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Doc utilities: Utilities related to documentation
    N)OrderedDictc                     t        j                  |       ryt        j                  |       }|j                         d   }t	        |      t	        |j                               z
  }d|z   S )z^Return the indentation level of the start of the docstring of a class or function (or method).   r   )inspectisclass	getsource
splitlineslenlstrip)funcsource
first_linefunction_def_levels       i/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/utils/doc.pyget_docstring_indentation_levelr      s_     tt$F""$Q'JZ3z/@/@/B+CC!!!    c                        fd}|S )Nc                 j    dj                        | j                  | j                  ndz   | _        | S N )join__doc__fndocstrs    r   docstring_decoratorz1add_start_docstrings.<locals>.docstring_decorator&   s,    WWV_bjj6L

RTU
	r    r   r   s   ` r   add_start_docstringsr   %        r   c                        fd}|S )Nc                 t   d| j                   j                  d      d    d}d| d}t        |       }| j                  | j                  nd}	 t	        d |j                         D              }t        |      t        |j                               z
  }
}|d	|z   k(  re
D cg c].  }t        j                  t        j                  |      d
|z        0 }}t        j                  t        j                  |      d
|z        }dj                  |      |z   }	||	z   | _        | S # t        $ r |}Y w xY wc c}w )Nz[`.r   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   H   K   | ]  }|j                         d k7  s|  yw)r   N)strip).0lines     r   	<genexpr>zUadd_start_docstrings_to_model_forward.<locals>.docstring_decorator.<locals>.<genexpr>>   s"     "cDPTPZPZP\`bPb4"cs   ""r    )__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   zBadd_start_docstrings_to_model_forward.<locals>.docstring_decorator.   s7   "////4Q78;
j\ 	* 	 >bA$&JJ$:bjj	2""cK4J4J4L"ccO!/2S9O9O9Q5RRO  a"555`fgY\HOOHOOC$8#@S:STgDgOOHOOE$:CBU<UVEGGDMK/	Y&
	  	21O	2 hs   AD$ #3D5$D21D2r   r   s   ` r   %add_start_docstrings_to_model_forwardr:   -   s    @ r   c                        fd}|S )Nc                 j    | j                   | j                   nddj                        z   | _         | S r   )r   r   r   s    r   r   z/add_end_docstrings.<locals>.docstring_decoratorR   s+    $&JJ$:bjjbggfoU
	r   r   r   s   ` r   add_end_docstringsr=   Q   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.

a*  
    Returns:
        [`{full_output_type}`] or `tuple(tf.Tensor)`: A [`{full_output_type}`] or a tuple of `tf.Tensor` (if
        `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
        configuration ([`{config_class}`]) and inputs.

c                 \    t        j                  d|       }|dS |j                         d   S )z.Returns the indentation in the first line of tz^(\s*)\Sr   r   )researchgroups)tr@   s     r   _get_indentrC   k   s,    YY{A&F27V]]_Q%77r   c                    t        |       }g }d}| j                  d      D ]C  }t        |      |k(  r(t        |      dkD  r|j                  |dd        | d}9||dd  dz  }E |j                  |dd        t	        t        |            D ]<  }t        j                  dd||         ||<   t        j                  d	d
||         ||<   > dj                  |      S )z,Convert output_args_doc to display properly.r   
r   N   z^(\s+)(\S+)(\s+)z\1- **\2**\3z:\s*\n\s*(\S)z -- \1)rC   r+   r
   appendranger?   subr   )output_args_docr/   blockscurrent_blockr'   is         r   _convert_output_args_docrO   q   s     )FFM%%d+ 	-t&=!A%mCR01#fBKM QRz_,M	- MM-$% 3v; CFF.Kq	FF+Yq	Bq	C 99Vr   c                    | j                   }d}||j                  d      }d}|t        |      k  rFt        j                  d||         -|dz  }|t        |      k  rt        j                  d||         -|t        |      k  r#dj                  ||dz   d       }t        |      }n|rt        d| j                   d      |rV| j                   d| j                   }| j                  j                  d	      rt        nt        }	|	j                  ||
      }	nt        |       }d| d}	||	dz  }	|	}
||
|z  }
||
j                  d      }d}t        ||         dk(  r|dz  }t        ||         dk(  rt        t        ||               }||k  r<d||z
  z  }|D cg c]  }t        |      dkD  r| | n| }}dj                  |      }
|
S c c}w )zH
    Prepares the return part of the docstring using `output_type`.
    NrE   r   z^\s*(Args|Parameters):\s*$   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#   TF)full_output_typeconfig_classz
Returns:
    ``z:
r)   )r   r+   r
   r?   r@   r   rO   
ValueError__name__
__module__
startswithTF_RETURN_INTRODUCTIONPT_RETURN_INTRODUCTIONformatstrrC   )output_typerT   
min_indent	add_introoutput_docstringparams_docstringlinesrN   rS   r2   resultr/   to_addr'   s                 r   _prepare_output_docstringsrf      s"    #**# &&t,#e*n+H%PQ(!S![FA #e*n+H%PQ(!S![s5z>#yyAy)9:78HIRS^SgSgRh iG G  )445Q{7K7K6LM*5*>*>*I*I$*O&Uk.>\Z{+#$4#5Q7'UNEF#"" T"%(mq FA %(mq [q*+JJ/0FPUV3t9q='dBVEVYYu%FM Ws   "G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 PIL import Image
    >>> import requests
    >>> from transformers import AutoProcessor, {model_class}

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

    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)

    >>> inputs = processor(images=image, return_tensors="pt")

    >>> outputs = model(**inputs)
    ```
a  
    Example:

    ```python
    >>> from transformers import AutoImageProcessor, {model_class}
    >>> import torch
    >>> from PIL import Image
    >>> import requests

    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)

    >>> 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
    >>> import requests
    >>> 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image-to-textvisual-question-answeringdepth-estimationvideo-classificationzero-shot-image-classificationimage-classificationzero-shot-object-detectionobject-detectionimage-segmentationimage-to-imageimage-feature-extractiontext-generationtable-question-answeringdocument-question-answeringquestion-answeringtext2text-generationnext-sentence-predictionmultiple-choicetext-classificationtoken-classification	fill-maskmask-generationpretraining))+MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMESru   )(MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMESrv   )(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMESrw   )MODEL_FOR_CTC_MAPPING_NAMESrw   )2MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMESrx   ),MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMESry   )%MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMESrz   )*MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMESr{   )$MODEL_FOR_VISION_2_SEQ_MAPPING_NAMESr|   )1MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMESr}   )(MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMESr~   ),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_TO_IMAGE_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   )*MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMESr   ),MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_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   aI  
    Example:

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import tensorflow as tf

    >>> 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="tf"
    ... )

    >>> logits = model(**inputs).logits
    >>> predicted_token_class_ids = tf.math.argmax(logits, axis=-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] for t in predicted_token_class_ids[0].numpy().tolist()]
    >>> predicted_tokens_classes
    {expected_output}
    ```

    ```python
    >>> labels = predicted_token_class_ids
    >>> loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)
    >>> round(float(loss), 2)
    {expected_loss}
    ```
a  
    Example:

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import tensorflow as tf

    >>> 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="tf")
    >>> outputs = model(**inputs)

    >>> answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
    >>> answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])

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

    ```python
    >>> # target is "nice puppet"
    >>> target_start_index = tf.constant([{qa_target_start_index}])
    >>> target_end_index = tf.constant([{qa_target_end_index}])

    >>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
    >>> loss = tf.math.reduce_mean(outputs.loss)
    >>> round(float(loss), 2)
    {expected_loss}
    ```
a  
    Example:

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import tensorflow as tf

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

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

    >>> logits = model(**inputs).logits

    >>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
    >>> model.config.id2label[predicted_class_id]
    {expected_output}
    ```

    ```python
    >>> # 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 = tf.constant(1)
    >>> loss = model(**inputs, labels=labels).loss
    >>> round(float(loss), 2)
    {expected_loss}
    ```
a4  
    Example:

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import tensorflow as tf

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

    >>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="tf")
    >>> logits = model(**inputs).logits

    >>> # retrieve index of {mask}
    >>> mask_token_index = tf.where((inputs.input_ids == tokenizer.mask_token_id)[0])
    >>> selected_logits = tf.gather_nd(logits[0], indices=mask_token_index)

    >>> predicted_token_id = tf.math.argmax(selected_logits, axis=-1)
    >>> tokenizer.decode(predicted_token_id)
    {expected_output}
    ```

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

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

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import tensorflow as tf

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

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

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

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import tensorflow as tf

    >>> 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."

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

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

    ```python
    >>> from transformers import AutoTokenizer, {model_class}
    >>> import tensorflow as tf

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

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
    >>> outputs = model(inputs)
    >>> logits = outputs.logits
    ```
a
  
    Example:

    ```python
    >>> from transformers import AutoProcessor, {model_class}
    >>> 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="tf")
    >>> 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 tensorflow as tf

    >>> 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="tf")
    >>> logits = model(**inputs).logits
    >>> predicted_ids = tf.math.argmax(logits, axis=-1)

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

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

    >>> # compute loss
    >>> loss = model(**inputs).loss
    >>> round(float(loss), 2)
    {expected_loss}
    ```
aY  
    Example:

    ```python
    >>> from transformers import AutoImageProcessor, {model_class}
    >>> 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="tf")
    >>> 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 tensorflow as tf
    >>> 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="tf")
    >>> logits = model(**inputs).logits

    >>> # model predicts one of the 1000 ImageNet classes
    >>> predicted_label = int(tf.math.argmax(logits, axis=-1))
    >>> print(model.config.id2label[predicted_label])
    {expected_output}
    ```
)rg   rh   ri   rj   rk   rl   rm   rn   ro   rs   rt   ar  
    Example:

    ```python
    >>> 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="jax")

    >>> outputs = model(**inputs)
    >>> logits = outputs.logits
    ```
a  
    Example:

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

    >>> 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="jax")

    >>> outputs = model(**inputs)
    >>> start_scores = outputs.start_logits
    >>> end_scores = outputs.end_logits
    ```
a}  
    Example:

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

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

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

    >>> outputs = model(**inputs)
    >>> logits = outputs.logits
    ```
a  
    Example:

    ```python
    >>> 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="jax")
    >>> outputs = model(**inputs)

    >>> last_hidden_states = outputs.last_hidden_state
    ```
a  
    Example:

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

    >>> 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."

    >>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="jax", padding=True)
    >>> outputs = model(**{{k: v[None, :] for k, v in encoding.items()}})

    >>> logits = outputs.logits
    ```
a  
    Example:

    ```python
    >>> 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="np")
    >>> outputs = model(**inputs)

    >>> # retrieve logts for next token
    >>> next_token_logits = outputs.logits[:, -1]
    ```
)rg   rh   ri   rj   rk   rm   rl   c                     |j                         D ]+  \  }}|	d|z   dz   }t        j                  d| dd|       } - | S )zo
    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\nrE   )itemsr?   rJ   )r9   kwargskeyvaluedoc_keys        r   filter_outputs_from_exampler     sX     lln L
U)c/FFogYb94K	L r   z[MASK]      )processor_class
checkpointr^   rT   maskqa_target_start_indexqa_target_end_index	model_clsmodalityexpected_outputexpected_lossreal_checkpointrevisionc                 F    	
 
	 fd}|S )Nc                    | j                   j                  d      d   n}|d d dk(  rt        }n|d d dk(  rt        }nt        }|dd}d	|v sd
|v rdk(  r|d
   }nd	|v r|d	   }nd|v r|d   }nd|v r|d   }nd|v r|d   }nd|v s|dv r|d   }nud|v sd|v r|d   }ngd|v r|d   }n]d|v r|d   }nSd|v rdk(  r|d   }nDd|v rdk(  r|d   }n5d|v rdk(  r|d   }n&d|v sd|v r|d   }nd|v r|d   }nt        d|       t        |      }	t        |z   }| j                  xs d d j                  
      z   }d nt        	      } |j                  d'i |}Bt        j                  d!      rt        d" d#      |j                  d$ d%d$ d& d%      }||z   |z   | _        | S )(Nr#   r   rG   rR   r   Flaxz{true})model_classr   r   r   r   r   r   r   r   fake_checkpointtruerg   rp   audiorh   ri   rj   rk   )FlaubertWithLMHeadModelXLMWithLMHeadModelrl   CausalLMro   rq   XVectorrr   Modelrn   visionrs   Encoderrm   rt   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="r   )r*   r+   TF_SAMPLE_DOCSTRINGSFLAX_SAMPLE_DOCSTRINGSPT_SAMPLE_DOCSTRINGSrV   r   FAKE_MODEL_DISCLAIMERr   r   rf   r\   r?   matchreplace)r   r   sample_docstrings
doc_kwargscode_samplefunc_doc
output_doc	built_docr   rT   r   r   r   r   r   r   r^   r   r   r   r   r   s           r   r   z7add_code_sample_docstrings.<locals>.docstring_decorator  s   7@7Hboo++C03ir?d" 4!_& 6 4 '.$%:#6.*.)

 %37LP[7[aimtat+,ABK%4+,DEK K/+,?@K"k1+,ABK,+,<=K;&+9j*j+J7K$
k(A+H5Kk!+E2K';6+,FGK+%(g*=+N;K#G(;+,=>K#H(<+,=>K#yK'?+K8K"k1+,ABKB;-PQQ1
 &/+=KJJ$"7&.R4N{\h4i
&K&&44	xx((3 -hZ 8L L  "))#J<r26G
|S`ai`jjl4mI 
*Y6
	r   r   )r   r   r^   rT   r   r   r   r   r   r   r   r   r   r   r   s   `````````````` r   add_code_sample_docstringsr     s     N N N` r   c                       fd}|S )Nc                    | j                   }|j                  d      }d}|t        |      k  rFt        j                  d||         -|dz  }|t        |      k  rt        j                  d||         -|t        |      k  r:t        t        ||               }t        |      ||<   dj                  |      }nt        d|  d|       || _         | S )NrE   r   z^\s*Returns?:\s*$rQ   )r_   zThe function ze should have an empty 'Return:' or 'Returns:' in its docstring as placeholder, current docstring is:
)	r   r+   r
   r?   r@   rC   rf   r   rV   )r   r   rc   rN   r/   rT   r^   s        r   r   z6replace_return_docstrings.<locals>.docstring_decorator  s    ::t$#e*n+?q!J!RFA #e*n+?q!J!Rs5z>U1X./F1+|X^_E!Hyy'Ht $**25  
	r   r   )r^   rT   r   s   `` r   replace_return_docstringsr     s    $ r   c                     t        j                  | j                  | j                  | j                  | j
                  | j                        }t        j                  ||       }| j                  |_	        |S )zReturns a copy of a function f.)nameargdefsclosure)
typesFunctionType__code____globals__rW   __defaults____closure__	functoolsupdate_wrapper__kwdefaults__)fgs     r   	copy_funcr   (  sX     	1::q}}1::q~~ghgtgtuA  A&A''AHr   )NT)NN)Yr   r   r   r?   r.   r   collectionsr   r   r   r:   r=   r[   rZ   rC   rO   rf   r   PT_TOKEN_CLASSIFICATION_SAMPLEPT_QUESTION_ANSWERING_SAMPLE!PT_SEQUENCE_CLASSIFICATION_SAMPLEPT_MASKED_LM_SAMPLEPT_BASE_MODEL_SAMPLEPT_MULTIPLE_CHOICE_SAMPLEPT_CAUSAL_LM_SAMPLEPT_SPEECH_BASE_MODEL_SAMPLEPT_SPEECH_CTC_SAMPLEPT_SPEECH_SEQ_CLASS_SAMPLEPT_SPEECH_FRAME_CLASS_SAMPLEPT_SPEECH_XVECTOR_SAMPLEPT_VISION_BASE_MODEL_SAMPLEPT_VISION_SEQ_CLASS_SAMPLEr    TEXT_TO_AUDIO_SPECTROGRAM_SAMPLETEXT_TO_AUDIO_WAVEFORM_SAMPLE!AUDIO_FRAME_CLASSIFICATION_SAMPLEAUDIO_XVECTOR_SAMPLEIMAGE_TO_TEXT_SAMPLEDEPTH_ESTIMATION_SAMPLEVIDEO_CLASSIFICATION_SAMPLE!ZERO_SHOT_OBJECT_DETECTION_SAMPLEIMAGE_TO_IMAGE_SAMPLEIMAGE_FEATURE_EXTRACTION_SAMPLE"DOCUMENT_QUESTION_ANSWERING_SAMPLENEXT_SENTENCE_PREDICTION_SAMPLEMULTIPLE_CHOICE_SAMPLEPRETRAINING_SAMPLEMASK_GENERATION_SAMPLE VISUAL_QUESTION_ANSWERING_SAMPLETEXT_GENERATION_SAMPLEIMAGE_CLASSIFICATION_SAMPLEIMAGE_SEGMENTATION_SAMPLEFILL_MASK_SAMPLEOBJECT_DETECTION_SAMPLEQUESTION_ANSWERING_SAMPLETEXT2TEXT_GENERATION_SAMPLETEXT_CLASSIFICATION_SAMPLETABLE_QUESTION_ANSWERING_SAMPLETOKEN_CLASSIFICATION_SAMPLEAUDIO_CLASSIFICATION_SAMPLE#AUTOMATIC_SPEECH_RECOGNITION_SAMPLE%ZERO_SHOT_IMAGE_CLASSIFICATION_SAMPLE$IMAGE_TEXT_TO_TEXT_GENERATION_SAMPLE#PIPELINE_TASKS_TO_SAMPLE_DOCSTRINGSMODELS_TO_PIPELINETF_TOKEN_CLASSIFICATION_SAMPLETF_QUESTION_ANSWERING_SAMPLE!TF_SEQUENCE_CLASSIFICATION_SAMPLETF_MASKED_LM_SAMPLETF_BASE_MODEL_SAMPLETF_MULTIPLE_CHOICE_SAMPLETF_CAUSAL_LM_SAMPLETF_SPEECH_BASE_MODEL_SAMPLETF_SPEECH_CTC_SAMPLETF_VISION_BASE_MODEL_SAMPLETF_VISION_SEQ_CLASS_SAMPLEr    FLAX_TOKEN_CLASSIFICATION_SAMPLEFLAX_QUESTION_ANSWERING_SAMPLE#FLAX_SEQUENCE_CLASSIFICATION_SAMPLEFLAX_MASKED_LM_SAMPLEFLAX_BASE_MODEL_SAMPLEFLAX_MULTIPLE_CHOICE_SAMPLEFLAX_CAUSAL_LM_SAMPLEr   r   r   r   r   r   r   r   <module>r7     sM     	   #"!H  842j " B   D8% !t @ " 0 " 4! F! H  :! F 2 8 @59/#!%25 <,25 $$  $! " %A ! 0  * B % ! # & "#  3   $    9     9   ? #  =  9  '; #) %( $D '2	$&FG	!#@A	')LM	%'HI	!<=	./	CD	./	$&FG	45	!<=	)+PQ	!<=	%'HI	45	89	01	#%DE	23	#%DE	&(JK	89	!<=	#%DE	23	 :;	!<=	&'	23	*+=!' #J !#% P" B!  F% !> B " .   0" H , 2 @59/#!%225 $   " $' #      ( $ B7;1%'#   	`F,r   