,
             attention_mask,

examples/pytorch/audio-classification/run_audio_classification.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/contrastive-image-text/run_clip.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2022 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/image-classification/run_image_classification.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/image-pretraining/run_mae.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/image-pretraining/run_mim.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/image-pretraining/run_mim_no_trainer.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/instance-segmentation/run_instance_segmentation.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -18,9 +17,10 @@
 import logging
 import os
 import sys
+from collections.abc import Mapping
 from dataclasses import dataclass, field
 from functools import partial
-from typing import Any, Dict, List, Mapping, Optional
+from typing import Any, Optional
 
 import albumentations as A
 import numpy as np
@@ -200,7 +200,7 @@ class Evaluator:
     def reset_metric(self):
         self.metric.reset()
 
-    def postprocess_target_batch(self, target_batch) -> List[Dict[str, torch.Tensor]]:
+    def postprocess_target_batch(self, target_batch) -> list[dict[str, torch.Tensor]]:
         """Collect targets in a form of list of dictionaries with keys "masks", "labels"."""
         batch_masks = target_batch[0]
         batch_labels = target_batch[1]
@@ -214,13 +214,13 @@ class Evaluator:
             )
         return post_processed_targets
 
-    def get_target_sizes(self, post_processed_targets) -> List[List[int]]:
+    def get_target_sizes(self, post_processed_targets) -> list[list[int]]:
         target_sizes = []
         for target in post_processed_targets:
             target_sizes.append(target["masks"].shape[-2:])
         return target_sizes
 
-    def postprocess_prediction_batch(self, prediction_batch, target_sizes) -> List[Dict[str, torch.Tensor]]:
+    def postprocess_prediction_batch(self, prediction_batch, target_sizes) -> list[dict[str, torch.Tensor]]:
         """Collect predictions in a form of list of dictionaries with keys "masks", "labels", "scores"."""
 
         model_output = ModelOutput(class_queries_logits=prediction_batch[0], masks_queries_logits=prediction_batch[1])

examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -21,9 +20,10 @@ import logging
 import math
 import os
 import sys
+from collections.abc import Mapping
 from functools import partial
 from pathlib import Path
-from typing import Any, Mapping
+from typing import Any
 
 import albumentations as A
 import datasets

examples/pytorch/language-modeling/run_clm.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2020 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/language-modeling/run_fim.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -551,7 +550,7 @@ def main():
                 covariance_matrix=1e-5 * sigma,
             )
             new_token_embeddings = torch.stack(
-                tuple((dist.sample() for _ in range(len(special_tokens)))),
+                tuple(dist.sample() for _ in range(len(special_tokens))),
                 dim=0,
             )
     else:
@@ -571,7 +570,7 @@ def main():
             covariance_matrix=1e-5 * sigma,
         )
         new_token_embeddings = torch.stack(
-            tuple((dist.sample() for _ in range(len(special_tokens)))),
+            tuple(dist.sample() for _ in range(len(special_tokens))),
             dim=0,
         )
 

examples/pytorch/language-modeling/run_fim_no_trainer.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -518,7 +517,7 @@ def main():
                 covariance_matrix=1e-5 * sigma,
             )
             new_token_embeddings = torch.stack(
-                tuple((dist.sample() for _ in range(len(special_tokens)))),
+                tuple(dist.sample() for _ in range(len(special_tokens))),
                 dim=0,
             )
     else:
@@ -538,7 +537,7 @@ def main():
             covariance_matrix=1e-5 * sigma,
         )
         new_token_embeddings = torch.stack(
-            tuple((dist.sample() for _ in range(len(special_tokens)))),
+            tuple(dist.sample() for _ in range(len(special_tokens))),
             dim=0,
         )
 

examples/pytorch/language-modeling/run_mlm.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2020 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/language-modeling/run_plm.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2020 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/multiple-choice/run_swag.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/object-detection/run_object_detection.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -18,9 +17,10 @@
 import logging
 import os
 import sys
+from collections.abc import Mapping
 from dataclasses import dataclass, field
 from functools import partial
-from typing import Any, List, Mapping, Optional, Tuple, Union
+from typing import Any, Optional, Union
 
 import albumentations as A
 import numpy as np
@@ -60,7 +60,7 @@ class ModelOutput:
 
 
 def format_image_annotations_as_coco(
-    image_id: str, categories: List[int], areas: List[float], bboxes: List[Tuple[float]]
+    image_id: str, categories: list[int], areas: list[float], bboxes: list[tuple[float]]
 ) -> dict:
     """Format one set of image annotations to the COCO format
 
@@ -94,7 +94,7 @@ def format_image_annotations_as_coco(
     }
 
 
-def convert_bbox_yolo_to_pascal(boxes: torch.Tensor, image_size: Tuple[int, int]) -> torch.Tensor:
+def convert_bbox_yolo_to_pascal(boxes: torch.Tensor, image_size: tuple[int, int]) -> torch.Tensor:
     """
     Convert bounding boxes from YOLO format (x_center, y_center, width, height) in range [0, 1]
     to Pascal VOC format (x_min, y_min, x_max, y_max) in absolute coordinates.
@@ -148,7 +148,7 @@ def augment_and_transform_batch(
     return result
 
 
-def collate_fn(batch: List[BatchFeature]) -> Mapping[str, Union[torch.Tensor, List[Any]]]:
+def collate_fn(batch: list[BatchFeature]) -> Mapping[str, Union[torch.Tensor, list[Any]]]:
     data = {}
     data["pixel_values"] = torch.stack([x["pixel_values"] for x in batch])
     data["labels"] = [x["labels"] for x in batch]

examples/pytorch/object-detection/run_object_detection_no_trainer.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -19,9 +18,10 @@ import json
 import logging
 import math
 import os
+from collections.abc import Mapping
 from functools import partial
 from pathlib import Path
-from typing import Any, List, Mapping, Tuple, Union
+from typing import Any, Union
 
 import albumentations as A
 import datasets
@@ -61,7 +61,7 @@ require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/sema
 
 # Copied from examples/pytorch/object-detection/run_object_detection.format_image_annotations_as_coco
 def format_image_annotations_as_coco(
-    image_id: str, categories: List[int], areas: List[float], bboxes: List[Tuple[float]]
+    image_id: str, categories: list[int], areas: list[float], bboxes: list[tuple[float]]
 ) -> dict:
     """Format one set of image annotations to the COCO format
 
@@ -96,7 +96,7 @@ def format_image_annotations_as_coco(
 
 
 # Copied from examples/pytorch/object-detection/run_object_detection.convert_bbox_yolo_to_pascal
-def convert_bbox_yolo_to_pascal(boxes: torch.Tensor, image_size: Tuple[int, int]) -> torch.Tensor:
+def convert_bbox_yolo_to_pascal(boxes: torch.Tensor, image_size: tuple[int, int]) -> torch.Tensor:
     """
     Convert bounding boxes from YOLO format (x_center, y_center, width, height) in range [0, 1]
     to Pascal VOC format (x_min, y_min, x_max, y_max) in absolute coordinates.
@@ -152,7 +152,7 @@ def augment_and_transform_batch(
 
 
 # Copied from examples/pytorch/object-detection/run_object_detection.collate_fn
-def collate_fn(batch: List[BatchFeature]) -> Mapping[str, Union[torch.Tensor, List[Any]]]:
+def collate_fn(batch: list[BatchFeature]) -> Mapping[str, Union[torch.Tensor, list[Any]]]:
     data = {}
     data["pixel_values"] = torch.stack([x["pixel_values"] for x in batch])
     data["labels"] = [x["labels"] for x in batch]

examples/pytorch/old_test_xla_examples.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2018 HuggingFace Inc..
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -33,7 +32,7 @@ def get_results(output_dir):
     results = {}
     path = os.path.join(output_dir, "all_results.json")
     if os.path.exists(path):
-        with open(path, "r") as f:
+        with open(path) as f:
             results = json.load(f)
     else:
         raise ValueError(f"can't find {path}")

examples/pytorch/question-answering/run_qa.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2020 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/question-answering/run_qa_beam_search.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2020 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/question-answering/run_seq2seq_qa.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -22,7 +21,7 @@ import logging
 import os
 import sys
 from dataclasses import dataclass, field
-from typing import List, Optional, Tuple
+from typing import Optional
 
 import datasets
 import evaluate
@@ -469,7 +468,7 @@ def main():
         question_column: str,
         context_column: str,
         answer_column: str,
-    ) -> Tuple[List[str], List[str]]:
+    ) -> tuple[list[str], list[str]]:
         questions = examples[question_column]
         contexts = examples[context_column]
         answers = examples[answer_column]

examples/pytorch/question-answering/trainer_qa.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2020 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/question-answering/trainer_seq2seq_qa.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -18,7 +17,7 @@ A subclass of `Trainer` specific to Question-Answering tasks
 
 import math
 import time
-from typing import Dict, List, Optional
+from typing import Optional
 
 from torch.utils.data import Dataset
 
@@ -42,10 +41,10 @@ class QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer):
         self,
         eval_dataset: Optional[Dataset] = None,
         eval_examples=None,
-        ignore_keys: Optional[List[str]] = None,
+        ignore_keys: Optional[list[str]] = None,
         metric_key_prefix: str = "eval",
         **gen_kwargs,
-    ) -> Dict[str, float]:
+    ) -> dict[str, float]:
         gen_kwargs = gen_kwargs.copy()
 
         # Use legacy argument setting if a) the option is not explicitly passed; and b) the argument is set in the

examples/pytorch/question-answering/utils_qa.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2020 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -20,7 +19,7 @@ import collections
 import json
 import logging
 import os
-from typing import Optional, Tuple
+from typing import Optional
 
 import numpy as np
 from tqdm.auto import tqdm
@@ -32,7 +31,7 @@ logger = logging.getLogger(__name__)
 def postprocess_qa_predictions(
     examples,
     features,
-    predictions: Tuple[np.ndarray, np.ndarray],
+    predictions: tuple[np.ndarray, np.ndarray],
     version_2_with_negative: bool = False,
     n_best_size: int = 20,
     max_answer_length: int = 30,
@@ -223,7 +222,7 @@ def postprocess_qa_predictions(
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
         if not os.path.isdir(output_dir):
-            raise EnvironmentError(f"{output_dir} is not a directory.")
+            raise OSError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
@@ -253,7 +252,7 @@ def postprocess_qa_predictions(
 def postprocess_qa_predictions_with_beam_search(
     examples,
     features,
-    predictions: Tuple[np.ndarray, np.ndarray],
+    predictions: tuple[np.ndarray, np.ndarray],
     version_2_with_negative: bool = False,
     n_best_size: int = 20,
     max_answer_length: int = 30,
@@ -417,7 +416,7 @@ def postprocess_qa_predictions_with_beam_search(
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
         if not os.path.isdir(output_dir):
-            raise EnvironmentError(f"{output_dir} is not a directory.")
+            raise OSError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"

examples/pytorch/semantic-segmentation/run_semantic_segmentation.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -258,7 +257,7 @@ def main():
     else:
         repo_id = data_args.dataset_name
         filename = "id2label.json"
-    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
+    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset")))
     id2label = {int(k): v for k, v in id2label.items()}
     label2id = {v: str(k) for k, v in id2label.items()}
 

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -316,7 +315,7 @@ def main():
     else:
         repo_id = args.dataset_name
         filename = "id2label.json"
-    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
+    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset")))
     id2label = {int(k): v for k, v in id2label.items()}
     label2id = {v: k for k, v in id2label.items()}
 

examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -20,7 +19,7 @@ import math
 import os
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Dict, List, Optional, Union
+from typing import Optional, Union
 
 import datasets
 import torch
@@ -328,7 +327,7 @@ class DataCollatorForWav2Vec2Pretraining:
     mask_time_prob: Optional[float] = 0.65
     mask_time_length: Optional[int] = 10
 
-    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+    def __call__(self, features: list[dict[str, Union[list[int], torch.Tensor]]]) -> dict[str, torch.Tensor]:
         # reformat list to dict and set to pytorch format
         batch = self.feature_extractor.pad(
             features,
@@ -716,7 +715,7 @@ def main():
                 }
                 log_str = ""
                 for k, v in train_logs.items():
-                    log_str += "| {}: {:.3e}".format(k, v.item())
+                    log_str += f"| {k}: {v.item():.3e}"
 
                 if accelerator.is_local_main_process:
                     progress_bar.write(log_str)
@@ -773,7 +772,7 @@ def main():
 
         log_str = ""
         for k, v in val_logs.items():
-            log_str += "| {}: {:.3e}".format(k, v.item())
+            log_str += f"| {k}: {v.item():.3e}"
 
         if accelerator.is_local_main_process:
             progress_bar.write(log_str)

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -24,7 +23,7 @@ import re
 import sys
 import warnings
 from dataclasses import dataclass, field
-from typing import Dict, List, Optional, Union
+from typing import Optional, Union
 
 import datasets
 import evaluate
@@ -211,11 +210,11 @@ class DataTrainingArguments:
             )
         },
     )
-    chars_to_ignore: Optional[List[str]] = list_field(
+    chars_to_ignore: Optional[list[str]] = list_field(
         default=None,
         metadata={"help": "A list of characters to remove from the transcripts."},
     )
-    eval_metrics: List[str] = list_field(
+    eval_metrics: list[str] = list_field(
         default=["wer"],
         metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
     )
@@ -318,7 +317,7 @@ class DataCollatorCTCWithPadding:
     pad_to_multiple_of_labels: Optional[int] = None
     feature_extractor_input_name: Optional[str] = "input_values"
 
-    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+    def __call__(self, features: list[dict[str, Union[list[int], torch.Tensor]]]) -> dict[str, torch.Tensor]:
         # split inputs and labels since they have to be of different lengths and need
         # different padding methods
         input_features = [

examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -24,7 +23,7 @@ import re
 import sys
 import warnings
 from dataclasses import dataclass, field
-from typing import Dict, List, Optional, Union
+from typing import Optional, Union
 
 import datasets
 import evaluate
@@ -201,11 +200,11 @@ class DataTrainingArguments:
             )
         },
     )
-    chars_to_ignore: Optional[List[str]] = list_field(
+    chars_to_ignore: Optional[list[str]] = list_field(
         default=None,
         metadata={"help": "A list of characters to remove from the transcripts."},
     )
-    eval_metrics: List[str] = list_field(
+    eval_metrics: list[str] = list_field(
         default=["wer"],
         metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
     )
@@ -300,7 +299,7 @@ class DataCollatorCTCWithPadding:
     pad_to_multiple_of: Optional[int] = None
     pad_to_multiple_of_labels: Optional[int] = None
 
-    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+    def __call__(self, features: list[dict[str, Union[list[int], torch.Tensor]]]) -> dict[str, torch.Tensor]:
         # split inputs and labels since they have to be of different lengths and need
         # different padding methods
         input_features = [{"input_values": feature["input_values"]} for feature in features]

examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -23,7 +22,7 @@ import logging
 import os
 import sys
 from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Optional, Union
 
 import datasets
 import evaluate
@@ -110,11 +109,11 @@ class ModelArguments:
     freeze_encoder: bool = field(
         default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}
     )
-    forced_decoder_ids: List[List[int]] = field(
+    forced_decoder_ids: list[list[int]] = field(
         default=None,
         metadata={"help": "Deprecated. Please use the `language` and `task` arguments instead."},
     )
-    suppress_tokens: List[int] = field(
+    suppress_tokens: list[int] = field(
         default=None,
         metadata={
             "help": (
@@ -247,7 +246,7 @@ class DataCollatorSpeechSeq2SeqWithPadding:
     decoder_start_token_id: int
     forward_attention_mask: bool
 
-    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+    def __call__(self, features: list[dict[str, Union[list[int], torch.Tensor]]]) -> dict[str, torch.Tensor]:
         # split inputs and labels since they have to be of different lengths and need
         # different padding methods
         model_input_name = self.processor.model_input_names[0]

examples/pytorch/summarization/run_summarization.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2021 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/test_accelerate_examples.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2018 HuggingFace Inc..
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -51,7 +50,7 @@ def get_results(output_dir):
     results = {}
     path = os.path.join(output_dir, "all_results.json")
     if os.path.exists(path):
-        with open(path, "r") as f:
+        with open(path) as f:
             results = json.load(f)
     else:
         raise ValueError(f"can't find {path}")

examples/pytorch/test_pytorch_examples.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2018 HuggingFace Inc..
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -87,7 +86,7 @@ def get_results(output_dir):
     results = {}
     path = os.path.join(output_dir, "all_results.json")
     if os.path.exists(path):
-        with open(path, "r") as f:
+        with open(path) as f:
             results = json.load(f)
     else:
         raise ValueError(f"can't find {path}")

examples/pytorch/text-classification/run_classification.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2020 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -21,7 +20,7 @@ import os
 import random
 import sys
 from dataclasses import dataclass, field
-from typing import List, Optional
+from typing import Optional
 
 import datasets
 import evaluate
@@ -256,7 +255,7 @@ class ModelArguments:
     )
 
 
-def get_label_list(raw_dataset, split="train") -> List[str]:
+def get_label_list(raw_dataset, split="train") -> list[str]:
     """Get the list of labels from a multi-label dataset"""
 
     if isinstance(raw_dataset[split]["label"][0], list):
@@ -537,7 +536,7 @@ def main():
         model.config.id2label = {id: label for label, id in label_to_id.items()}
     elif not is_regression:  # classification, but not training
         logger.info("using label infos in the model config")
-        logger.info("label2id: {}".format(model.config.label2id))
+        logger.info(f"label2id: {model.config.label2id}")
         label_to_id = model.config.label2id
     else:  # regression
         label_to_id = None
@@ -549,7 +548,7 @@ def main():
         )
     max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
 
-    def multi_labels_to_ids(labels: List[str]) -> List[float]:
+    def multi_labels_to_ids(labels: list[str]) -> list[float]:
         ids = [0.0] * len(label_to_id)  # BCELoss requires float as target type
         for label in labels:
             ids[label_to_id[label]] = 1.0
@@ -735,7 +734,7 @@ def main():
                     else:
                         item = label_list[item]
                         writer.write(f"{index}\t{item}\n")
-        logger.info("Predict results saved at {}".format(output_predict_file))
+        logger.info(f"Predict results saved at {output_predict_file}")
     kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
 
     if training_args.push_to_hub:

examples/pytorch/text-classification/run_glue.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2020 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/text-classification/run_xnli.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #

examples/pytorch/text-generation/run_generation.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
@@ -19,7 +18,6 @@
 import argparse
 import inspect
 import logging
-from typing import Tuple
 
 import torch
 from accelerate import PartialState
@@ -271,8 +269,8 @@ class _ModelFallbackWrapper(GenerationMixin):
         )
 
     def _reorder_cache(
-        self, past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
+        self, past_key_values: tuple[tuple[torch.Tensor]], beam_idx: torch.Tensor
-    ) -> Tuple[Tuple[torch.Tensor]]:
+    ) -> tuple[tuple[torch.Tensor]]:
         """
         This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
         [`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct

examples/pytorch/text-generation/run_generation_contrastive_search.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2022 University of Cambridge, Tencent AI Lab, DeepMind and The University of Hong Kong Authors and The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/pytorch/token-classification/run_ner.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2020 The HuggingFace Team All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");