Repository: huggingface/transformers
Pull request #31109 (2024-05-29 11:42:17)

Fix Cannot convert [array()] to EagerTensor of dtype int64
While running the model.prepare_tf_dataset() method,
it raises the error below:
```
TypeError: Cannot convert [array([322.,   1.])] to EagerTensor of dtype int64
```

This happens, in  "DataCollatorForSeq2Seq" function when we are try
to convert the labels to tensors. While converting the labels to tensors,
the labels can be in the format of list of list or list of ndarrays.
There is no problem converting the list of list lables. There is a problem
when the list of ndarrays are float values(like below).

```
[array([322.,   1.])]
```

so the exception raises while trying to convert this label to tensors using
below code.

```
batch["labels"] = tf.constant(batch["labels"], dtype=tf.int64)
```

The labels are always integer values, so this got converted to float
values in the label padding operation below.
```
batch["labels"] = [
                    call(label)
                    if padding_side == "right"
                    else np.concatenate([[self.label_pad_token_id] * (max_label_length - len(label)), label])
                    for label in labels
                    ]
```
Here we have 2 cases:
1 - Concatenating an array having integer padding token value with labels.
2 - Concatenating an empty array with labels.

----------------------------------------------------------------------------------------
case 1: Concatenating an array having integer padding token value with labels.
WORKS EXPECTED:
----------------------------------------------------------------------------------------
```
label = np.array([233, 1])
max_label_length = 4
label_pad_token_id = -100
np.concatenate([[label_pad_token_id] * (max_label_length - len(label)), label])
o/p:
array([-100, -100,  233,    1])
```

----------------------------------------------------------------------------------------
Case 2: Concatenating an empty array with labels.
GIVES THE ISSUE:
This scenorio can happen when the label has the maximum label length -- No padding needed.
----------------------------------------------------------------------------------------
```
label = np.array([233, 1])
max_label_length = 2
label_pad_token_id = -100
np.concatenate([[label_pad_token_id] * (max_label_length - len(label)), label])
o/p:
array([233.,   1.])
```

----------------------------------------------------------------------------------------
Solution:
----------------------------------------------------------------------------------------
We need to concatenate a ndarray of dtype int with labels.

AFTER FIX:
----------
case 1:
```

label = np.array([233, 1])
max_label_length = 4
label_pad_token_id = -100
np.concatenate([np.array([label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),label])

o/p:
array([-100, -100,  233,    1])
```

case 2:
```

label = np.array([233, 1])
max_label_length = 2
label_pad_token_id = -100
np.concatenate([np.array([label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),label])

o/p:
array([233,   1])
```
# What does this PR do?

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Fixes # (issue)
[31112](https://github.com/huggingface/transformers/issues/31112)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
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- [ ] Did you write any new necessary tests?


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<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @pacman100

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc and @younesbelkada

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- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


LysandreJik: cc @muellerzr 
pavi-ninjaac: @muellerzr i am done, please check at your own time. 
HuggingFaceDocBuilderDev: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_31109). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Patch:
diff --git a/SECURITY.md b/SECURITY.md
index f5a3acc5a9..fcb8b9b6f1 100644
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -14,7 +14,7 @@ Models uploaded on the Hugging Face Hub come in different formats. We heavily re
 models in the [`safetensors`](https://github.com/huggingface/safetensors) format (which is the default prioritized
 by the transformers library), as developed specifically to prevent arbitrary code execution on your system.
 
-To avoid loading models from unsafe formats(e.g. [pickle](https://docs.python.org/3/library/pickle.html), you should use the `use_safetenstors` parameter. If doing so, in the event that no .safetensors file is present, transformers will error when loading the model.
+To avoid loading models from unsafe formats(e.g. [pickle](https://docs.python.org/3/library/pickle.html), you should use the `use_safetensors` parameter. If doing so, in the event that no .safetensors file is present, transformers will error when loading the model.
 
 ### Remote code
 
diff --git a/examples/research_projects/token-healing/README.md b/examples/research_projects/token-healing/README.md
new file mode 100644
index 0000000000..f3594f32dc
--- /dev/null
+++ b/examples/research_projects/token-healing/README.md
@@ -0,0 +1,40 @@
+<!-- back to top link -->
+<a name="readme-top"></a>
+
+<!-- ABOUT THE PROJECT -->
+## What is token healing?
+
+Token healing rectifies the token boundary bias in greedy tokenization. It does this by trimming and regrowing the prompt to better align with the model's tokenizer, thus enhancing generation quality. The improvement is clearest with completion models.
+
+Example: given a completion prompt with a partial url ending with `:`, the model might have seen the expected completion `://` as a _single_ token in training. However, the prompt's tail token `:` tells it that the next token is not `//`, and so it looks for wrong completions. Such errors compound in auto-regressive language models.
+
+Debiasing token boundaries also addresses output sensitivity to prompts ending with whitespace.
+
+A more thorough explanation can be found on [The Art of Prompt Design: Prompt Boundaries and Token Healing | by Scott Lundberg](https://towardsdatascience.com/the-art-of-prompt-design-prompt-boundaries-and-token-healing-3b2448b0be38).
+
+## Usage
+
+```py
+prompt = 'The link is <a href="http:'
+raw_output = generate(prompt, completion_model, tokenizer, token_healing=False)
+# The link is <a href="http:&#47;&#47;www&#47;dailymail&#
+
+# The model saw '://' as a single token in training. Seeing a prompt ending with `:` tells it that the
+# next token is likely not `//`, because otherwise it would've seen `://`.
+# Thus, it completes with a token other than `//`, in this case, `&`.
+
+healed_output = generate(prompt, completion_model, tokenizer, token_healing=True)
+# The link is <a href="http://www.365doki.com/post/3699
+
+# You can also use token healing in isolation
+# This can be useful if you have other work to do before the generation
+# Or if you want to delegate generation to another process
+input_ids = tokenizer(test_prompts, return_tensors='pt', padding=True).input_ids.cuda()
+healed_ids = model.heal_tokens(input_ids)
+healed_prompts = tokenizer.batch_decode(healed_ids, skip_special_tokens=True)
+# outputs the healed prompts without further completion/generation
+```
+
+See `run_token_healing.py` for the full example.
+
+<p align="right">(<a href="#readme-top">back to top</a>)</p>
\ No newline at end of file
diff --git a/examples/research_projects/token-healing/run_token_healing.py b/examples/research_projects/token-healing/run_token_healing.py
new file mode 100644
index 0000000000..2dd9148c1b
--- /dev/null
+++ b/examples/research_projects/token-healing/run_token_healing.py
@@ -0,0 +1,62 @@
+import argparse
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+
+def generate(inputs, model, tokenizer, token_healing):
+    input_ids = tokenizer(inputs, return_tensors="pt", padding=True, device_map="auto").input_ids
+    generation_config = GenerationConfig(
+        max_new_tokens=8,
+        token_healing=token_healing,
+        pad_token_id=model.config.pad_token_id,
+        repetition_penalty=1.1,
+    )
+    output = model.generate(inputs=input_ids, generation_config=generation_config)
+    return tokenizer.batch_decode(output, skip_special_tokens=True)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--prompt", type=str)
+    parser.add_argument("--model_name_or_path", type=str, default="TheBloke/deepseek-llm-7B-base-GPTQ")
+    args = parser.parse_args()
+
+    prompts = (
+        [args.prompt]
+        if args.prompt
+        else [
+            'An example ["like this"] and another example [',
+            'The link is <a href="http:',
+            'The link is <a href="http',  # test aggressive healing http->https
+            "I read a book about ",  # test trailing whitespace
+            "I read a book about",  # test nothing to heal
+        ]
+    )
+
+    model_name_or_path = args.model_name_or_path
+    completion_model = AutoModelForCausalLM.from_pretrained(
+        model_name_or_path,
+        device_map="auto",
+        use_cache=True,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+
+    raw_output = generate(prompts, completion_model, tokenizer, token_healing=False)
+    healed_output = generate(prompts, completion_model, tokenizer, token_healing=True)
+
+    for p, a, b in zip(prompts, raw_output, healed_output):
+        print(f"\nPrompt: {p}\nWithout healing:\n{a}\nWith healing:\n{b}")
+
+    # You can also use token healing in isolation
+    # This can be useful if you have other work to do before the generation
+    # Or if you want to delegate generation to another process
+    input_ids = tokenizer(prompts, return_tensors="pt", padding=True).input_ids.cuda()
+    healed_ids = completion_model.heal_tokens(input_ids)
+    healed_prompts = tokenizer.batch_decode(healed_ids, skip_special_tokens=True)
+    print("\nhealed prompts:")
+    for p in healed_prompts:
+        print(p)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/transformers/data/data_collator.py b/src/transformers/data/data_collator.py
index aec91c7555..ce17f79ccf 100644
--- a/src/transformers/data/data_collator.py
+++ b/src/transformers/data/data_collator.py
@@ -632,9 +632,19 @@ class DataCollatorForSeq2Seq:
                     ]
                 else:
                     batch["labels"] = [
-                        np.concatenate([label, [self.label_pad_token_id] * (max_label_length - len(label))])
+                        np.concatenate(
+                            [
+                                label,
+                                np.array([self.label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),
+                            ]
+                        )
                         if padding_side == "right"
-                        else np.concatenate([[self.label_pad_token_id] * (max_label_length - len(label)), label])
+                        else np.concatenate(
+                            [
+                                np.array([self.label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),
+                                label,
+                            ]
+                        )
                         for label in labels
                     ]
 
diff --git a/src/transformers/generation/configuration_utils.py b/src/transformers/generation/configuration_utils.py
index 0d1eba0bd5..7dbeb6cce5 100644
--- a/src/transformers/generation/configuration_utils.py
+++ b/src/transformers/generation/configuration_utils.py
@@ -222,6 +222,9 @@ class GenerationConfig(PushToHubMixin):
             Dictionary that maps a sequence of tokens to its bias term. Positive biases increase the odds of the
             sequence being selected, while negative biases do the opposite. Check
             [`~generation.SequenceBiasLogitsProcessor`] for further documentation and examples.
+        token_healing (`bool`, *optional*, defaults to `False`):
+            Heal tail tokens of prompts by replacing them with their appropriate extensions.
+            This enhances the quality of completions for prompts affected by greedy tokenization bias.
         guidance_scale (`float`, *optional*):
             The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale > 1`.
             Higher guidance scale encourages the model to generate samples that are more closely linked to the input
@@ -360,6 +363,7 @@ class GenerationConfig(PushToHubMixin):
         self.begin_suppress_tokens = kwargs.pop("begin_suppress_tokens", None)
         self.forced_decoder_ids = kwargs.pop("forced_decoder_ids", None)
         self.sequence_bias = kwargs.pop("sequence_bias", None)
+        self.token_healing = kwargs.pop("token_healing", False)
         self.guidance_scale = kwargs.pop("guidance_scale", None)
         self.low_memory = kwargs.pop("low_memory", None)
         watermarking_config = kwargs.pop("watermarking_config", None)
diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index 84c9dd995e..074f51b357 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -42,6 +42,7 @@ from ..models.auto import (
     MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
     MODEL_FOR_VISION_2_SEQ_MAPPING,
 )
+from ..tokenization_utils import ExtensionsTrie
 from ..utils import (
     ModelOutput,
     is_accelerate_available,
@@ -1591,6 +1592,8 @@ class GenerationMixin:
             else:
                 synced_gpus = False
 
+        tokenizer = kwargs.pop("tokenizer", None)
+
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
 
@@ -1653,6 +1656,9 @@ class GenerationMixin:
         else:
             input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
 
+        if generation_config.token_healing:
+            input_ids = self.heal_tokens(input_ids, tokenizer)
+
         if streamer is not None:
             streamer.put(input_ids.cpu())
 
@@ -1989,6 +1995,75 @@ class GenerationMixin:
             return False
         return True
 
+    def heal_tokens(
+        self, input_ids: torch.LongTensor, tokenizer: Optional["PreTrainedTokenizerBase"] = None
+    ) -> torch.LongTensor:
+        r"""
+        Generates sequences of token ids for models with a language modeling head.
+        Parameters:
+            input_ids (`torch.LongTensor`): The sequence used as a prompt for the generation.
+            tokenizer (`PreTrainedTokenizerBase`, *optional*): The tokenizer used to decode the input ids.
+        Return:
+            `torch.LongTensor` where each sequence has its tail token replaced with its appropriate extension.
+        """
+        if tokenizer is None:
+            raise ValueError(
+                " When generating with token healing, you must pass the model's tokenizer to the `tokenizer` "
+                "argument of `generate`."
+            )
+
+        bos_token_id, pad_token_id = tokenizer.bos_token_id, tokenizer.pad_token_id
+        vocab_trie = ExtensionsTrie(tokenizer.get_vocab())
+        generation_config = GenerationConfig(max_new_tokens=1, pad_token_id=pad_token_id)
+
+        # assumption: leading/trailing whitespace is not meaningful, so the prompts are
+        # stripped before re-tokenizing to desensitize generation to whitespace artefacts
+        prompts = [p.strip() for p in tokenizer.batch_decode(input_ids, skip_special_tokens=True)]
+        input_ids = tokenizer(
+            prompts,
+            return_tensors="pt",
+            padding=True,
+        ).input_ids.to(input_ids.device)
+
+        # replace bos with pad to not condition healing on it
+        input_ids = torch.where(input_ids == bos_token_id, pad_token_id, input_ids)
+
+        tail_ids = input_ids[:, -1].tolist()
+        space_tok = tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids(" "))[0]
+        # tail tokens are used for a prefix search, thus, whitespaces are replaced with
+        # their tokenization (e.g. 'Ġ') to enable search for tokens prefixed with a whitespace
+        tail_toks = (tokenizer.decode(t).replace(" ", space_tok) for t in tail_ids)
+
+        for batch_idx, (tail_id, tail_tok) in enumerate(zip(tail_ids, tail_toks)):
+            batch_ids = input_ids[batch_idx]
+            if torch.all(batch_ids == pad_token_id).item():
+                continue  # skip empty sequences (all pad ids)
+
+            # apply bias for alternatives (extensions) to the tail token
+            seq_bias = {(alt_tok,): 10.0 for alt_tok in vocab_trie.values(prefix=tail_tok)}
+            if len(seq_bias) == 1:
+                continue  # skip if there are no token alternatives to heal with
+
+            # slightly favor original token to limit aggressive healing e.g. 'http' -> 'https'
+            seq_bias[(tail_id,)] += 1.0
+            generation_config.update(sequence_bias=seq_bias)
+
+            trimmed_ids = batch_ids[:-1]
+            # if the prompt is a single (non-pad) token, regenerate from bos
+            if len(batch_ids[batch_ids != pad_token_id]) == 1:
+                trimmed_ids[-1] = bos_token_id
+
+            input_ids[batch_idx] = self.generate(trimmed_ids.unsqueeze(0), generation_config=generation_config)
+
+        return input_ids
+
+    def contrastive_search(self, *args, **kwargs):
+        logger.warning_once(
+            "Calling `contrastive_search` directly is deprecated and will be removed in v4.41. Use `generate` or a "
+            "custom generation loop instead.",
+        )
+        return self._contrastive_search(*args, **kwargs)
+
     @torch.no_grad()
     def _contrastive_search(
         self,
diff --git a/src/transformers/models/deprecated/deta/image_processing_deta.py b/src/transformers/models/deprecated/deta/image_processing_deta.py
index 57a9584397..9e660198a2 100644
--- a/src/transformers/models/deprecated/deta/image_processing_deta.py
+++ b/src/transformers/models/deprecated/deta/image_processing_deta.py
@@ -78,7 +78,6 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC)
 
 
-# Copied from transformers.models.detr.image_processing_detr.get_size_with_aspect_ratio
 def get_size_with_aspect_ratio(image_size, size, max_size=None) -> Tuple[int, int]:
     """
     Computes the output image size given the input image size and the desired output size.
@@ -110,7 +109,6 @@ def get_size_with_aspect_ratio(image_size, size, max_size=None) -> Tuple[int, in
     return (oh, ow)
 
 
-# Copied from transformers.models.detr.image_processing_detr.get_resize_output_image_size
 def get_resize_output_image_size(
     input_image: np.ndarray,
     size: Union[int, Tuple[int, int], List[int]],
@@ -139,7 +137,6 @@ def get_resize_output_image_size(
     return get_size_with_aspect_ratio(image_size, size, max_size)
 
 
-# Copied from transformers.models.detr.image_processing_detr.get_image_size_for_max_height_width
 def get_image_size_for_max_height_width(
     input_image: np.ndarray,
     max_height: int,
@@ -175,7 +172,6 @@ def get_image_size_for_max_height_width(
     return new_height, new_width
 
 
-# Copied from transformers.models.detr.image_processing_detr.get_numpy_to_framework_fn
 def get_numpy_to_framework_fn(arr) -> Callable:
     """
     Returns a function that converts a numpy array to the framework of the input array.
@@ -200,7 +196,6 @@ def get_numpy_to_framework_fn(arr) -> Callable:
     raise ValueError(f"Cannot convert arrays of type {type(arr)}")
 
 
-# Copied from transformers.models.detr.image_processing_detr.safe_squeeze
 def safe_squeeze(arr: np.ndarray, axis: Optional[int] = None) -> np.ndarray:
     """
     Squeezes an array, but only if the axis specified has dim 1.
@@ -214,7 +209,6 @@ def safe_squeeze(arr: np.ndarray, axis: Optional[int] = None) -> np.ndarray:
         return arr
 
 
-# Copied from transformers.models.detr.image_processing_detr.normalize_annotation
 def normalize_annotation(annotation: Dict, image_size: Tuple[int, int]) -> Dict:
     image_height, image_width = image_size
     norm_annotation = {}
@@ -229,7 +223,6 @@ def normalize_annotation(annotation: Dict, image_size: Tuple[int, int]) -> Dict:
     return norm_annotation
 
 
-# Copied from transformers.models.detr.image_processing_detr.max_across_indices
 def max_across_indices(values: Iterable[Any]) -> List[Any]:
     """
     Return the maximum value across all indices of an iterable of values.
@@ -237,7 +230,6 @@ def max_across_indices(values: Iterable[Any]) -> List[Any]:
     return [max(values_i) for values_i in zip(*values)]
 
 
-# Copied from transformers.models.detr.image_processing_detr.get_max_height_width
 def get_max_height_width(
     images: List[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None
 ) -> List[int]:
@@ -256,7 +248,6 @@ def get_max_height_width(
     return (max_height, max_width)
 
 
-# Copied from transformers.models.detr.image_processing_detr.make_pixel_mask
 def make_pixel_mask(
     image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
 ) -> np.ndarray:
@@ -275,7 +266,6 @@ def make_pixel_mask(
     return mask
 
 
-# Copied from transformers.models.detr.image_processing_detr.convert_coco_poly_to_mask
 def convert_coco_poly_to_mask(segmentations, height: int, width: int) -> np.ndarray:
     """
     Convert a COCO polygon annotation to a mask.
@@ -310,7 +300,6 @@ def convert_coco_poly_to_mask(segmentations, height: int, width: int) -> np.ndar
     return masks
 
 
-# Copied from transformers.models.detr.image_processing_detr.prepare_coco_detection_annotation with DETR->DETA
 def prepare_coco_detection_annotation(
     image,
     target,
@@ -371,7 +360,6 @@ def prepare_coco_detection_annotation(
     return new_target
 
 
-# Copied from transformers.models.detr.image_processing_detr.masks_to_boxes
 def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
     """
     Compute the bounding boxes around the provided panoptic segmentation masks.
@@ -406,7 +394,6 @@ def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
     return np.stack([x_min, y_min, x_max, y_max], 1)
 
 
-# Copied from transformers.models.detr.image_processing_detr.prepare_coco_panoptic_annotation with DETR->DETA
 def prepare_coco_panoptic_annotation(
     image: np.ndarray,
     target: Dict,
@@ -448,7 +435,6 @@ def prepare_coco_panoptic_annotation(
     return new_target
 
 
-# Copied from transformers.models.detr.image_processing_detr.resize_annotation
 def resize_annotation(
     annotation: Dict[str, Any],
     orig_size: Tuple[int, int],
@@ -594,7 +580,6 @@ class DetaImageProcessor(BaseImageProcessor):
         self.do_pad = do_pad
         self.pad_size = pad_size
 
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_annotation with DETR->DETA
     def prepare_annotation(
         self,
         image: np.ndarray,
@@ -683,7 +668,6 @@ class DetaImageProcessor(BaseImageProcessor):
         )
         return image
 
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize_annotation
     def resize_annotation(
         self,
         annotation,
@@ -697,7 +681,6 @@ class DetaImageProcessor(BaseImageProcessor):
         """
         return resize_annotation(annotation, orig_size=orig_size, target_size=size, resample=resample)
 
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.rescale
     def rescale(
         self,
         image: np.ndarray,
@@ -726,7 +709,6 @@ class DetaImageProcessor(BaseImageProcessor):
         """
         return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format)
 
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.normalize_annotation
     def normalize_annotation(self, annotation: Dict, image_size: Tuple[int, int]) -> Dict:
         """
         Normalize the boxes in the annotation from `[top_left_x, top_left_y, bottom_right_x, bottom_right_y]` to
@@ -734,7 +716,6 @@ class DetaImageProcessor(BaseImageProcessor):
         """
         return normalize_annotation(annotation, image_size=image_size)
 
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._update_annotation_for_padded_image
     def _update_annotation_for_padded_image(
         self,
         annotation: Dict,
@@ -778,7 +759,6 @@ class DetaImageProcessor(BaseImageProcessor):
                 new_annotation[key] = value
         return new_annotation
 
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._pad_image
     def _pad_image(
         self,
         image: np.ndarray,
@@ -812,7 +792,6 @@ class DetaImageProcessor(BaseImageProcessor):
             )
         return padded_image, annotation
 
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.pad
     def pad(
         self,
         images: List[np.ndarray],
diff --git a/src/transformers/models/deprecated/deta/modeling_deta.py b/src/transformers/models/deprecated/deta/modeling_deta.py
index 03341f0ab8..bc19574939 100644
--- a/src/transformers/models/deprecated/deta/modeling_deta.py
+++ b/src/transformers/models/deprecated/deta/modeling_deta.py
@@ -52,7 +52,6 @@ logger = logging.get_logger(__name__)
 MultiScaleDeformableAttention = None
 
 
-# Copied from models.deformable_detr.load_cuda_kernels
 def load_cuda_kernels():
     from torch.utils.cpp_extension import load
 
@@ -83,7 +82,6 @@ def load_cuda_kernels():
     )
 
 
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.MultiScaleDeformableAttentionFunction
 class MultiScaleDeformableAttentionFunction(Function):
     @staticmethod
     def forward(
@@ -152,7 +150,6 @@ _CHECKPOINT_FOR_DOC = "jozhang97/deta-swin-large-o365"
 
 
 @dataclass
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrDecoderOutput with DeformableDetr->Deta
 class DetaDecoderOutput(ModelOutput):
     """
     Base class for outputs of the DetaDecoder. This class adds two attributes to
@@ -344,7 +341,6 @@ def inverse_sigmoid(x, eps=1e-5):
     return torch.log(x1 / x2)
 
 
-# Copied from transformers.models.detr.modeling_detr.DetrFrozenBatchNorm2d with Detr->Deta
 class DetaFrozenBatchNorm2d(nn.Module):
     """
     BatchNorm2d where the batch statistics and the affine parameters are fixed.
@@ -384,7 +380,6 @@ class DetaFrozenBatchNorm2d(nn.Module):
         return x * scale + bias
 
 
-# Copied from transformers.models.detr.modeling_detr.replace_batch_norm with Detr->Deta
 def replace_batch_norm(model):
     r"""
     Recursively replace all `torch.nn.BatchNorm2d` with `DetaFrozenBatchNorm2d`.
@@ -454,7 +449,6 @@ class DetaBackboneWithPositionalEncodings(nn.Module):
         return out, pos
 
 
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrSinePositionEmbedding with DeformableDetr->Deta
 class DetaSinePositionEmbedding(nn.Module):
     """
     This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
@@ -493,7 +487,6 @@ class DetaSinePositionEmbedding(nn.Module):
         return pos
 
 
-# Copied from transformers.models.detr.modeling_detr.DetrLearnedPositionEmbedding
 class DetaLearnedPositionEmbedding(nn.Module):
     """
     This module learns positional embeddings up to a fixed maximum size.
@@ -517,7 +510,6 @@ class DetaLearnedPositionEmbedding(nn.Module):
         return pos
 
 
-# Copied from transformers.models.detr.modeling_detr.build_position_encoding with Detr->Deta
 def build_position_encoding(config):
     n_steps = config.d_model // 2
     if config.position_embedding_type == "sine":
@@ -531,7 +523,6 @@ def build_position_encoding(config):
     return position_embedding
 
 
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.multi_scale_deformable_attention
 def multi_scale_deformable_attention(
     value: Tensor, value_spatial_shapes: Tensor, sampling_locations: Tensor, attention_weights: Tensor
 ) -> Tensor:
@@ -571,7 +562,6 @@ def multi_scale_deformable_attention(
     return output.transpose(1, 2).contiguous()
 
 
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrMultiscaleDeformableAttention with DeformableDetr->Deta
 class DetaMultiscaleDeformableAttention(nn.Module):
     """
     Multiscale deformable attention as proposed in Deformable DETR.
@@ -715,7 +705,6 @@ class DetaMultiscaleDeformableAttention(nn.Module):
         return output, attention_weights
 
 
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrMultiheadAttention with DeformableDetr->Deta,Deformable DETR->DETA
 class DetaMultiheadAttention(nn.Module):
     """
     Multi-headed attention from 'Attention Is All You Need' paper.
@@ -1506,11 +1495,9 @@ class DetaModel(DetaPreTrainedModel):
 
         self.post_init()
 
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.get_encoder
     def get_encoder(self):
         return self.encoder
 
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.get_decoder
     def get_decoder(self):
         return self.decoder
 
@@ -1522,7 +1509,6 @@ class DetaModel(DetaPreTrainedModel):
         for name, param in self.backbone.model.named_parameters():
             param.requires_grad_(True)
 
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.get_valid_ratio
     def get_valid_ratio(self, mask, dtype=torch.float32):
         """Get the valid ratio of all feature maps."""
 
@@ -1534,7 +1520,6 @@ class DetaModel(DetaPreTrainedModel):
         valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1)
         return valid_ratio
 
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.get_proposal_pos_embed
     def get_proposal_pos_embed(self, proposals):
         """Get the position embedding of the proposals."""
 
@@ -1869,7 +1854,6 @@ class DetaForObjectDetection(DetaPreTrainedModel):
     # We can't initialize the model on meta device as some weights are modified during the initialization
     _no_split_modules = None
 
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrForObjectDetection.__init__ with DeformableDetr->Deta
     def __init__(self, config: DetaConfig):
         super().__init__(config)
 
@@ -2105,7 +2089,6 @@ class DetaForObjectDetection(DetaPreTrainedModel):
         return dict_outputs
 
 
-# Copied from transformers.models.detr.modeling_detr.dice_loss
 def dice_loss(inputs, targets, num_boxes):
     """
     Compute the DICE loss, similar to generalized IOU for masks
@@ -2125,7 +2108,6 @@ def dice_loss(inputs, targets, num_boxes):
     return loss.sum() / num_boxes
 
 
-# Copied from transformers.models.detr.modeling_detr.sigmoid_focal_loss
 def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2):
     """
     Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
@@ -2197,7 +2179,6 @@ class DetaLoss(nn.Module):
         if self.assign_second_stage:
             self.stg2_assigner = DetaStage2Assigner(num_queries)
 
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_labels
     def loss_labels(self, outputs, targets, indices, num_boxes):
         """
         Classification loss (Binary focal loss) targets dicts must contain the key "class_labels" containing a tensor
@@ -2232,7 +2213,6 @@ class DetaLoss(nn.Module):
         return losses
 
     @torch.no_grad()
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_cardinality
     def loss_cardinality(self, outputs, targets, indices, num_boxes):
         """
         Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes.
@@ -2248,7 +2228,6 @@ class DetaLoss(nn.Module):
         losses = {"cardinality_error": card_err}
         return losses
 
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_boxes
     def loss_boxes(self, outputs, targets, indices, num_boxes):
         """
         Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss.
@@ -2273,21 +2252,18 @@ class DetaLoss(nn.Module):
         losses["loss_giou"] = loss_giou.sum() / num_boxes
         return losses
 
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss._get_source_permutation_idx
     def _get_source_permutation_idx(self, indices):
         # permute predictions following indices
         batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)])
         source_idx = torch.cat([source for (source, _) in indices])
         return batch_idx, source_idx
 
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss._get_target_permutation_idx
     def _get_target_permutation_idx(self, indices):
         # permute targets following indices
         batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)])
         target_idx = torch.cat([target for (_, target) in indices])
         return batch_idx, target_idx
 
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.get_loss
     def get_loss(self, loss, outputs, targets, indices, num_boxes):
         loss_map = {
             "labels": self.loss_labels,
@@ -2360,7 +2336,6 @@ class DetaLoss(nn.Module):
         return losses
 
 
-# Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead
 class DetaMLPPredictionHead(nn.Module):
     """
     Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
@@ -2382,7 +2357,6 @@ class DetaMLPPredictionHead(nn.Module):
         return x
 
 
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrHungarianMatcher with DeformableDetr->Deta
 class DetaHungarianMatcher(nn.Module):
     """
     This class computes an assignment between the targets and the predictions of the network.
@@ -2463,7 +2437,6 @@ class DetaHungarianMatcher(nn.Module):
         return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
 
 
-# Copied from transformers.models.detr.modeling_detr._upcast
 def _upcast(t: Tensor) -> Tensor:
     # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
     if t.is_floating_point():
@@ -2472,7 +2445,6 @@ def _upcast(t: Tensor) -> Tensor:
         return t if t.dtype in (torch.int32, torch.int64) else t.int()
 
 
-# Copied from transformers.models.detr.modeling_detr.box_area
 def box_area(boxes: Tensor) -> Tensor:
     """
     Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
@@ -2489,7 +2461,6 @@ def box_area(boxes: Tensor) -> Tensor:
     return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
 
 
-# Copied from transformers.models.detr.modeling_detr.box_iou
 def box_iou(boxes1, boxes2):
     area1 = box_area(boxes1)
     area2 = box_area(boxes2)
@@ -2506,7 +2477,6 @@ def box_iou(boxes1, boxes2):
     return iou, union
 
 
-# Copied from transformers.models.detr.modeling_detr.generalized_box_iou
 def generalized_box_iou(boxes1, boxes2):
     """
     Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format.
diff --git a/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py b/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py
index 461490c7f5..306790021a 100644
--- a/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py
+++ b/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py
@@ -239,7 +239,6 @@ class EfficientFormerConvMlp(nn.Module):
         return hidden_state
 
 
-# Copied from transformers.models.convnext.modeling_convnext.drop_path
 def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
     """
     Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
@@ -260,7 +259,6 @@ def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = Fals
     return output
 
 
-# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->EfficientFormer
 class EfficientFormerDropPath(nn.Module):
     """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
 
diff --git a/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py b/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py
index d8349ee5aa..68d270874c 100755
--- a/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py
+++ b/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py
@@ -86,7 +86,6 @@ class ErnieMEmbeddings(nn.Module):
         return embeddings
 
 
-# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->ErnieM,self.value->self.v_proj,self.key->self.k_proj,self.query->self.q_proj
 class ErnieMSelfAttention(nn.Module):
     def __init__(self, config, position_embedding_type=None):
         super().__init__()
@@ -380,7 +379,6 @@ class ErnieMEncoder(nn.Module):
         )
 
 
-# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->ErnieM
 class ErnieMPooler(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -599,7 +597,6 @@ class ErnieMModel(ErnieMPreTrainedModel):
     ERNIE_M_START_DOCSTRING,
 )
 class ErnieMForSequenceClassification(ErnieMPreTrainedModel):
-    # Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with Bert->ErnieM,bert->ernie_m
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
@@ -701,7 +698,6 @@ class ErnieMForSequenceClassification(ErnieMPreTrainedModel):
     ERNIE_M_START_DOCSTRING,
 )
 class ErnieMForMultipleChoice(ErnieMPreTrainedModel):
-    # Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice.__init__ with Bert->ErnieM,bert->ernie_m
     def __init__(self, config):
         super().__init__(config)
 
@@ -791,7 +787,6 @@ class ErnieMForMultipleChoice(ErnieMPreTrainedModel):
     ERNIE_M_START_DOCSTRING,
 )
 class ErnieMForTokenClassification(ErnieMPreTrainedModel):
-    # Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with Bert->ErnieM,bert->ernie_m
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
@@ -872,7 +867,6 @@ class ErnieMForTokenClassification(ErnieMPreTrainedModel):
     ERNIE_M_START_DOCSTRING,
 )
 class ErnieMForQuestionAnswering(ErnieMPreTrainedModel):
-    # Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with Bert->ErnieM,bert->ernie_m
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
@@ -968,7 +962,6 @@ class ErnieMForQuestionAnswering(ErnieMPreTrainedModel):
     compute `start_prob` and `end_prob`, designed for Universal Information Extraction.""",
     ERNIE_M_START_DOCSTRING,
 )
-# Copied from paddlenlp.transformers.ernie_m.modeling.UIEM
 class ErnieMForInformationExtraction(ErnieMPreTrainedModel):
     def __init__(self, config):
         super(ErnieMForInformationExtraction, self).__init__(config)
diff --git a/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py b/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py
index 5129c1091b..c7a195dbea 100644
--- a/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py
+++ b/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py
@@ -45,7 +45,6 @@ _CHECKPOINT_FOR_DOC = "Tanrei/GPTSAN-japanese"
 ####################################################
 
 
-# Copied from transformers.models.switch_transformers.modeling_switch_transformers.router_z_loss_func
 def router_z_loss_func(router_logits: torch.Tensor) -> float:
     r"""
     Compute the router z-loss implemented in PyTorch.
@@ -66,7 +65,6 @@ def router_z_loss_func(router_logits: torch.Tensor) -> float:
     return torch.sum(z_loss) / (num_groups * tokens_per_group)
 
 
-# Copied from transformers.models.switch_transformers.modeling_switch_transformers.load_balancing_loss_func
 def load_balancing_loss_func(router_probs: torch.Tensor, expert_indices: torch.Tensor) -> float:
     r"""
     Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
@@ -140,7 +138,6 @@ class GPTSanJapaneseDenseActDense(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersTop1Router with SwitchTransformers->GPTSanJapanese
 class GPTSanJapaneseTop1Router(nn.Module):
     """
     Router using tokens choose top-1 experts assignment.
@@ -234,7 +231,6 @@ class GPTSanJapaneseTop1Router(nn.Module):
         return expert_index, router_probs, router_logits
 
 
-# Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersSparseMLP with SwitchTransformers->GPTSanJapanese
 class GPTSanJapaneseSparseMLP(nn.Module):
     r"""
     Implementation of the Switch Transformers Sparse MLP module.
@@ -345,7 +341,6 @@ class GPTSanJapaneseLayerDenseFF(nn.Module):
         return output
 
 
-# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->GPTSanJapanese
 class GPTSanJapaneseAttention(nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper"""
 
@@ -749,7 +744,6 @@ class GPTSanJapanesePreTrainedModel(PreTrainedModel):
                 module.experts[f"expert_{idx}"].wi.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
                 module.experts[f"expert_{idx}"].wo.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
 
-    # Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel._shift_right
     def _shift_right(self, input_ids):
         decoder_start_token_id = self.config.decoder_start_token_id
         pad_token_id = self.config.pad_token_id
@@ -1298,17 +1292,14 @@ class GPTSanJapaneseForConditionalGeneration(GPTSanJapanesePreTrainedModel):
             "past_key_values": None,
         }
 
-    # Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersForConditionalGeneration.prepare_decoder_input_ids_from_labels with SwitchTransformers->GPTSanJapanese
     def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
         return self._shift_right(labels)
 
-    # Copied from transformers.models.mbart.modeling_mbart.MBartForConditionalGeneration.resize_token_embeddings with MBart->GPTSanJapanese
     def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
         new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
 
-    # Copied from transformers.models.mbart.modeling_mbart.MBartForConditionalGeneration._resize_final_logits_bias with MBart->GPTSanJapanese
     def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
         old_num_tokens = self.final_logits_bias.shape[-1]
         if new_num_tokens <= old_num_tokens:
@@ -1324,15 +1315,12 @@ class GPTSanJapaneseForConditionalGeneration(GPTSanJapanesePreTrainedModel):
     def set_input_embeddings(self, new_embeddings):
         self.model.set_input_embeddings(new_embeddings)
 
-    # Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersForConditionalGeneration.set_output_embeddings with SwitchTransformers->GPTSanJapanese
     def set_output_embeddings(self, new_embeddings):
         self.lm_head = new_embeddings
 
-    # Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersForConditionalGeneration.get_output_embeddings with SwitchTransformers->GPTSanJapanese
     def get_output_embeddings(self):
         return self.lm_head
 
-    # Copied from transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersForConditionalGeneration._unpack_router_logits with SwitchTransformers->GPTSanJapanese
     def _unpack_router_logits(self, router_outputs):
         total_router_logits = []
         total_expert_indexes = []
diff --git a/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py b/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py
index e86aa47c1a..51789e49b2 100644
--- a/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py
+++ b/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py
@@ -179,25 +179,20 @@ class GPTSanJapaneseTokenizer(PreTrainedTokenizer):
         )
 
     @property
-    # Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer.vocab_size
     def vocab_size(self):
         # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
         return len(self.raw_vocab)
 
-    # Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer.get_vocab
     def get_vocab(self):
         return dict(self.raw_vocab, **self.added_tokens_encoder)
 
-    # Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer._tokenize
     def _tokenize(self, text):
         return self.subword_tokenizer.tokenize(text, clean=self.do_clean_text)
 
-    # Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer._convert_token_to_id
     def _convert_token_to_id(self, token):
         """Converts a token (str) in an id using the vocab."""
         return self.vocab.get(token, self.vocab.get(self.unk_token))
 
-    # Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer._convert_id_to_token
     def _convert_id_to_token(self, index):
         """Converts an index (integer) in a token (str) using the vocab."""
         return self.subword_tokenizer.convert_id_to_token(index)
@@ -254,7 +249,6 @@ class GPTSanJapaneseTokenizer(PreTrainedTokenizer):
             "{% endfor %}"
         )
 
-    # Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer.save_vocabulary
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         index = 0
         if os.path.isdir(save_directory):
@@ -412,7 +406,6 @@ class SubWordJapaneseTokenizer(object):
     SOFTWARE.
     """
 
-    # Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.__init__
     def __init__(self, vocab, ids_to_tokens, emoji):
         self.vocab = vocab  # same as swe
         self.ids_to_tokens = ids_to_tokens  # same as bpe
@@ -434,11 +427,9 @@ class SubWordJapaneseTokenizer(object):
         blocks = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
         self.content_trans1 = str.maketrans({k: "<BLOCK>" for k in keisen + blocks})
 
-    # Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.__len__
     def __len__(self):
         return len(self.ids_to_tokens)
 
-    # Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.clean_text
     def clean_text(self, content):
         content = self.content_repatter1.sub("<URL>", content)
         content = self.content_repatter2.sub("<EMAIL>", content)
@@ -451,7 +442,6 @@ class SubWordJapaneseTokenizer(object):
             content = content.replace("<BLOCK><BLOCK>", "<BLOCK>")
         return content
 
-    # Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.tokenize
     def tokenize(self, text, clean=False):
         text = text.replace(" ", "<SP>")
         text = text.replace("　", "<SP>")
diff --git a/src/transformers/models/deprecated/nat/modeling_nat.py b/src/transformers/models/deprecated/nat/modeling_nat.py
index 58d92ada0b..b3827f3787 100644
--- a/src/transformers/models/deprecated/nat/modeling_nat.py
+++ b/src/transformers/models/deprecated/nat/modeling_nat.py
@@ -256,7 +256,6 @@ class NatDownsampler(nn.Module):
         return input_feature
 
 
-# Copied from transformers.models.beit.modeling_beit.drop_path
 def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
     """
     Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
@@ -277,7 +276,6 @@ def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = Fals
     return output
 
 
-# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Nat
 class NatDropPath(nn.Module):
     """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
 
diff --git a/src/transformers/models/deprecated/nezha/modeling_nezha.py b/src/transformers/models/deprecated/nezha/modeling_nezha.py
index ef20396c00..3346a4f835 100644
--- a/src/transformers/models/deprecated/nezha/modeling_nezha.py
+++ b/src/transformers/models/deprecated/nezha/modeling_nezha.py
@@ -346,7 +346,6 @@ class NezhaSelfAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->Nezha
 class NezhaSelfOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -410,7 +409,6 @@ class NezhaAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Nezha
 class NezhaIntermediate(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -426,7 +424,6 @@ class NezhaIntermediate(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Nezha
 class NezhaOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -527,7 +524,6 @@ class NezhaLayer(nn.Module):
         return layer_output
 
 
-# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Nezha
 class NezhaEncoder(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -621,7 +617,6 @@ class NezhaEncoder(nn.Module):
         )
 
 
-# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->Nezha
 class NezhaPooler(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -637,7 +632,6 @@ class NezhaPooler(nn.Module):
         return pooled_output
 
 
-# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->Nezha
 class NezhaPredictionHeadTransform(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -655,7 +649,6 @@ class NezhaPredictionHeadTransform(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Nezha
 class NezhaLMPredictionHead(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -679,7 +672,6 @@ class NezhaLMPredictionHead(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Nezha
 class NezhaOnlyMLMHead(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -690,7 +682,6 @@ class NezhaOnlyMLMHead(nn.Module):
         return prediction_scores
 
 
-# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->Nezha
 class NezhaOnlyNSPHead(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -701,7 +692,6 @@ class NezhaOnlyNSPHead(nn.Module):
         return seq_relationship_score
 
 
-# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->Nezha
 class NezhaPreTrainingHeads(nn.Module):
     def __init__(self, config):
         super().__init__()
diff --git a/src/transformers/models/deprecated/open_llama/configuration_open_llama.py b/src/transformers/models/deprecated/open_llama/configuration_open_llama.py
index 259fd19367..e20c33f24a 100644
--- a/src/transformers/models/deprecated/open_llama/configuration_open_llama.py
+++ b/src/transformers/models/deprecated/open_llama/configuration_open_llama.py
@@ -145,7 +145,6 @@ class OpenLlamaConfig(PretrainedConfig):
             **kwargs,
         )
 
-    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
     def _rope_scaling_validation(self):
         """
         Validate the `rope_scaling` configuration.
diff --git a/src/transformers/models/deprecated/open_llama/modeling_open_llama.py b/src/transformers/models/deprecated/open_llama/modeling_open_llama.py
index e748529c9e..7d2098f2f6 100644
--- a/src/transformers/models/deprecated/open_llama/modeling_open_llama.py
+++ b/src/transformers/models/deprecated/open_llama/modeling_open_llama.py
@@ -46,7 +46,6 @@ except ImportError:
 _CONFIG_FOR_DOC = "OpenLlamaConfig"
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->OpenLlama
 class OpenLlamaRMSNorm(nn.Module):
     def __init__(self, hidden_size, eps=1e-6):
         """
@@ -64,7 +63,6 @@ class OpenLlamaRMSNorm(nn.Module):
         return self.weight * hidden_states.to(input_dtype)
 
 
-# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->OpenLlama
 class OpenLlamaRotaryEmbedding(nn.Module):
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
@@ -101,7 +99,6 @@ class OpenLlamaRotaryEmbedding(nn.Module):
         )
 
 
-# Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->OpenLlama
 class OpenLlamaLinearScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
     """OpenLlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
 
@@ -121,7 +118,6 @@ class OpenLlamaLinearScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
 
 
-# Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->OpenLlama
 class OpenLlamaDynamicNTKScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
     """OpenLlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
 
@@ -155,7 +151,6 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-# Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
 
@@ -228,7 +223,6 @@ class OpenLlamaAttention(nn.Module):
         self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
         self._init_rope()
 
-    # Copied from transformers.models.llama.modeling_llama.LlamaAttention._init_rope with Llama->OpenLlama
     def _init_rope(self):
         if self.config.rope_scaling is None:
             self.rotary_emb = OpenLlamaRotaryEmbedding(
diff --git a/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py b/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py
index f58c9b7fd6..036ca99c73 100755
--- a/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py
+++ b/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py
@@ -142,7 +142,6 @@ def load_tf_weights_in_qdqbert(model, tf_checkpoint_path):
     return model
 
 
-# Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert -> QDQBert
 class QDQBertEmbeddings(nn.Module):
     """Construct the embeddings from word, position and token_type embeddings."""
 
@@ -628,7 +627,6 @@ class QDQBertEncoder(nn.Module):
         )
 
 
-# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert -> QDQBert
 class QDQBertPooler(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -644,7 +642,6 @@ class QDQBertPooler(nn.Module):
         return pooled_output
 
 
-# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert -> QDQBert
 class QDQBertPredictionHeadTransform(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -697,7 +694,6 @@ class QDQBertOnlyMLMHead(nn.Module):
         return prediction_scores
 
 
-# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert -> QDQBert
 class QDQBertOnlyNSPHead(nn.Module):
     def __init__(self, config):
         super().__init__()
diff --git a/src/transformers/models/deprecated/realm/modeling_realm.py b/src/transformers/models/deprecated/realm/modeling_realm.py
index f41eafe184..605f204fb5 100644
--- a/src/transformers/models/deprecated/realm/modeling_realm.py
+++ b/src/transformers/models/deprecated/realm/modeling_realm.py
@@ -150,7 +150,6 @@ def load_tf_weights_in_realm(model, config, tf_checkpoint_path):
     return model
 
 
-# Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert->Realm
 class RealmEmbeddings(nn.Module):
     """Construct the embeddings from word, position and token_type embeddings."""
 
@@ -215,7 +214,6 @@ class RealmEmbeddings(nn.Module):
         return embeddings
 
 
-# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Realm
 class RealmSelfAttention(nn.Module):
     def __init__(self, config, position_embedding_type=None):
         super().__init__()
@@ -350,7 +348,6 @@ class RealmSelfAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->Realm
 class RealmSelfOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -370,7 +367,6 @@ REALM_SELF_ATTENTION_CLASSES = {
 }
 
 
-# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Realm,BERT->REALM
 class RealmAttention(nn.Module):
     def __init__(self, config, position_embedding_type=None):
         super().__init__()
@@ -422,7 +418,6 @@ class RealmAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Realm
 class RealmIntermediate(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -438,7 +433,6 @@ class RealmIntermediate(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Realm
 class RealmOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -453,7 +447,6 @@ class RealmOutput(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Realm
 class RealmLayer(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -540,7 +533,6 @@ class RealmLayer(nn.Module):
         return layer_output
 
 
-# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Realm
 class RealmEncoder(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -634,7 +626,6 @@ class RealmEncoder(nn.Module):
         )
 
 
-# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->Realm
 class RealmPooler(nn.Module):
     def __init__(self, config):
         super().__init__()
diff --git a/src/transformers/models/deprecated/retribert/tokenization_retribert.py b/src/transformers/models/deprecated/retribert/tokenization_retribert.py
index c991f39722..2f66fcc1ed 100644
--- a/src/transformers/models/deprecated/retribert/tokenization_retribert.py
+++ b/src/transformers/models/deprecated/retribert/tokenization_retribert.py
@@ -28,7 +28,6 @@ logger = logging.get_logger(__name__)
 VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
 
 
-# Copied from transformers.models.bert.tokenization_bert.load_vocab
 def load_vocab(vocab_file):
     """Loads a vocabulary file into a dictionary."""
     vocab = collections.OrderedDict()
@@ -40,7 +39,6 @@ def load_vocab(vocab_file):
     return vocab
 
 
-# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
 def whitespace_tokenize(text):
     """Runs basic whitespace cleaning and splitting on a piece of text."""
     text = text.strip()
@@ -96,7 +94,6 @@ class RetriBertTokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     model_input_names = ["input_ids", "attention_mask"]
 
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.__init__
     def __init__(
         self,
         vocab_file,
@@ -145,20 +142,16 @@ class RetriBertTokenizer(PreTrainedTokenizer):
         )
 
     @property
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.do_lower_case
     def do_lower_case(self):
         return self.basic_tokenizer.do_lower_case
 
     @property
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.vocab_size
     def vocab_size(self):
         return len(self.vocab)
 
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.get_vocab
     def get_vocab(self):
         return dict(self.vocab, **self.added_tokens_encoder)
 
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer._tokenize
     def _tokenize(self, text, split_special_tokens=False):
         split_tokens = []
         if self.do_basic_tokenize:
@@ -174,23 +167,19 @@ class RetriBertTokenizer(PreTrainedTokenizer):
             split_tokens = self.wordpiece_tokenizer.tokenize(text)
         return split_tokens
 
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer._convert_token_to_id
     def _convert_token_to_id(self, token):
         """Converts a token (str) in an id using the vocab."""
         return self.vocab.get(token, self.vocab.get(self.unk_token))
 
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer._convert_id_to_token
     def _convert_id_to_token(self, index):
         """Converts an index (integer) in a token (str) using the vocab."""
         return self.ids_to_tokens.get(index, self.unk_token)
 
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.convert_tokens_to_string
     def convert_tokens_to_string(self, tokens):
         """Converts a sequence of tokens (string) in a single string."""
         out_string = " ".join(tokens).replace(" ##", "").strip()
         return out_string
 
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.build_inputs_with_special_tokens
     def build_inputs_with_special_tokens(
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
     ) -> List[int]:
@@ -216,7 +205,6 @@ class RetriBertTokenizer(PreTrainedTokenizer):
         sep = [self.sep_token_id]
         return cls + token_ids_0 + sep + token_ids_1 + sep
 
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.get_special_tokens_mask
     def get_special_tokens_mask(
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
     ) -> List[int]:
@@ -245,7 +233,6 @@ class RetriBertTokenizer(PreTrainedTokenizer):
             return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
         return [1] + ([0] * len(token_ids_0)) + [1]
 
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.create_token_type_ids_from_sequences
     def create_token_type_ids_from_sequences(
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
     ) -> List[int]:
@@ -275,7 +262,6 @@ class RetriBertTokenizer(PreTrainedTokenizer):
             return len(cls + token_ids_0 + sep) * [0]
         return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
 
-    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.save_vocabulary
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         index = 0
         if os.path.isdir(save_directory):
@@ -297,7 +283,6 @@ class RetriBertTokenizer(PreTrainedTokenizer):
         return (vocab_file,)
 
 
-# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
 class BasicTokenizer(object):
     """
     Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
@@ -459,7 +444,6 @@ class BasicTokenizer(object):
         return "".join(output)
 
 
-# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
 class WordpieceTokenizer(object):
     """Runs WordPiece tokenization."""
 
diff --git a/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py b/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py
index 97fbfc07d3..9a915d1597 100644
--- a/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py
+++ b/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py
@@ -76,7 +76,6 @@ class RetriBertTokenizerFast(PreTrainedTokenizerFast):
     slow_tokenizer_class = RetriBertTokenizer
     model_input_names = ["input_ids", "attention_mask"]
 
-    # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.__init__
     def __init__(
         self,
         vocab_file=None,
@@ -119,7 +118,6 @@ class RetriBertTokenizerFast(PreTrainedTokenizerFast):
 
         self.do_lower_case = do_lower_case
 
-    # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.build_inputs_with_special_tokens
     def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
         """
         Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
@@ -144,7 +142,6 @@ class RetriBertTokenizerFast(PreTrainedTokenizerFast):
 
         return output
 
-    # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.create_token_type_ids_from_sequences
     def create_token_type_ids_from_sequences(
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
     ) -> List[int]:
@@ -174,7 +171,6 @@ class RetriBertTokenizerFast(PreTrainedTokenizerFast):
             return len(cls + token_ids_0 + sep) * [0]
         return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
 
-    # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.save_vocabulary
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         files = self._tokenizer.model.save(save_directory, name=filename_prefix)
         return tuple(files)
diff --git a/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py b/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py
index 6953821648..8f1a837093 100755
--- a/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py
+++ b/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py
@@ -36,7 +36,6 @@ _CONFIG_FOR_DOC = "Speech2Text2Config"
 _CHECKPOINT_FOR_DOC = "facebook/s2t-wav2vec2-large-en-de"
 
 
-# Copied from transformers.models.speech_to_text.modeling_speech_to_text.Speech2TextSinusoidalPositionalEmbedding with Speech2Text->Speech2Text2
 class Speech2Text2SinusoidalPositionalEmbedding(nn.Module):
     """This module produces sinusoidal positional embeddings of any length."""
 
@@ -107,7 +106,6 @@ class Speech2Text2SinusoidalPositionalEmbedding(nn.Module):
         return incremental_indices.long() + padding_idx
 
 
-# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Speech2Text2
 class Speech2Text2Attention(nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper"""
 
diff --git a/src/transformers/models/deprecated/tvlt/modeling_tvlt.py b/src/transformers/models/deprecated/tvlt/modeling_tvlt.py
index ae84a7df19..7f82aacf6e 100644
--- a/src/transformers/models/deprecated/tvlt/modeling_tvlt.py
+++ b/src/transformers/models/deprecated/tvlt/modeling_tvlt.py
@@ -340,7 +340,6 @@ class TvltAudioPatchEmbeddings(nn.Module):
         return embeddings
 
 
-# Copied from transformers.models.vilt.modeling_vilt.ViltSelfAttention with Vilt->Tvlt
 class TvltSelfAttention(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -401,7 +400,6 @@ class TvltSelfAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.vilt.modeling_vilt.ViltSelfOutput with Vilt->Tvlt
 class TvltSelfOutput(nn.Module):
     """
     The residual connection is defined in TvltLayer instead of here (as is the case with other models), due to the
@@ -420,7 +418,6 @@ class TvltSelfOutput(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.vilt.modeling_vilt.ViltAttention with Vilt->Tvlt
 class TvltAttention(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -455,7 +452,6 @@ class TvltAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.vilt.modeling_vilt.ViltIntermediate with Vilt->Tvlt
 class TvltIntermediate(nn.Module):
     def __init__(self, config: TvltConfig) -> None:
         super().__init__()
@@ -472,7 +468,6 @@ class TvltIntermediate(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.vilt.modeling_vilt.ViltOutput with Vilt->Tvlt
 class TvltOutput(nn.Module):
     def __init__(self, config: TvltConfig) -> None:
         super().__init__()
@@ -488,7 +483,6 @@ class TvltOutput(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.vilt.modeling_vilt.ViltLayer with Vilt->Tvlt
 class TvltLayer(nn.Module):
     """This corresponds to the Block class in the timm implementation."""
 
@@ -527,7 +521,6 @@ class TvltLayer(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.vilt.modeling_vilt.ViltEncoder with Vilt->Tvlt
 class TvltEncoder(nn.Module):
     def __init__(self, config):
         super().__init__()
diff --git a/src/transformers/models/deprecated/van/modeling_van.py b/src/transformers/models/deprecated/van/modeling_van.py
index 1b26d8892b..440881c751 100644
--- a/src/transformers/models/deprecated/van/modeling_van.py
+++ b/src/transformers/models/deprecated/van/modeling_van.py
@@ -48,7 +48,6 @@ _IMAGE_CLASS_CHECKPOINT = "Visual-Attention-Network/van-base"
 _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
 
 
-# Copied from transformers.models.convnext.modeling_convnext.drop_path
 def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
     """
     Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
@@ -69,7 +68,6 @@ def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = Fals
     return output
 
 
-# Copied from transformers.models.convnext.modeling_convnext.ConvNextDropPath with ConvNext->Van
 class VanDropPath(nn.Module):
     """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
 
diff --git a/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py b/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py
index b8db4a7fae..89a8f9e676 100644
--- a/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py
+++ b/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py
@@ -140,7 +140,6 @@ class ViTHybridImageProcessor(BaseImageProcessor):
             "input_data_format",
         ]
 
-    # Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize
     def resize(
         self,
         image: np.ndarray,
diff --git a/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py b/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py
index 9c025d3615..a1f34bfe40 100644
--- a/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py
+++ b/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py
@@ -51,7 +51,6 @@ class ViTHybridEmbeddings(nn.Module):
     Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
     """
 
-    # Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.__init__ with ViT->ViTHybrid
     def __init__(self, config: ViTHybridConfig, use_mask_token: bool = False) -> None:
         super().__init__()
 
@@ -186,7 +185,6 @@ class ViTHybridPatchEmbeddings(nn.Module):
         return embeddings
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->ViTHybrid
 class ViTHybridSelfAttention(nn.Module):
     def __init__(self, config: ViTHybridConfig) -> None:
         super().__init__()
@@ -247,7 +245,6 @@ class ViTHybridSelfAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTSdpaSelfAttention with ViT->ViTHybrid
 class ViTHybridSdpaSelfAttention(ViTHybridSelfAttention):
     def __init__(self, config: ViTHybridConfig) -> None:
         super().__init__(config)
@@ -279,7 +276,6 @@ class ViTHybridSdpaSelfAttention(ViTHybridSelfAttention):
         return context_layer, None
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->ViTHybrid
 class ViTHybridSelfOutput(nn.Module):
     """
     The residual connection is defined in ViTHybridLayer instead of here (as is the case with other models), due to the
@@ -298,7 +294,6 @@ class ViTHybridSelfOutput(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->ViTHybrid
 class ViTHybridAttention(nn.Module):
     def __init__(self, config: ViTHybridConfig) -> None:
         super().__init__()
@@ -338,14 +333,12 @@ class ViTHybridAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTSdpaAttention with ViT->ViTHybrid
 class ViTHybridSdpaAttention(ViTHybridAttention):
     def __init__(self, config: ViTHybridConfig) -> None:
         super().__init__(config)
         self.attention = ViTHybridSdpaSelfAttention(config)
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->ViTHybrid
 class ViTHybridIntermediate(nn.Module):
     def __init__(self, config: ViTHybridConfig) -> None:
         super().__init__()
@@ -362,7 +355,6 @@ class ViTHybridIntermediate(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->ViTHybrid
 class ViTHybridOutput(nn.Module):
     def __init__(self, config: ViTHybridConfig) -> None:
         super().__init__()
@@ -427,7 +419,6 @@ class ViTHybridLayer(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->ViTHybrid
 class ViTHybridEncoder(nn.Module):
     def __init__(self, config: ViTHybridConfig) -> None:
         super().__init__()
@@ -479,7 +470,6 @@ class ViTHybridEncoder(nn.Module):
         )
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTPreTrainedModel with ViT->ViTHybrid
 class ViTHybridPreTrainedModel(PreTrainedModel):
     """
     An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
@@ -558,7 +548,6 @@ VIT_INPUTS_DOCSTRING = r"""
     "The bare ViT Hybrid Model transformer outputting raw hidden-states without any specific head on top.",
     VIT_START_DOCSTRING,
 )
-# Copied from transformers.models.vit.modeling_vit.ViTModel with ViT->ViTHybrid
 class ViTHybridModel(ViTHybridPreTrainedModel):
     def __init__(self, config: ViTHybridConfig, add_pooling_layer: bool = True, use_mask_token: bool = False):
         super().__init__(config)
@@ -654,7 +643,6 @@ class ViTHybridModel(ViTHybridPreTrainedModel):
         )
 
 
-# Copied from transformers.models.vit.modeling_vit.ViTPooler with ViT->ViTHybrid
 class ViTHybridPooler(nn.Module):
     def __init__(self, config: ViTHybridConfig):
         super().__init__()
@@ -677,7 +665,6 @@ class ViTHybridPooler(nn.Module):
     """,
     VIT_START_DOCSTRING,
 )
-# Copied from transformers.models.vit.modeling_vit.ViTForImageClassification with ViT->ViTHybrid
 class ViTHybridForImageClassification(ViTHybridPreTrainedModel):
     def __init__(self, config: ViTHybridConfig) -> None:
         super().__init__(config)
diff --git a/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py b/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py
index 68fb70d4f1..e9e709af99 100644
--- a/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py
+++ b/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py
@@ -44,7 +44,6 @@ logger = logging.get_logger(__name__)
 _CONFIG_FOR_DOC = "XLMProphetNetConfig"
 
 
-# Copied from src.transformers.models.prophetnet.modeling_prophetnet.PROPHETNET_START_DOCSTRING with ProphetNetConfig->XLMProphetNetConfig
 XLM_PROPHETNET_START_DOCSTRING = r"""
     This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
     library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
@@ -64,7 +63,6 @@ XLM_PROPHETNET_START_DOCSTRING = r"""
             configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
 """
 
-# Copied from src.transformers.models.prophetnet.modeling_prophetnet.PROPHETNET_INPUTS_DOCSTRING with ProphetNet->XLMProphetNet
 XLM_PROPHETNET_INPUTS_DOCSTRING = r"""
     Args:
         input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
@@ -139,7 +137,6 @@ XLM_PROPHETNET_INPUTS_DOCSTRING = r"""
 """
 
 
-# Copied from src.transformers.models.prophetnet.modeling_prophetnet.PROPHETNET_STANDALONE_INPUTS_DOCSTRING with ProphetNet->XLMProphetNet
 XLM_PROPHETNET_STANDALONE_INPUTS_DOCSTRING = r"""
     Args:
         input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
@@ -174,7 +171,6 @@ XLM_PROPHETNET_STANDALONE_INPUTS_DOCSTRING = r"""
 """
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.softmax
 def softmax(hidden_state, dim, onnx_trace=False):
     if onnx_trace:
         return nn.functional.softmax(hidden_state.float(), dim=dim)
@@ -182,7 +178,6 @@ def softmax(hidden_state, dim, onnx_trace=False):
         return nn.functional.softmax(hidden_state, dim=dim, dtype=torch.float32)
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ngram_attention_bias
 def ngram_attention_bias(sequence_length, ngram, device, dtype):
     """
     This function computes the bias for the predict stream
@@ -200,7 +195,6 @@ def ngram_attention_bias(sequence_length, ngram, device, dtype):
     return torch.cat([left_block, right_block], dim=2)
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.compute_relative_buckets
 def compute_relative_buckets(num_buckets, max_distance, relative_positions, is_bidirectional=False):
     """
     This function computes individual parts of the relative position buckets. For more detail, see paper.
@@ -228,7 +222,6 @@ def compute_relative_buckets(num_buckets, max_distance, relative_positions, is_b
     return rel_positions_bucket
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.compute_all_stream_relative_buckets
 def compute_all_stream_relative_buckets(num_buckets, max_distance, position_ids):
     """
     This function computes both main and predict relative position buckets. For more detail, see paper.
@@ -253,7 +246,6 @@ def compute_all_stream_relative_buckets(num_buckets, max_distance, position_ids)
 
 
 @dataclass
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput with ProphetNet->XLMProphetNet all-casing
 class XLMProphetNetSeq2SeqLMOutput(ModelOutput):
     """
     Base class for sequence-to-sequence language models outputs.
@@ -339,7 +331,6 @@ class XLMProphetNetSeq2SeqLMOutput(ModelOutput):
 
 
 @dataclass
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput with ProphetNet->XLMProphetNet all-casing
 class XLMProphetNetSeq2SeqModelOutput(ModelOutput):
     """
     Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
@@ -426,7 +417,6 @@ class XLMProphetNetSeq2SeqModelOutput(ModelOutput):
 
 
 @dataclass
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput with ProphetNet->XLMProphetNet all-casing
 class XLMProphetNetDecoderModelOutput(ModelOutput):
     """
     Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
@@ -487,7 +477,6 @@ class XLMProphetNetDecoderModelOutput(ModelOutput):
 
 
 @dataclass
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput with ProphetNet->XLMProphetNet all-casing
 class XLMProphetNetDecoderLMOutput(ModelOutput):
     """
     Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
@@ -549,7 +538,6 @@ class XLMProphetNetDecoderLMOutput(ModelOutput):
     cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetPreTrainedModel with ProphetNet->XLMProphetNet
 class XLMProphetNetPreTrainedModel(PreTrainedModel):
     config_class = XLMProphetNetConfig
     base_model_prefix = "prophetnet"
@@ -588,7 +576,6 @@ class XLMProphetNetPreTrainedModel(PreTrainedModel):
         return shifted_input_ids
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetPositionalEmbeddings with ProphetNet->XLMProphetNet
 class XLMProphetNetPositionalEmbeddings(nn.Embedding):
     """
     This module learns positional embeddings up to a fixed maximum size. Padding ids are ignored by either offsetting
@@ -632,7 +619,6 @@ class XLMProphetNetPositionalEmbeddings(nn.Embedding):
         return super().forward(position_ids)
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetAttention with ProphetNet->XLMProphetNet
 class XLMProphetNetAttention(nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper"""
 
@@ -762,7 +748,6 @@ class XLMProphetNetAttention(nn.Module):
         return attn_output, attn_weights_reshaped, past_key_value
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetFeedForward with ProphetNet->XLMProphetNet
 class XLMProphetNetFeedForward(nn.Module):
     """
     This is the residual two feed-forward layer block based on the original Transformer implementation.
@@ -786,7 +771,6 @@ class XLMProphetNetFeedForward(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetNgramSelfAttention with ProphetNet->XLMProphetNet
 class XLMProphetNetNgramSelfAttention(nn.Module):
     def __init__(self, config: XLMProphetNetConfig):
         super().__init__()
@@ -1106,7 +1090,6 @@ class XLMProphetNetNgramSelfAttention(nn.Module):
         return predict_relative_pos_embeddings
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetEncoderLayer with ProphetNet->XLMProphetNet, Prophetnet->XLMProphetnet
 class XLMProphetNetEncoderLayer(nn.Module):
     """
     Encoder block for XLMProphetnet
@@ -1150,7 +1133,6 @@ class XLMProphetNetEncoderLayer(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLayer with Prophetnet->XLMProphetnet, ProphetNet->XLMProphetNet
 class XLMProphetNetDecoderLayer(nn.Module):
     """
     Decoder block for XLMProphetnet
@@ -1239,7 +1221,6 @@ class XLMProphetNetDecoderLayer(nn.Module):
     "The standalone encoder part of the XLMProphetNetModel.",
     XLM_PROPHETNET_START_DOCSTRING,
 )
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetEncoder with microsoft/prophetnet-large-uncased->patrickvonplaten/xprophetnet-large-uncased-standalone, ProphetNet->XLMProphetNet, PROPHETNET->XLM_PROPHETNET
 class XLMProphetNetEncoder(XLMProphetNetPreTrainedModel):
     r"""
     word_embeddings  (`torch.nn.Embeddings` of shape `(config.vocab_size, config.hidden_size)`, *optional*):
@@ -1374,7 +1355,6 @@ class XLMProphetNetEncoder(XLMProphetNetPreTrainedModel):
     "The standalone decoder part of the XLMProphetNetModel.",
     XLM_PROPHETNET_START_DOCSTRING,
 )
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoder with microsoft/prophetnet-large-uncased->patrickvonplaten/xprophetnet-large-uncased-standalone, ProphetNet->XLMProphetNet, PROPHETNET->XLM_PROPHETNET,
 class XLMProphetNetDecoder(XLMProphetNetPreTrainedModel):
     r"""
     word_embeddings  (`torch.nn.Embeddings` of shape `(config.vocab_size, config.hidden_size)`, *optional*):
@@ -1743,7 +1723,6 @@ class XLMProphetNetDecoder(XLMProphetNetPreTrainedModel):
     "The bare XLMProphetNet Model outputting raw hidden-states without any specific head on top.",
     XLM_PROPHETNET_START_DOCSTRING,
 )
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetModel with microsoft/prophetnet-large-uncased->patrickvonplaten/xprophetnet-large-uncased-standalone, ProphetNet->XLMProphetNet, PROPHETNET->XLM_PROPHETNET
 class XLMProphetNetModel(XLMProphetNetPreTrainedModel):
     _tied_weights_keys = ["encoder.word_embeddings.weight", "decoder.word_embeddings.weight"]
 
@@ -1878,7 +1857,6 @@ class XLMProphetNetModel(XLMProphetNetPreTrainedModel):
     "The XLMProphetNet Model with a language modeling head. Can be used for sequence generation tasks.",
     XLM_PROPHETNET_START_DOCSTRING,
 )
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetForConditionalGeneration with microsoft/prophetnet-large-uncased->patrickvonplaten/xprophetnet-large-uncased-standalone, ProphetNet->XLMProphetNet, PROPHETNET->XLM_PROPHETNET
 class XLMProphetNetForConditionalGeneration(XLMProphetNetPreTrainedModel):
     _tied_weights_keys = ["encoder.word_embeddings.weight", "decoder.word_embeddings.weight", "lm_head.weight"]
 
@@ -2073,7 +2051,6 @@ class XLMProphetNetForConditionalGeneration(XLMProphetNetPreTrainedModel):
         return self._shift_right(labels)
 
     @staticmethod
-    # Copied from transformers.models.bart.modeling_bart.BartForConditionalGeneration._reorder_cache
     def _reorder_cache(past_key_values, beam_idx):
         reordered_past = ()
         for layer_past in past_key_values:
@@ -2096,7 +2073,6 @@ class XLMProphetNetForConditionalGeneration(XLMProphetNetPreTrainedModel):
     " language modeling.",
     XLM_PROPHETNET_START_DOCSTRING,
 )
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetForCausalLM with microsoft/prophetnet-large-uncased->patrickvonplaten/xprophetnet-large-uncased-standalone, ProphetNet->XLMProphetNet, PROPHETNET->XLM_PROPHETNET
 class XLMProphetNetForCausalLM(XLMProphetNetPreTrainedModel):
     _tied_weights_keys = [
         "prophetnet.word_embeddings.weight",
@@ -2329,7 +2305,6 @@ class XLMProphetNetForCausalLM(XLMProphetNetPreTrainedModel):
         }
 
     @staticmethod
-    # Copied from transformers.models.bart.modeling_bart.BartForCausalLM._reorder_cache
     def _reorder_cache(past_key_values, beam_idx):
         reordered_past = ()
         for layer_past in past_key_values:
@@ -2339,7 +2314,6 @@ class XLMProphetNetForCausalLM(XLMProphetNetPreTrainedModel):
         return reordered_past
 
 
-# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderWrapper with ProphetNet->XLMProphetNet, prophetnet->XLMProphetNet
 class XLMProphetNetDecoderWrapper(XLMProphetNetPreTrainedModel):
     """
     This is a wrapper class, so that [`XLMProphetNetForCausalLM`] can correctly be loaded from pretrained XLMProphetNet
diff --git a/src/transformers/models/gemma/modeling_gemma.py b/src/transformers/models/gemma/modeling_gemma.py
index ff0f7082e9..2c948dd74c 100644
--- a/src/transformers/models/gemma/modeling_gemma.py
+++ b/src/transformers/models/gemma/modeling_gemma.py
@@ -408,7 +408,7 @@ class GemmaFlashAttention2(GemmaAttention):
             query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
         )
 
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
         attn_output = self.o_proj(attn_output)
 
         if not output_attentions:
@@ -594,7 +594,7 @@ class GemmaSdpaAttention(GemmaAttention):
         )
 
         attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+        attn_output = attn_output.view(bsz, q_len, -1)
 
         attn_output = self.o_proj(attn_output)
 
@@ -866,9 +866,9 @@ class GemmaModel(GemmaPreTrainedModel):
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
-        return_legacy_cache = False
+        return_legacy_cache = False  # noqa: F841
         if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
-            return_legacy_cache = True
+            return_legacy_cache = True  # noqa: F841
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 
         if cache_position is None:
diff --git a/src/transformers/models/llama/modeling_llama.py b/src/transformers/models/llama/modeling_llama.py
index 836528ee21..92c3249247 100644
--- a/src/transformers/models/llama/modeling_llama.py
+++ b/src/transformers/models/llama/modeling_llama.py
@@ -360,7 +360,7 @@ class LlamaAttention(nn.Module):
 
         attn_output = attn_output.transpose(1, 2).contiguous()
 
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+        attn_output = attn_output.reshape(bsz, q_len, -1)
 
         if self.config.pretraining_tp > 1:
             attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
@@ -467,7 +467,7 @@ class LlamaFlashAttention2(LlamaAttention):
             query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
         )
 
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
         attn_output = self.o_proj(attn_output)
 
         if not output_attentions:
@@ -653,7 +653,7 @@ class LlamaSdpaAttention(LlamaAttention):
         )
 
         attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+        attn_output = attn_output.view(bsz, q_len, -1)
 
         attn_output = self.o_proj(attn_output)
 
diff --git a/src/transformers/models/mistral/modeling_mistral.py b/src/transformers/models/mistral/modeling_mistral.py
index c54b8774ee..4a489ecb25 100644
--- a/src/transformers/models/mistral/modeling_mistral.py
+++ b/src/transformers/models/mistral/modeling_mistral.py
@@ -620,7 +620,6 @@ class MistralSdpaAttention(MistralAttention):
         value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
 
         cos, sin = self.rotary_emb(value_states, position_ids)
-
         query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
         if past_key_value is not None:
@@ -656,7 +655,7 @@ class MistralSdpaAttention(MistralAttention):
         )
 
         attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+        attn_output = attn_output.view(bsz, q_len, -1)
 
         attn_output = self.o_proj(attn_output)
 
diff --git a/src/transformers/tokenization_utils.py b/src/transformers/tokenization_utils.py
index b7c023d951..4231526265 100644
--- a/src/transformers/tokenization_utils.py
+++ b/src/transformers/tokenization_utils.py
@@ -56,14 +56,26 @@ class Trie:
     Loose reference https://en.wikipedia.org/wiki/Trie
     """
 
-    def __init__(self):
+    def __init__(self, *args):
         self.data = {}
         self._tokens = set()
+        self._termination_char = ""
+        self.update(*args)
+
+    def update(self, *args):
+        """
+        Updates the Trie with new tokens provided as arguments.
+
+        Args:
+            *args: Variable number of words to be added to the Trie.
+        """
+        for token in tuple(*args):
+            self.add(token)
 
     def add(self, word: str):
         """
         Passes over every char (utf-8 char) on word and recursively adds it to the internal `data` trie representation.
-        The special key `""` is used to represent termination.
+        The special key `""` in `self._termination_char` is used to represent termination.
 
         This function is idempotent, adding twice the same word will leave the trie unchanged
 
@@ -87,9 +99,9 @@ class Trie:
         self._tokens.add(word)
         ref = self.data
         for char in word:
-            ref[char] = char in ref and ref[char] or {}
+            ref[char] = ref.setdefault(char, {})
             ref = ref[char]
-        ref[""] = 1
+        ref[self._termination_char] = 1
 
     def split(self, text: str) -> List[str]:
         """
@@ -269,6 +281,62 @@ class Trie:
         return tokens
 
 
+class ExtensionsTrie(Trie):
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def extensions(self, prefix: str):
+        """
+        Generates all extensions of a given prefix token in the Trie.
+
+        Example:
+
+        ```python
+        >>> trie = Trie()
+        >>> trie.add("apple")
+        >>> trie.add("app")
+        >>> trie.add("application")
+        >>> trie.extensions("app")
+        ['app', 'apple', 'application']
+        ```
+        """
+        prefix_node = self._get_node(prefix)
+        ret = self._collect_tokens(prefix_node)
+        return [prefix + token for token in ret]
+
+    def _get_node(self, token: str) -> dict:
+        """
+        Retrieves the node corresponding to the given token in the Trie.
+
+        Args:
+            token (str): The token for which the corresponding node needs to be retrieved.
+
+        Returns:
+            dict: The node in the Trie corresponding to the given token.
+        """
+        node = self.data
+        for char in token:
+            node = node[char]
+        return node
+
+    def _collect_tokens(self, node: dict) -> list:
+        """
+        Generates all tokens in the Trie starting from a given node.
+
+        Args:
+            node (dict): The node in the Trie from which tokens need to be generated.
+
+        Returns:
+            list: List of tokens generated from the given node.
+        """
+        tokens = [self._termination_char] if self._termination_char in node else []
+        for token, subtrie_head in node.items():
+            if token != self._termination_char:
+                subtokens = self._collect_tokens(subtrie_head)
+                tokens.extend([token + subtoken for subtoken in subtokens])
+        return tokens
+
+
 def _is_whitespace(char):
     """Checks whether `char` is a whitespace character."""
     # \t, \n, and \r are technically control characters but we treat them
diff --git a/utils/deprecate_models.py b/utils/deprecate_models.py
index 23308e91a7..add8da74d9 100644
--- a/utils/deprecate_models.py
+++ b/utils/deprecate_models.py
@@ -124,6 +124,25 @@ def update_relative_imports(filename, model):
         f.write("\n".join(new_file_lines))
 
 
+def remove_copied_from_statements(model):
+    model_path = REPO_PATH / f"src/transformers/models/{model}"
+    for file in os.listdir(model_path):
+        if file == "__pycache__":
+            continue
+        file_path = model_path / file
+        with open(file_path, "r") as f:
+            file_lines = f.read()
+
+        new_file_lines = []
+        for line in file_lines.split("\n"):
+            if "# Copied from" in line:
+                continue
+            new_file_lines.append(line)
+
+        with open(file_path, "w") as f:
+            f.write("\n".join(new_file_lines))
+
+
 def move_model_files_to_deprecated(model):
     model_path = REPO_PATH / f"src/transformers/models/{model}"
     deprecated_model_path = REPO_PATH / f"src/transformers/models/deprecated/{model}"
@@ -321,6 +340,10 @@ def deprecate_models(models):
         print("Adding tip message to model doc page")
         insert_tip_to_model_doc(model_info["model_doc_path"], tip_message)
 
+        # Remove #Copied from statements from model's files
+        print("Removing #Copied from statements from model's files")
+        remove_copied_from_statements(model)
+
         # Move the model file to deprecated: src/transfomers/models/model -> src/transformers/models/deprecated/model
         print("Moving model files to deprecated for model")
         move_model_files_to_deprecated(model)
diff --git a/utils/diff_model_converter.py b/utils/diff_model_converter.py
index d9786a9b3c..b37ad74cb1 100644
--- a/utils/diff_model_converter.py
+++ b/utils/diff_model_converter.py
@@ -238,11 +238,16 @@ class SuperTransformer(cst.CSTTransformer):
         Helper method to update the body by removing duplicates before adding new statements.
         """
         deduplicated_new_body = []
-        existing_nodes = {
-            self.python_module.code_for_node(node).strip() for node in new_statements if isinstance(node, cst.CSTNode)
-        }
+        existing_nodes = set()
+        for node in new_statements:
+            code = self.python_module.code_for_node(node)
+            comment_less_code = re.sub(r"#.*", "", code).strip()
+            comment_less_code = re.sub(r"\ *\n", "\n", comment_less_code).strip()
+            existing_nodes.add(comment_less_code)
         for stmt in existing_body:
-            if self.python_module.code_for_node(stmt).strip() not in existing_nodes:
+            comment_less_code = re.sub(r"#.*", "", self.python_module.code_for_node(stmt)).strip()
+            comment_less_code = re.sub(r"\ *\n", "\n", comment_less_code).strip()
+            if comment_less_code not in existing_nodes:
                 if m.matches(stmt, DOCSTRING_NODE) and self.has_docstring:
                     continue
                 deduplicated_new_body.append(stmt)
@@ -542,7 +547,7 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--files_to_parse",
-        default=["/Users/arthurzucker/Work/transformers/examples/diff-conversion/diff_my_new_model.py"],
+        default=["all"],
         nargs="+",
         help="A list of `diff_xxxx` files that should be converted to single model file",
     )


Test patch:
diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index 57b6c6d188..1981f5a639 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -27,6 +27,7 @@ from transformers import is_torch_available, pipeline, set_seed
 from transformers.testing_utils import (
     is_flaky,
     require_accelerate,
+    require_auto_gptq,
     require_quanto,
     require_torch,
     require_torch_multi_accelerator,
@@ -3066,6 +3067,43 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
         self.assertTrue(y_prob > 0.001 and n_prob > 0.001)
         self.assertTrue(y_prob <= 1.0 and n_prob <= 1.0)
 
+
+@require_torch
+class TokenHealingTestCase(unittest.TestCase):
+    @parameterized.expand(
+        [
+            (
+                "square_bracket",
+                'An example ["like this"] and another example [',
+                'An example ["like this"] and another example ["',
+            ),
+            ("url", 'The link is <a href="http:', 'The link is <a href="http://'),
+            # aggressive_healing: "http" shouldn't be replaced with "https"
+            ("aggressive_healing", 'The link is <a href="http', 'The link is <a href="http'),
+            ("trailing_whitespace", "I read a book about ", "I read a book about"),
+            ("nothing_to_heal", "I read a book about", "I read a book about"),
+            ("single_token", "I", "I"),
+            ("empty_prompt", "", ""),
+        ]
+    )
+    @require_auto_gptq
+    def test_prompts(self, name, input, expected):
+        model_name_or_path = "TheBloke/deepseek-llm-7B-base-GPTQ"
+        tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
+        completion_model = AutoModelForCausalLM.from_pretrained(
+            model_name_or_path,
+            device_map="auto",
+            trust_remote_code=False,
+            revision="main",
+            use_cache=True,
+        )
+        input_ids = tokenizer(input, return_tensors="pt").input_ids.to(completion_model.device)
+
+        healed_ids = completion_model.heal_tokens(input_ids)
+        predicted = tokenizer.decode(healed_ids[0], skip_special_tokens=True)
+
+        self.assertEqual(predicted, expected)
+
     def test_generate_from_inputs_embeds_with_bos_token_id_is_none(self):
         article = "Today a dragon flew over Paris."
         model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
diff --git a/tests/test_tokenization_utils.py b/tests/test_tokenization_utils.py
index 885c6d6d2e..894567d93d 100644
--- a/tests/test_tokenization_utils.py
+++ b/tests/test_tokenization_utils.py
@@ -33,7 +33,7 @@ from transformers import (
     is_tokenizers_available,
 )
 from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
-from transformers.tokenization_utils import Trie
+from transformers.tokenization_utils import ExtensionsTrie, Trie
 
 
 sys.path.append(str(Path(__file__).parent.parent / "utils"))
@@ -274,3 +274,35 @@ class TrieTest(unittest.TestCase):
         trie = Trie()
         parts = trie.cut_text("ABC", [0, 0, 2, 1, 2, 3])
         self.assertEqual(parts, ["AB", "C"])
+
+
+class ExtensionsTrieTest(unittest.TestCase):
+    def test_extensions(self):
+        # Test searching by prefix
+        trie = ExtensionsTrie()
+        trie.add("foo")
+        trie.add("food")
+        trie.add("foodie")
+        trie.add("helium")
+        self.assertEqual(trie.extensions("foo"), ["foo", "food", "foodie"])
+        self.assertEqual(trie.extensions("helium"), ["helium"])
+
+    def test_empty_prefix(self):
+        trie = ExtensionsTrie()
+        # Test searching with an empty prefix returns all values
+        trie.add("hello")
+        trie.add("bye")
+        self.assertEqual(trie.extensions(""), ["hello", "bye"])
+
+    def test_no_extension_match(self):
+        trie = ExtensionsTrie()
+        # Test searching for a prefix that doesn't match any key
+        with self.assertRaises(KeyError):
+            trie.extensions("unknown")
+
+    def test_update_value(self):
+        trie = ExtensionsTrie()
+        # Test updating the value of an existing key
+        trie.add("hi")
+        trie.add("hi")
+        self.assertEqual(trie.extensions("hi"), ["hi"])