+
    QV-jð�  ã                   ór  € R t ^ RIt^ RIt^ RIt^ RIt^ RIt^ RIHt ^ RIH	t	 R t
R tR tR tRtR	 tR
 tRYR ltRtRtRtRtRtRtRtRtRtRtRtRtRtRtRt R]R]R]R]R]R ]R!]R"]R#]R$]R%]R&]R']R(] /t!R)t"R*t#]t$]t%R+t&R,t'R,t(R,t)R,t*R,t+R,t,]t-R,t.R,t/R,t0R,t1] t2R,t3R,t4R,t5]t6]t7R,t8]t9]t:]t;R,t<R-t=]! R.]"3R/]#3R0];3R1]$3R2]:3R3]%3R4]=3R5]&3R6]'3R7]<3R8]23R9](3R:]53R;]33R<]*3R=]13R>]83R?]+3R@],3RA]-3RB]73RC]93RD]43RE]/3RF].3.4      t>]! . RZO4      t?RG t@RHRRIRRJRRKRRLRMRN^RO^RPRRQRRRRRSRRTRRUR/RV ltAR[RW ltBRX tCR# )\z3
Doc utilities: Utilities related to documentation
N)ÚOrderedDict)Úcastc                óü   € \         P                  ! V 4      '       d   ^# \         P                  ! V 4      pVP                  4       ^ ,          p\	        V4      \	        VP                  4       4      ,
          p^V,           # )z^Return the indentation level of the start of the docstring of a class or function (or method).)ÚinspectÚisclassÚ	getsourceÚ
splitlinesÚlenÚlstrip)ÚfuncÚsourceÚ
first_lineÚfunction_def_levels   &   Úg/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/transformers/utils/doc.pyÚget_docstring_indentation_levelr      sb   € ô ‡‚�t×ÒÙÜ×Ò˜tÓ$€FØ×"Ñ"Ó$ QÕ'€JÜ˜Z›¬3¨z×/@Ñ/@Ó/BÓ+CÕCÐØÐ!Õ!Ð!ó    c                  ó   a € V 3R  lpV# )c                 óv   <€ R P                  S4      V P                  e   V P                  MR ,           V n        V # )Ú )ÚjoinÚ__doc__©ÚfnÚdocstrs   &€r   Údocstring_decoratorÚ1add_start_docstrings.<locals>.docstring_decorator'   s,   ø€ Ø—W‘W˜V“_°b·j±jÒ6L¨¯
ª
ÐRTÕUˆŒ
Øˆ	r   © ©r   r   s   j r   Úadd_start_docstringsr   &   ó   ø€ õð Ðr   c                  ó   a € V 3R  lpV# )c                 óÂ  <€ R V P                   P                  R4      ^ ,           R2pRV R2p\        V 4      pV P                  e   V P                  MRp \	        R VP                  4        4       4      p\        V4      \        VP                  4       4      ,
          pS
pV^V,           8X  dt   S
 Uu. uF5  p\        P                  ! \        P                  ! V4      RV,          4      NK7  	  pp\        P                  ! \        P                  ! V4      RV,          4      pRP                  V4      V,           p	W),           V n        V #   \         d    Tp L¹i ; iu upi )z[`Ú.z`]z    The aa   forward method, overrides the `__call__` special method.

    <Tip>

    Although the recipe for forward pass needs to be defined within this function, one should call the [`Module`]
    instance afterwards instead of this since the former takes care of running the pre and post processing steps while
    the latter silently ignores them.

    </Tip>
r   c              3   óR   "  € T F  qP                  4       R 8w  g   K  Vx € K  	  R# 5i)r   N)Ústrip)Ú.0Úlines   & r   Ú	<genexpr>ÚUadd_start_docstrings_to_model_forward.<locals>.docstring_decorator.<locals>.<genexpr>?   s#   é € Ð"cÑ4L¨D×PZÑPZÓP\Ð`bÑPb§4¢4Ó4Lùs   ‚'�
'Ú )Ú__qualname__Úsplitr   r   Únextr   r	   r
   ÚStopIterationÚtextwrapÚindentÚdedentr   )r   Ú
class_nameÚintroÚcorrect_indentationÚcurrent_docÚfirst_non_emptyÚdoc_indentationÚdocsÚdocÚ	docstringr   s   &         €r   r   ÚBadd_start_docstrings_to_model_forward.<locals>.docstring_decorator/   s8  ø€ Ø˜"Ÿ/™/×/Ñ/°Ó4°QÕ7Ð8¸Ð;ˆ
Ø˜j˜\ð 	*ð 	ˆô >¸bÓAÐØ$&§J¡JÒ$:�b—j’jÀˆð	2Ü"Ñ"c°K×4JÑ4JÔ4LÓ"cÓcˆOÜ! /Ó2´S¸×9OÑ9OÓ9QÓ5RÕRˆOð ˆð ˜aÐ"5Õ5Ô5Ù`fÓgÑ`fÐY\”H—O’O¤H§O¢O°CÓ$8¸#Ð@SÕ:SÖTÑ`fˆDÐgÜ—O’O¤H§O¢O°EÓ$:¸CÐBUÕ<UÓVˆEà—G‘G˜D“M KÕ/ˆ	ØÕ&ˆŒ
Øˆ	øô ô 	2Ø1ŠOð	2üò hs   ÁA	E	 Â3;EÅ	EÅEr   r   s   j r   Ú%add_start_docstrings_to_model_forwardr;   .   s   ø€ õð@ Ðr   c                  ó   a € V 3R  lpV# )c                 óv   <€ V P                   e   V P                   MRRP                  S4      ,           V n         V # )Nr   )r   r   r   s   &€r   r   Ú/add_end_docstrings.<locals>.docstring_decoratorS   s+   ø€ Ø$&§J¡JÒ$:�b—j’jÀÀbÇgÁgÈfÃoÕUˆŒ
Øˆ	r   r   r   s   j r   Úadd_end_docstringsr?   R   r   r   a:  
    Returns:
        [`{full_output_type}`] or `tuple(torch.FloatTensor)`: A [`{full_output_type}`] or a tuple of
        `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
        elements depending on the configuration ([`{config_class}`]) and inputs.

c                ój   € \         P                  ! RV 4      pVf   R# VP                  4       ^ ,          # )z.Returns the indentation in the first line of tz^(\s*)\Sr   )ÚreÚsearchÚgroups)ÚtrB   s   & r   Ú_get_indentrE   c   s,   € ä�YŠY�{ AÓ&€FØ’ˆ2Ð7 V§]¡]£_°QÕ%7Ð7r   c                óê  € \        V 4      p. pRpV P                  R4       FP  p\        V4      V8X  d,   \        V4      ^ 8”  d   VP                  VRR	 4       V R2pK>  W4R,           R2,          pKR  	  VP                  VRR	 4       \	        \        V4      4       FC  p\
        P                  ! RRW%,          4      W%&   \
        P                  ! RRW%,          4      W%&   KE  	  RP                  V4      # )
z,Convert output_args_doc to display properly.r   Ú
N:é   NNz^(\s+)(\S+)(\s+)z\1- **\2**\3z:\s*\n\s*(\S)z -- \1éÿÿÿÿ)rE   r+   r	   ÚappendÚrangerA   Úsubr   )Úoutput_args_docr/   ÚblocksÚcurrent_blockr&   Úis   &     r   Ú_convert_output_args_docrQ   i   sâ   € ô ˜Ó)€FØ€FØ€MØ×%Ñ% dÖ+ˆä�tÓ Ô&Ü�=Ó! AÔ%Ø—‘˜m¨C¨RÐ0Ô1Ø#˜f B˜KŠMð  R¥˜z¨˜_Õ,ŠMñ ,ð ‡M�M�-  Ð$Ô%ô ”3�v“;ÖˆÜ—F’FÐ.°ÀÅÓKˆ‰	Ü—F’FÐ+¨Y¸½	ÓBˆ‹	ñ  ð �9‰9�VÓÐr   c                ó†  € V P                   pRpVe¥   VP                  R4      p^ pV\        V4      8  d+   \        P                  ! RWg,          4      f   V^,          pK:  V\        V4      8  d'   RP                  Wg^,           R 4      p\        V4      pM!V'       d   \        RV P                   R24      hV'       d3   V P                   RV P                   2p\        P                  W�R7      p	M\        V 4      pRV R	2p	Ve
   V	R
,          p	T	p
Ve	   W¥,          p
Veœ   V
P                  R4      p^ p\        Wg,          4      ^ 8X  d   V^,          pK!  \        \        Wg,          4      4      pW²8  dH   RW+,
          ,          pV Uu. uF  p\        V4      ^ 8”  d   V V 2MTNK  	  ppRP                  V4      p
V
# u upi )z@
Prepares the return part of the docstring using `output_type`.
NrG   z^\s*(Args|Parameters):\s*$z@No `Args` or `Parameters` section is found in the docstring of `zH`. Make sure it has docstring and contain either `Args` or `Parameters`.r"   )Úfull_output_typeÚconfig_classz
Returns:
    `Ú`z:
r)   )r   r+   r	   rA   rB   r   rQ   Ú
ValueErrorÚ__name__Ú
__module__ÚPT_RETURN_INTRODUCTIONÚformatÚstrrE   )Úoutput_typerT   Ú
min_indentÚ	add_introÚoutput_docstringÚparams_docstringÚlinesrP   rS   r2   Úresultr/   Úto_addr&   s   &&&&          r   Ú_prepare_output_docstringsrd   ƒ   sÅ  € ð #×*Ñ*ÐØÐØÒ#à ×&Ñ& tÓ,ˆØˆØ”#�e“*Œn¤§¢Ð+HÈ%Í(Ó!SÒ![Ø��FŠAØŒs�5‹zŒ>Ø#Ÿy™y¨°Aµ¨yÐ)9Ó:ÐÜ7Ð8HÓIÑßÜØRÐS^×SgÑSgÐRhð iGð Góð ÷ Ø)×4Ñ4Ð5°Q°{×7KÑ7KÐ6LÐMÐÜ&×-Ñ-Ð?OÐ-Ók‰ä˜{Ó+ÐØ#Ð$4Ð#5°QÐ7ˆØÒ'Ø�U�NˆEà€FØÒ#ØÕ"ˆð ÒØ—‘˜TÓ"ˆàˆÜ�%•(‹m˜qÔ Ø��FŠAÜ”[ ¥Ó*Ó+ˆàÔØ˜JÕ/Õ0ˆFÙPUÓVÑPUÈ¬3¨t«9°q¬=˜˜  Ñ'¸dÒBÑPUˆEÐVØ—Y‘Y˜uÓ%ˆFà€Mùò Ws   Æ!F>aJ  
    <Tip warning={true}>

    This example uses a random model as the real ones are all very big. To get proper results, you should use
    {real_checkpoint} instead of {fake_checkpoint}. If you get out-of-memory when loading that checkpoint, you can try
    adding `device_map="auto"` in the `from_pretrained` call.

    </Tip>
a  
    Example:

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> inputs = tokenizer(
    ...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
    ... )

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> predicted_token_class_ids = logits.argmax(-1)

    >>> # Note that tokens are classified rather then input words which means that
    >>> # there might be more predicted token classes than words.
    >>> # Multiple token classes might account for the same word
    >>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
    >>> predicted_tokens_classes
    {expected_output}

    >>> labels = predicted_token_class_ids
    >>> loss = model(**inputs, labels=labels).loss
    >>> round(loss.item(), 2)
    {expected_loss}
    ```
a_  
    Example:

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

    >>> inputs = tokenizer(question, text, return_tensors="pt")
    >>> with torch.no_grad():
    ...     outputs = model(**inputs)

    >>> answer_start_index = outputs.start_logits.argmax()
    >>> answer_end_index = outputs.end_logits.argmax()

    >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
    >>> tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
    {expected_output}

    >>> # target is "nice puppet"
    >>> target_start_index = torch.tensor([{qa_target_start_index}])
    >>> target_end_index = torch.tensor([{qa_target_end_index}])

    >>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
    >>> loss = outputs.loss
    >>> round(loss.item(), 2)
    {expected_loss}
    ```
a  
    Example of single-label classification:

    ```python
    >>> import torch
    >>> from transformers import AutoTokenizer, {model_class}

    >>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> predicted_class_id = logits.argmax().item()
    >>> model.config.id2label[predicted_class_id]
    {expected_output}

    >>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
    >>> num_labels = len(model.config.id2label)
    >>> model = {model_class}.from_pretrained("{checkpoint}", num_labels=num_labels)

    >>> labels = torch.tensor([1])
    >>> loss = model(**inputs, labels=labels).loss
    >>> round(loss.item(), 2)
    {expected_loss}
    ```

    Example of multi-label classification:

    ```python
    >>> import torch
    >>> from transformers import AutoTokenizer, {model_class}

    >>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}", problem_type="multi_label_classification")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]

    >>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
    >>> num_labels = len(model.config.id2label)
    >>> model = {model_class}.from_pretrained(
    ...     "{checkpoint}", num_labels=num_labels, problem_type="multi_label_classification"
    ... )

    >>> labels = torch.sum(
    ...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
    ... ).to(torch.float)
    >>> loss = model(**inputs, labels=labels).loss
    ```
a   
    Example:

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="pt")

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> # retrieve index of {mask}
    >>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]

    >>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
    >>> tokenizer.decode(predicted_token_id)
    {expected_output}

    >>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
    >>> # mask labels of non-{mask} tokens
    >>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

    >>> outputs = model(**inputs, labels=labels)
    >>> round(outputs.loss.item(), 2)
    {expected_loss}
    ```
a�  
    Example:

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
    >>> outputs = model(**inputs)

    >>> last_hidden_states = outputs.last_hidden_state
    ```
a•  
    Example:

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
    >>> choice0 = "It is eaten with a fork and a knife."
    >>> choice1 = "It is eaten while held in the hand."
    >>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1

    >>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
    >>> outputs = model(**{{k: v.unsqueeze(0) for k, v in encoding.items()}}, labels=labels)  # batch size is 1

    >>> # the linear classifier still needs to be trained
    >>> loss = outputs.loss
    >>> logits = outputs.logits
    ```
a½  
    Example:

    ```python
    >>> import torch
    >>> from transformers import AutoTokenizer, {model_class}

    >>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
    >>> outputs = model(**inputs, labels=inputs["input_ids"])
    >>> loss = outputs.loss
    >>> logits = outputs.logits
    ```
aA  
    Example:

    ```python
    >>> from transformers import AutoProcessor, {model_class}
    >>> import torch
    >>> from datasets import load_dataset

    >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
    >>> dataset = dataset.sort("id")
    >>> sampling_rate = dataset.features["audio"].sampling_rate

    >>> processor = AutoProcessor.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> # audio file is decoded on the fly
    >>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
    >>> with torch.no_grad():
    ...     outputs = model(**inputs)

    >>> last_hidden_states = outputs.last_hidden_state
    >>> list(last_hidden_states.shape)
    {expected_output}
    ```
a]  
    Example:

    ```python
    >>> from transformers import AutoProcessor, {model_class}
    >>> from datasets import load_dataset
    >>> import torch

    >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
    >>> dataset = dataset.sort("id")
    >>> sampling_rate = dataset.features["audio"].sampling_rate

    >>> processor = AutoProcessor.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> # audio file is decoded on the fly
    >>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits
    >>> predicted_ids = torch.argmax(logits, dim=-1)

    >>> # transcribe speech
    >>> transcription = processor.batch_decode(predicted_ids)
    >>> transcription[0]
    {expected_output}

    >>> inputs["labels"] = processor(text=dataset[0]["text"], return_tensors="pt").input_ids

    >>> # compute loss
    >>> loss = model(**inputs).loss
    >>> round(loss.item(), 2)
    {expected_loss}
    ```
a²  
    Example:

    ```python
    >>> from transformers import AutoFeatureExtractor, {model_class}
    >>> from datasets import load_dataset
    >>> import torch

    >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
    >>> dataset = dataset.sort("id")
    >>> sampling_rate = dataset.features["audio"].sampling_rate

    >>> feature_extractor = AutoFeatureExtractor.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> # audio file is decoded on the fly
    >>> inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> predicted_class_ids = torch.argmax(logits, dim=-1).item()
    >>> predicted_label = model.config.id2label[predicted_class_ids]
    >>> predicted_label
    {expected_output}

    >>> # compute loss - target_label is e.g. "down"
    >>> target_label = model.config.id2label[0]
    >>> inputs["labels"] = torch.tensor([model.config.label2id[target_label]])
    >>> loss = model(**inputs).loss
    >>> round(loss.item(), 2)
    {expected_loss}
    ```
aÉ  
    Example:

    ```python
    >>> from transformers import AutoFeatureExtractor, {model_class}
    >>> from datasets import load_dataset
    >>> import torch

    >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
    >>> dataset = dataset.sort("id")
    >>> sampling_rate = dataset.features["audio"].sampling_rate

    >>> feature_extractor = AutoFeatureExtractor.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> # audio file is decoded on the fly
    >>> inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt", sampling_rate=sampling_rate)
    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> probabilities = torch.sigmoid(logits[0])
    >>> # labels is a one-hot array of shape (num_frames, num_speakers)
    >>> labels = (probabilities > 0.5).long()
    >>> labels[0].tolist()
    {expected_output}
    ```
a  
    Example:

    ```python
    >>> from transformers import AutoFeatureExtractor, {model_class}
    >>> from datasets import load_dataset
    >>> import torch

    >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
    >>> dataset = dataset.sort("id")
    >>> sampling_rate = dataset.features["audio"].sampling_rate

    >>> feature_extractor = AutoFeatureExtractor.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> # audio file is decoded on the fly
    >>> inputs = feature_extractor(
    ...     [d["array"] for d in dataset[:2]["audio"]], sampling_rate=sampling_rate, return_tensors="pt", padding=True
    ... )
    >>> with torch.no_grad():
    ...     embeddings = model(**inputs).embeddings

    >>> embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu()

    >>> # the resulting embeddings can be used for cosine similarity-based retrieval
    >>> cosine_sim = torch.nn.CosineSimilarity(dim=-1)
    >>> similarity = cosine_sim(embeddings[0], embeddings[1])
    >>> threshold = 0.7  # the optimal threshold is dataset-dependent
    >>> if similarity < threshold:
    ...     print("Speakers are not the same!")
    >>> round(similarity.item(), 2)
    {expected_output}
    ```
a‘  
    Example:

    ```python
    >>> from transformers import AutoImageProcessor, {model_class}
    >>> import torch
    >>> from datasets import load_dataset

    >>> dataset = load_dataset("huggingface/cats-image")
    >>> image = dataset["test"]["image"][0]

    >>> image_processor = AutoImageProcessor.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> inputs = image_processor(image, return_tensors="pt")

    >>> with torch.no_grad():
    ...     outputs = model(**inputs)

    >>> last_hidden_states = outputs.last_hidden_state
    >>> list(last_hidden_states.shape)
    {expected_output}
    ```
aÜ  
    Example:

    ```python
    >>> from transformers import AutoImageProcessor, {model_class}
    >>> import torch
    >>> from datasets import load_dataset

    >>> dataset = load_dataset("huggingface/cats-image")
    >>> image = dataset["test"]["image"][0]

    >>> image_processor = AutoImageProcessor.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> inputs = image_processor(image, return_tensors="pt")

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> # model predicts one of the 1000 ImageNet classes
    >>> predicted_label = logits.argmax(-1).item()
    >>> print(model.config.id2label[predicted_label])
    {expected_output}
    ```
ÚSequenceClassificationÚQuestionAnsweringÚTokenClassificationÚMultipleChoiceÚMaskedLMÚLMHeadÚ	BaseModelÚSpeechBaseModelÚCTCÚAudioClassificationÚAudioFrameClassificationÚAudioXVectorÚVisionBaseModelÚImageClassificationa  
    Example:

    ```python
    >>> from transformers import AutoProcessor, {model_class}, SpeechT5HifiGan

    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> processor = AutoProcessor.from_pretrained("{checkpoint}")
    >>> vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
    >>> inputs = processor(text="Hello, my dog is cute", return_tensors="pt")

    >>> # generate speech
    >>> speech = model.generate(inputs["input_ids"], speaker_embeddings=speaker_embeddings, vocoder=vocoder)
    ```
az  
    Example:

    ```python
    >>> from transformers import AutoProcessor, {model_class}

    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> processor = AutoProcessor.from_pretrained("{checkpoint}")
    >>> inputs = processor(text="Hello, my dog is cute", return_tensors="pt")

    >>> # generate speech
    >>> speech = model(inputs["input_ids"])
    ```
a.  
    Example:

    ```python
    >>> from transformers import AutoImageProcessor, {model_class}
    >>> import torch
    >>> from PIL import Image
    >>> import httpx
        >>> from io import BytesIO

    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> with httpx.stream("GET", url) as response:
    ...     image = Image.open(BytesIO(response.read())).convert("RGB")

    >>> processor = AutoImageProcessor.from_pretrained("{checkpoint}")
    >>> model = {model_class}.from_pretrained("{checkpoint}")

    >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    >>> model.to(device)

    >>> # prepare image for the model
    >>> inputs = processor(images=image, return_tensors="pt").to(device)

    >>> with torch.no_grad():
    ...     outputs = model(**inputs)

    >>> # interpolate to original size
    >>> post_processed_output = processor.post_process_depth_estimation(
    ...     outputs, [(image.height, image.width)],
    ... )
    >>> predicted_depth = post_processed_output[0]["predicted_depth"]
    ```
z%
    Example:

    ```python
    ```
aÆ  
    Example:

    ```python
    >>> from PIL import Image
    >>> from transformers import AutoProcessor, {model_class}

    >>> model = {model_class}.from_pretrained("{checkpoint}")
    >>> processor = AutoProcessor.from_pretrained("{checkpoint}")

    >>> messages = [
    ...     {{
    ...         "role": "user", "content": [
    ...             {{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"}},
    ...             {{"type": "text", "text": "Where is the cat standing?"}},
    ...         ]
    ...     }},
    ... ]

    >>> inputs = processor.apply_chat_template(
    ...     messages,
    ...     tokenize=True,
    ...     return_dict=True,
    ...     return_tensors="pt",
    ...     add_generation_prompt=True
    ... )
    >>> # Generate
    >>> generate_ids = model.generate(**inputs)
    >>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0]
    ```
útext-to-audio-spectrogramútext-to-audio-waveformúautomatic-speech-recognitionúaudio-frame-classificationúaudio-classificationúaudio-xvectorúimage-text-to-textúdepth-estimationúvideo-classificationúzero-shot-image-classificationúimage-classificationúzero-shot-object-detectionúobject-detectionúimage-segmentationúimage-feature-extractionútext-generationútable-question-answeringúdocument-question-answeringúnext-sentence-predictionúmultiple-choiceútext-classificationútoken-classificationú	fill-maskúmask-generationÚpretrainingc                óœ   € VP                  4        F7  w  r#Ve   K  RV,           R,           p\        P                  ! RV R2RV 4      p K9  	  V # )zg
Removes the lines testing an output with the doctest syntax in a code sample when it's set to `None`.
Ú{Ú}z\n([^\n]+)\n\s+z\nrG   )ÚitemsrA   rL   )r9   ÚkwargsÚkeyÚvalueÚdoc_keys   &,   r   Úfilter_outputs_from_exampler”   »  sO   € ð —l‘l–n‰
ˆØÒÙà˜•)˜c•/ˆÜ—F’F˜o¨g¨Y°bÐ9¸4ÀÓKŠ	ñ %ð Ðr   Úprocessor_classÚ
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        V,           pV P                  ;'       g    R$R$P                  S
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À|ÐS`ÐaiÐ`jÐjlÐ4móˆIð Õ*¨YÕ6ˆŒ
Øˆ	r   r   )r•   r–   r\   rT   r—   r˜   r™   rš   r›   rœ   r�   rž   rŸ   r   r   s   dddddddddddddj r   Úadd_code_sample_docstringsr¸   É  s   ÿý€ ÷ I÷ Iò IðV Ðr   c                 ó   a a€ VV 3R  lpV# )c                 ó€  <€ V P                   pVP                  R 4      p^ pV\        V4      8  d+   \        P                  ! RW#,          4      f   V^,          pK:  V\        V4      8  d=   \        \        W#,          4      4      p\        SSVR7      W#&   R P                  V4      pM\        RV  RV 24      hWn         V # )rG   z^\s*Returns?:\s*$)r]   zThe function ze should have an empty 'Return:' or 'Returns:' in its docstring as placeholder, current docstring is:
)	r   r+   r	   rA   rB   rE   rd   r   rV   )r   r´   ra   rP   r/   rT   r\   s   &    €€r   r   Ú6replace_return_docstrings.<locals>.docstring_decorator(  s°   ø€ Ø—:‘:ˆØ—‘˜tÓ$ˆØˆØ”#�e“*Œn¤§¢Ð+?ÀÅÓ!JÒ!RØ��FŠAØŒs�5‹zŒ>Üœ U¥XÓ.Ó/ˆFÜ1°+¸|ÐX^Ô_ˆE‰HØ—y‘y Ó'‰HäØ ˜tð $*Ø*2¨ð5óð ð Œ
Øˆ	r   r   )r\   rT   r   s   ff r   Úreplace_return_docstringsr¼   '  s   ù€ öð$ Ðr   c                ó  € \         P                  ! V P                  V P                  V P                  V P
                  V P                  R7      p\        \         P                  \        P                  ! W4      4      pV P                  Vn
        V# )zReturns a copy of a function f.)ÚnameÚargdefsÚclosure)ÚtypesÚFunctionTypeÚ__code__Ú__globals__rW   Ú__defaults__Ú__closure__r   Ú	functoolsÚupdate_wrapperÚ__kwdefaults__)ÚfÚgs   & r   Ú	copy_funcrÌ   =  sc   € ô 	×Ò˜1Ÿ:™: q§}¡}¸1¿:¹:ÈqÏ~É~Ðgh×gtÑgtÔu€AÜŒU×Ñ¤×!9Ò!9¸!Ó!?Ó@€AØ×'Ñ'€AÔØ€Hr   )NT))Ú+MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMESrs   )Ú(MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMESrt   )Ú(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMESru   )ÚMODEL_FOR_CTC_MAPPING_NAMESru   )Ú2MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMESrv   )Ú,MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMESrw   )Ú%MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMESrx   )Ú*MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMESry   )Ú(MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMESrz   )Ú,MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMESr{   )Ú6MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMESr|   )Ú,MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMESr}   )Ú2MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMESr~   )Ú(MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMESr   )Ú*MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMESr€   )ÚMODEL_FOR_IMAGE_MAPPING_NAMESr�   )Ú!MODEL_FOR_CAUSAL_LM_MAPPING_NAMESr‚   )Ú0MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMESrƒ   )Ú3MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMESr„   )Ú0MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMESr…   )Ú'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMESr†   )Ú/MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMESr‡   )Ú,MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMESrˆ   )Ú!MODEL_FOR_MASKED_LM_MAPPING_NAMESr‰   )Ú'MODEL_FOR_MASK_GENERATION_MAPPING_NAMESrŠ   )Ú#MODEL_FOR_PRETRAINING_MAPPING_NAMESr‹   )NN)Dr   rÇ   r   rA   r.   rÁ   Úcollectionsr   Útypingr   r   r   r;   r?   rY   rE   rQ   rd   r®   ÚPT_TOKEN_CLASSIFICATION_SAMPLEÚPT_QUESTION_ANSWERING_SAMPLEÚ!PT_SEQUENCE_CLASSIFICATION_SAMPLEÚPT_MASKED_LM_SAMPLEÚPT_BASE_MODEL_SAMPLEÚPT_MULTIPLE_CHOICE_SAMPLEÚPT_CAUSAL_LM_SAMPLEÚPT_SPEECH_BASE_MODEL_SAMPLEÚPT_SPEECH_CTC_SAMPLEÚPT_SPEECH_SEQ_CLASS_SAMPLEÚPT_SPEECH_FRAME_CLASS_SAMPLEÚPT_SPEECH_XVECTOR_SAMPLEÚPT_VISION_BASE_MODEL_SAMPLEÚPT_VISION_SEQ_CLASS_SAMPLEr­   Ú TEXT_TO_AUDIO_SPECTROGRAM_SAMPLEÚTEXT_TO_AUDIO_WAVEFORM_SAMPLEÚ!AUDIO_FRAME_CLASSIFICATION_SAMPLEÚAUDIO_XVECTOR_SAMPLEÚDEPTH_ESTIMATION_SAMPLEÚVIDEO_CLASSIFICATION_SAMPLEÚ!ZERO_SHOT_OBJECT_DETECTION_SAMPLEÚIMAGE_TO_IMAGE_SAMPLEÚIMAGE_FEATURE_EXTRACTION_SAMPLEÚ"DOCUMENT_QUESTION_ANSWERING_SAMPLEÚNEXT_SENTENCE_PREDICTION_SAMPLEÚMULTIPLE_CHOICE_SAMPLEÚPRETRAINING_SAMPLEÚMASK_GENERATION_SAMPLEÚ VISUAL_QUESTION_ANSWERING_SAMPLEÚTEXT_GENERATION_SAMPLEÚIMAGE_CLASSIFICATION_SAMPLEÚIMAGE_SEGMENTATION_SAMPLEÚFILL_MASK_SAMPLEÚOBJECT_DETECTION_SAMPLEÚQUESTION_ANSWERING_SAMPLEÚTEXT_CLASSIFICATION_SAMPLEÚTABLE_QUESTION_ANSWERING_SAMPLEÚTOKEN_CLASSIFICATION_SAMPLEÚAUDIO_CLASSIFICATION_SAMPLEÚ#AUTOMATIC_SPEECH_RECOGNITION_SAMPLEÚ%ZERO_SHOT_IMAGE_CLASSIFICATION_SAMPLEÚ$IMAGE_TEXT_TO_TEXT_GENERATION_SAMPLEÚ#PIPELINE_TASKS_TO_SAMPLE_DOCSTRINGSÚMODELS_TO_PIPELINEr”   r¸   r¼   rÌ   r   r   r   Ú<module>r     s¸  ðñó Û Û 	Û Û Ý #Ý ò"òò!òHðÐ ò8òô41ðhÐ ð"Ð ðB  Ð ðD8%Ð !ðtÐ ð@Ð ð"Ð ð0Ð ð"Ð ð4!Ð ðF!Ð ðH Ð ð:!Ð ðFÐ ð2Ð ð8 Ð?ØÐ5ØÐ9ØÐ/ØÐ#ØÐ!ØÐ%ØÐ2Ø	ÐØÐ5ØÐ <ØÐ,ØÐ2ØÐ5ðÐ ð$$Ð  ð$!Ð ð" %AÐ !ð 0Ð ð Ð ðFÐ ð%Ð !ðÐ ð#Ð ð&Ð "ð#Ð ð 3Ð ðÐ ðÐ ð$Ð  ðÐ ð 9Ð ðÐ ðÐ ðÐ ð 9Ð ð ?Ð ð#Ð ð =Ð ð 9Ð ð ';Ð #ð)Ð %ð(Ð $ñB '2à	$Ð&FÐGØ	!Ð#@ÐAØ	'Ð)LÐMØ	%Ð'HÐIØ	Ð!<Ð=Ø	Ð.Ð/Ø	ÐCÐDØ	Ð4Ð5Ø	Ð!<Ð=Ø	)Ð+PÐQØ	Ð!<Ð=Ø	%Ð'HÐIØ	Ð4Ð5Ø	Ð8Ð9Ø	#Ð%DÐEØ	Ð2Ð3Ø	#Ð%DÐEØ	&Ð(JÐKØ	#Ð%DÐEØ	Ð2Ð3Ø	Ð :Ð;Ø	Ð!<Ð=Ø	Ð&Ð'Ø	Ð2Ð3Ø	Ð*Ð+ð3ó'Ð #ñ@ !òó Ð òFð[àð[ð ð[ð ð	[ð
 ð[ð 
ð[ð ð[ð ð[ð ð[ð ð[ð ð[ð ð[ð ð[ð ô[ô|ô,r   