+
    ~j                     r   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)OrderedDict)castc                    \         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).)inspectisclass	getsource
splitlineslenlstrip)funcsource
first_linefunction_def_levels   &   k/Users/mitch_tango/dev/rabbit-r1-livekit/agent/.venv/lib/python3.14/site-packages/transformers/utils/doc.pyget_docstring_indentation_levelr      sb     tt$F""$Q'JZ3z/@/@/B+CC!!!    c                     a  V 3R  lpV# )c                 v   < R P                  S4      V P                  e   V P                  MR ,           V n        V # ) )join__doc__fndocstrs   &r   docstring_decorator1add_start_docstrings.<locals>.docstring_decorator'   s,    WWV_bjj6L

RTU
	r    r   r   s   j r   add_start_docstringsr   &        r   c                     a  V 3R  lpV# )c                   < 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 Li ; iu upi )z[`.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   R   "   T F  qP                  4       R 8w  g   K  Vx  K  	  R# 5i)r   N)strip).0lines   & r   	<genexpr>Uadd_start_docstrings_to_model_forward.<locals>.docstring_decorator.<locals>.<genexpr>?   s#     "c4LDPZPZP\`bPb444Ls   '
' )__qualname__splitr   r   nextr   r	   r
   StopIterationtextwrapindentdedentr   )r   
class_nameintrocorrect_indentationcurrent_docfirst_non_emptydoc_indentationdocsdoc	docstringr   s   &         r   r   Badd_start_docstrings_to_model_forward.<locals>.docstring_decorator/   s8   "////4Q78;
j\ 	* 	 >bA$&JJ$:bjj	2""cK4J4J4L"ccO!/2S9O9O9Q5RRO  a"555`fg`fY\HOOHOOC$8#@S:ST`fDgOOHOOE$:CBU<UVEGGDMK/	&
	  	21O	2 hs   A	E	 3;E	EEr   r   s   j r   %add_start_docstrings_to_model_forwardr;   .   s    @ r   c                     a  V 3R  lpV# )c                 v   < V P                   e   V P                   MRRP                  S4      ,           V n         V # )Nr   )r   r   r   s   &r   r   /add_end_docstrings.<locals>.docstring_decoratorS   s+    $&JJ$:bjjbggfoU
	r   r   r   s   j r   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                j    \         P                  ! RV 4      pVf   R# VP                  4       ^ ,          # )z.Returns the indentation in the first line of tz^(\s*)\Sr   )researchgroups)trB   s   & r   _get_indentrE   c   s,    YY{A&F27V]]_Q%77r   c                   \        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   
N:   NNz^(\s+)(\S+)(\s+)z\1- **\2**\3z:\s*\n\s*(\S)z -- \1)rE   r+   r	   appendrangerA   subr   )output_args_docr/   blockscurrent_blockr&   is   &     r   _convert_output_args_docrQ   i   s     )FFM%%d+t&=!A%mCR01#fBKM Rz_,M , MM-$% 3v;FF.K	FF+Y	B	   99Vr   c                   V P                   pRpVe   VP                  R4      p^ pV\        V4      8  d+   \        P                  ! RWg,          4      f   V^,          pK:  V\        V4      8  d'   RP                  Wg^,           R 4      p\        V4      pM!V'       d   \        RV P                   R24      hV'       d3   V P                   RV P                   2p\        P                  WR7      p	M\        V 4      pRV R	2p	Ve
   V	R
,          p	T	p
Ve	   W,          p
Ve   V
P                  R4      p^ p\        Wg,          4      ^ 8X  d   V^,          pK!  \        \        Wg,          4      4      pW8  dH   RW+,
          ,          pV Uu. uF  p\        V4      ^ 8  d   V V 2MTNK  	  ppRP                  V4      p
V
# u upi )z@
Prepares the return part of the docstring using `output_type`.
NrG   z^\s*(Args|Parameters):\s*$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"   )full_output_typeconfig_classz
Returns:
    ``z:
r)   )r   r+   r	   rA   rB   r   rQ   
ValueError__name__
__module__PT_RETURN_INTRODUCTIONformatstrrE   )output_typerT   
min_indent	add_introoutput_docstringparams_docstringlinesrP   rS   r2   resultr/   to_addr&   s   &&&&          r   _prepare_output_docstringsrd      s    #**# &&t,#e*n+H%(!S![FAs5z>#yyAy)9:78HIRS^SgSgRh iG G  )445Q{7K7K6LM&--?O-k{+#$4#5Q7'UNEF#" T"%(mq FA[*+J/0FPUVPU3t9q='dBPUEVYYu%FM Ws   !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
    ```
a  
    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}
    ```
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(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}
    ```
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(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}
    ```
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():
    ...     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}
    ```
SequenceClassificationQuestionAnsweringTokenClassificationMultipleChoiceMaskedLMLMHead	BaseModelSpeechBaseModelCTCAudioClassificationAudioFrameClassificationAudioXVectorVisionBaseModel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
    ```
a  
    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]
    ```
text-to-audio-spectrogramtext-to-audio-waveformautomatic-speech-recognitionaudio-frame-classificationaudio-classificationaudio-xvectorimage-text-to-textdepth-estimationvideo-classificationzero-shot-image-classificationimage-classificationzero-shot-object-detectionobject-detectionimage-segmentationimage-feature-extractiontext-generationtable-question-answeringdocument-question-answeringnext-sentence-predictionmultiple-choicetext-classificationtoken-classification	fill-maskmask-generationpretrainingc                    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`.
{}z\n([^\n]+)\n\s+z\nrG   )itemsrA   rL   )r9   kwargskeyvaluedoc_keys   &,   r   filter_outputs_from_exampler     sO     lln
)c/FFogYb94K	 % r   processor_class
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aaa VVVV
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SRSRR/pRV9   g   RV9   d   SR8X  d   VR,          pEMRV9   d   VR,          pEMRV9   d   VR,          pMRV9   d   VR,          pMRV9   d   VR,          pMRV9   g   VR+9   d   VR,          pMRV9   g   RV9   d   VR,          pMRV9   d   VR,          pMRV9   d   VR,          pMRV9   d   SR8X  d   VR,          pMgRV9   d   SR8X  d   VR,          pMORV9   d   SR8X  d   VR,          pM7RV9   g   RV9   d   VR ,          pMR!V9   d   VR!,          pM\        R"V 24      h\	        VSSR#7      pSe   \
        V,           pV P                  ;'       g    R$R$P                  S
4      ,           pSf   R$M\        SS	4      pVP                  ! R,/ VB pSeJ   \        P                  ! R%S4      '       d   \        R&S R'24      hVP                  R(S R)2R(S R*S R)24      pWV,           V,           V n        V # )-Nr"   model_classr   r   r   r   r   r   r   r   fake_checkpointtruez{true}re   rn   audiorf   rg   rh   ri   rj   CausalLMrm   ro   XVectorrp   Modelrl   visionrq   Encoderrk   rr   z#Docstring can't be built for model )r   r   r   z^refs/pr/\\d+zThe provided revision 'zW' is incorrect. It should point to a pull request reference on the hub like 'refs/pr/6'zfrom_pretrained("z")z", revision=")FlaubertWithLMHeadModelXLMWithLMHeadModelr   )r*   r+   PT_SAMPLE_DOCSTRINGSrV   r   FAKE_MODEL_DISCLAIMERr   r   rd   rZ   rA   matchreplace)r   r   sample_docstrings
doc_kwargscode_samplefunc_doc
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	r   r   )r   r   r\   rT   r   r   r   r   r   r   r   r   r   r   r   s   dddddddddddddj r   add_code_sample_docstringsr     s     I I IV r   c                    a a VV 3R  lpV# )c                   < V P                   pVP                  R 4      p^ pV\        V4      8  d+   \        P                  ! RW#,          4      f   V^,          pK:  V\        V4      8  d=   \        \        W#,          4      4      p\        SSVR7      W#&   R P                  V4      pM\        RV  RV 24      hWn         V # )rG   z^\s*Returns?:\s*$)r]   zThe function ze should have an empty 'Return:' or 'Returns:' in its docstring as placeholder, current docstring is:
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	r   r   )r\   rT   r   s   ff r   replace_return_docstringsr   '  s    $ r   c                   \         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.)nameargdefsclosure)typesFunctionType__code____globals__rW   __defaults____closure__r   	functoolsupdate_wrapper__kwdefaults__)fgs   & r   	copy_funcr   =  sc     	1::q}}1::q~~ghgtgtuAU!9!9!!?@A''AHr   )NT))+MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMESrs   )(MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMESrt   )(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMESru   )MODEL_FOR_CTC_MAPPING_NAMESru   )2MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMESrv   ),MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMESrw   )%MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMESrx   )*MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMESry   )(MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMESrz   ),MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMESr{   )6MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMESr|   ),MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMESr}   )2MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMESr~   )(MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMESr   )*MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMESr   )MODEL_FOR_IMAGE_MAPPING_NAMESr   )!MODEL_FOR_CAUSAL_LM_MAPPING_NAMESr   )0MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMESr   )3MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMESr   )0MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMESr   )'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMESr   )/MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMESr   ),MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMESr   )!MODEL_FOR_MASKED_LM_MAPPING_NAMESr   )'MODEL_FOR_MASK_GENERATION_MAPPING_NAMESr   )#MODEL_FOR_PRETRAINING_MAPPING_NAMESr   )NN)Dr   r   r   rA   r.   r   collectionsr   typingr   r   r   r;   r?   rY   rE   rQ   rd   r   PT_TOKEN_CLASSIFICATION_SAMPLEPT_QUESTION_ANSWERING_SAMPLE!PT_SEQUENCE_CLASSIFICATION_SAMPLEPT_MASKED_LM_SAMPLEPT_BASE_MODEL_SAMPLEPT_MULTIPLE_CHOICE_SAMPLEPT_CAUSAL_LM_SAMPLEPT_SPEECH_BASE_MODEL_SAMPLEPT_SPEECH_CTC_SAMPLEPT_SPEECH_SEQ_CLASS_SAMPLEPT_SPEECH_FRAME_CLASS_SAMPLEPT_SPEECH_XVECTOR_SAMPLEPT_VISION_BASE_MODEL_SAMPLEPT_VISION_SEQ_CLASS_SAMPLEr    TEXT_TO_AUDIO_SPECTROGRAM_SAMPLETEXT_TO_AUDIO_WAVEFORM_SAMPLE!AUDIO_FRAME_CLASSIFICATION_SAMPLEAUDIO_XVECTOR_SAMPLEDEPTH_ESTIMATION_SAMPLEVIDEO_CLASSIFICATION_SAMPLE!ZERO_SHOT_OBJECT_DETECTION_SAMPLEIMAGE_TO_IMAGE_SAMPLEIMAGE_FEATURE_EXTRACTION_SAMPLE"DOCUMENT_QUESTION_ANSWERING_SAMPLENEXT_SENTENCE_PREDICTION_SAMPLEMULTIPLE_CHOICE_SAMPLEPRETRAINING_SAMPLEMASK_GENERATION_SAMPLE VISUAL_QUESTION_ANSWERING_SAMPLETEXT_GENERATION_SAMPLEIMAGE_CLASSIFICATION_SAMPLEIMAGE_SEGMENTATION_SAMPLEFILL_MASK_SAMPLEOBJECT_DETECTION_SAMPLEQUESTION_ANSWERING_SAMPLETEXT_CLASSIFICATION_SAMPLETABLE_QUESTION_ANSWERING_SAMPLETOKEN_CLASSIFICATION_SAMPLEAUDIO_CLASSIFICATION_SAMPLE#AUTOMATIC_SPEECH_RECOGNITION_SAMPLE%ZERO_SHOT_IMAGE_CLASSIFICATION_SAMPLE$IMAGE_TEXT_TO_TEXT_GENERATION_SAMPLE#PIPELINE_TASKS_TO_SAMPLE_DOCSTRINGSMODELS_TO_PIPELINEr   r   r   r   r   r   r   <module>r     s     	   # "!H 841h " B   D8% !t @ " 0 " 4! F! H  :! F 2 8 ?59/#!%2	5 <,25 $$  $! " %A ! 0   F % ! # & "#  3   $    9     9  ? #  =  9  '; #) %( $B '2	$&FG	!#@A	')LM	%'HI	!<=	./	CD	45	!<=	)+PQ	!<=	%'HI	45	89	#%DE	23	#%DE	&(JK	#%DE	23	 :;	!<=	&'	23	*+3' #@ !  F[[ [ 	[
 [ 
[ [ [ [ [ [ [ [ [|,r   