
import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






def 						__magic_name__ (    __a			:       Dict						):



     '''simple docstring'''




     return 1.0 / (1.0 + np.exp(-_outputs						))






def 						__magic_name__ (    __a			:       int						):



     '''simple docstring'''




     UpperCamelCase__				=				np.max(_outputs       ,		axis=-1       ,		keepdims=_lowercase						)
     UpperCamelCase__				=				np.exp(_outputs - maxes						)
     return shifted_exp / shifted_exp.sum(axis=-1       ,		keepdims=_lowercase						)




class 	__A(				_lowerCAmelCase ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			"""sigmoid"""
  SCREAMING_SNAKE_CASE__			    =			"""softmax"""
  SCREAMING_SNAKE_CASE__			    =			"""none"""






@add_end_docstrings(
    _lowerCAmelCase				,      r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """				,      )
class 	__A(				_lowerCAmelCase ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			False
  SCREAMING_SNAKE_CASE__			    =			ClassificationFunction.NONE







  def __init__(self       ,			**SCREAMING_SNAKE_CASE_						):
       super().__init__(**SCREAMING_SNAKE_CASE_						)

       self.check_model_type(
           TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
           if self.framework == """tf"""
           else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_=None       ,			SCREAMING_SNAKE_CASE_=None       ,			SCREAMING_SNAKE_CASE_=""       ,			**SCREAMING_SNAKE_CASE_						):
       # Using "" as default argument because we're going to use `top_k=None` in user code to declare
       # "No top_k"
       UpperCamelCase__				=				tokenizer_kwargs

       UpperCamelCase__				=				{}
       if hasattr(self.model.config       ,			"""return_all_scores"""						) and return_all_scores is None:
            UpperCamelCase__				=				self.model.config.return_all_scores

       if isinstance(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						) or top_k is None:
            UpperCamelCase__				=				top_k
            UpperCamelCase__				=				False
       elif return_all_scores is not None:
            warnings.warn(
                """`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"""
                """ `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."""       ,			SCREAMING_SNAKE_CASE_       ,			)
            if return_all_scores:
                 UpperCamelCase__				=				None
            else:
                 UpperCamelCase__				=				1

       if isinstance(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=				ClassificationFunction[function_to_apply.upper()]

       if function_to_apply is not None:
            UpperCamelCase__				=				function_to_apply
       return preprocess_params, {}, postprocess_params







  def __call__(self       ,			*SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				super().__call__(*SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						)
       # TODO try and retrieve it in a nicer way from _sanitize_parameters.
       UpperCamelCase__				=				"""top_k""" not in kwargs
       if isinstance(args[0]       ,			SCREAMING_SNAKE_CASE_						) and _legacy:
            # This pipeline is odd, and return a list when single item is run
            return [result]
       else:
            return result







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				self.framework
       if isinstance(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
            return self.tokenizer(**SCREAMING_SNAKE_CASE_       ,			return_tensors=SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						)
       elif isinstance(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						) and len(SCREAMING_SNAKE_CASE_						) == 1 and isinstance(inputs[0]       ,			SCREAMING_SNAKE_CASE_						) and len(inputs[0]						) == 2:
            # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
            return self.tokenizer(
                text=inputs[0][0]       ,			text_pair=inputs[0][1]       ,			return_tensors=SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						)
       elif isinstance(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
            # This is likely an invalid usage of the pipeline attempting to pass text pairs.
            raise ValueError(
                """The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"""
                """ dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."""						)
       return self.tokenizer(SCREAMING_SNAKE_CASE_       ,			return_tensors=SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_						):
       return self.model(**SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_=None       ,			SCREAMING_SNAKE_CASE_=1       ,			SCREAMING_SNAKE_CASE_=True						):
       # `_legacy` is used to determine if we're running the naked pipeline and in backward
       # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
       # the more natural result containing the list.
       # Default value before `set_parameters`
       if function_to_apply is None:
            if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                 UpperCamelCase__				=				ClassificationFunction.SIGMOID
            elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                 UpperCamelCase__				=				ClassificationFunction.SOFTMAX
            elif hasattr(self.model.config       ,			"""function_to_apply"""						) and function_to_apply is None:
                 UpperCamelCase__				=				self.model.config.function_to_apply
            else:
                 UpperCamelCase__				=				ClassificationFunction.NONE

       UpperCamelCase__				=				model_outputs["""logits"""][0]
       UpperCamelCase__				=				outputs.numpy()

       if function_to_apply == ClassificationFunction.SIGMOID:
            UpperCamelCase__				=				sigmoid(SCREAMING_SNAKE_CASE_						)
       elif function_to_apply == ClassificationFunction.SOFTMAX:
            UpperCamelCase__				=				softmax(SCREAMING_SNAKE_CASE_						)
       elif function_to_apply == ClassificationFunction.NONE:
            UpperCamelCase__				=				outputs
       else:
            raise ValueError(F"Unrecognized `function_to_apply` argument: {function_to_apply}"						)

       if top_k == 1 and _legacy:
            return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

       UpperCamelCase__				=				[
           {"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(SCREAMING_SNAKE_CASE_						)
       ]
       if not _legacy:
            dict_scores.sort(key=lambda SCREAMING_SNAKE_CASE_						: x["score"]       ,			reverse=SCREAMING_SNAKE_CASE_						)
            if top_k is not None:
                 UpperCamelCase__				=				dict_scores[:top_k]
       return dict_scores










"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__a      =				"src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
__a      =				direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__a      =				re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__a      =				re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__a      =				re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__a      =				[
    ("pretraining", "MODEL_FOR_PRETRAINING_MAPPING_NAMES", "AutoModelForPreTraining"),
    ("feature-extraction", "MODEL_MAPPING_NAMES", "AutoModel"),
    ("audio-classification", "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForAudioClassification"),
    ("text-generation", "MODEL_FOR_CAUSAL_LM_MAPPING_NAMES", "AutoModelForCausalLM"),
    ("automatic-speech-recognition", "MODEL_FOR_CTC_MAPPING_NAMES", "AutoModelForCTC"),
    ("image-classification", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForImageClassification"),
    ("image-segmentation", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES", "AutoModelForImageSegmentation"),
    ("fill-mask", "MODEL_FOR_MASKED_LM_MAPPING_NAMES", "AutoModelForMaskedLM"),
    ("object-detection", "MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES", "AutoModelForObjectDetection"),
    (
        "zero-shot-object-detection",
        "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES",
        "AutoModelForZeroShotObjectDetection",
    ),
    ("question-answering", "MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForQuestionAnswering"),
    ("text2text-generation", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES", "AutoModelForSeq2SeqLM"),
    ("text-classification", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForSequenceClassification"),
    ("automatic-speech-recognition", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES", "AutoModelForSpeechSeq2Seq"),
    (
        "table-question-answering",
        "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForTableQuestionAnswering",
    ),
    ("token-classification", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES", "AutoModelForTokenClassification"),
    ("multiple-choice", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES", "AutoModelForMultipleChoice"),
    (
        "next-sentence-prediction",
        "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES",
        "AutoModelForNextSentencePrediction",
    ),
    (
        "audio-frame-classification",
        "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForAudioFrameClassification",
    ),
    ("audio-xvector", "MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES", "AutoModelForAudioXVector"),
    (
        "document-question-answering",
        "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForDocumentQuestionAnswering",
    ),
    (
        "visual-question-answering",
        "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForVisualQuestionAnswering",
    ),
    ("image-to-text", "MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES", "AutoModelForVision2Seq"),
    (
        "zero-shot-image-classification",
        "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForZeroShotImageClassification",
    ),
    ("depth-estimation", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES", "AutoModelForDepthEstimation"),
    ("video-classification", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForVideoClassification"),
    ("mask-generation", "MODEL_FOR_MASK_GENERATION_MAPPING_NAMES", "AutoModelForMaskGeneration"),
]
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Any						=			re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""",					_lowercase	)
						return [m.group(0	) for m in matches]
def    A_  (			):







						'''simple docstring'''


						snake_case_ :int						=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						snake_case_ :Dict						=			{
						    config.replace("""Config""",					""""""	): model_type for model_type, config in config_maping_names.items()
						}

						# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
						snake_case_ :Optional[Any]						=			collections.defaultdict(_lowercase	)
						snake_case_ :int						=			collections.defaultdict(_lowercase	)
						snake_case_ :List[str]						=			collections.defaultdict(_lowercase	)

						# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
						for attr_name in dir(_lowercase	):
												snake_case_ :int						=			None
												if _re_tf_models.match(_lowercase	) is not None:
																		snake_case_ :int						=			tf_models
																		snake_case_ :List[str]						=			_re_tf_models.match(_lowercase	).groups()[0]
												elif _re_flax_models.match(_lowercase	) is not None:
																		snake_case_ :List[Any]						=			flax_models
																		snake_case_ :Any						=			_re_flax_models.match(_lowercase	).groups()[0]
												elif _re_pt_models.match(_lowercase	) is not None:
																		snake_case_ :Optional[Any]						=			pt_models
																		snake_case_ :int						=			_re_pt_models.match(_lowercase	).groups()[0]

												if lookup_dict is not None:
																		while len(_lowercase	) > 0:
																								if attr_name in model_prefix_to_model_type:
																														snake_case_ :Optional[int]						=			True
																														break
																								# Try again after removing the last word in the name
																								snake_case_ :Optional[Any]						=			"""""".join(camel_case_split(_lowercase	)[:-1]	)

						snake_case_ :Optional[int]						=			set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
						snake_case_ :Optional[Any]						=			list(_lowercase	)
						all_models.sort()

						snake_case_ :Optional[int]						=			{"""model_type""": all_models}
						snake_case_ :Optional[int]						=			[pt_models[t] for t in all_models]
						snake_case_ :Any						=			[tf_models[t] for t in all_models]
						snake_case_ :Dict						=			[flax_models[t] for t in all_models]

						# Now let's use the auto-mapping names to make sure
						snake_case_ :Dict						=			{}
						for t in all_models:
												if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
																		snake_case_ :Optional[Any]						=			"""AutoProcessor"""
												elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
																		snake_case_ :Tuple						=			"""AutoTokenizer"""
												elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
																		snake_case_ :Tuple						=			"""AutoFeatureExtractor"""
												else:
																		# Default to AutoTokenizer if a model has nothing, for backward compatibility.
																		snake_case_ :str						=			"""AutoTokenizer"""

						snake_case_ :int						=			[processors[t] for t in all_models]

						return pd.DataFrame(_lowercase	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			[
						    transformers_module.models.auto.modeling_auto,
						    transformers_module.models.auto.modeling_tf_auto,
						    transformers_module.models.auto.modeling_flax_auto,
						]
						for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
												snake_case_ :Optional[int]						=			[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
												snake_case_ :List[str]						=			[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
												# Loop through all three frameworks
												for module, cls, mapping in zip(_lowercase,					_lowercase,					_lowercase	):
																		# The type of pipeline may not exist in this framework
																		if not hasattr(_lowercase,					_lowercase	):
																								continue
																		# First extract all model_names
																		snake_case_ :Tuple						=			[]
																		for name in getattr(_lowercase,					_lowercase	).values():
																								if isinstance(_lowercase,					_lowercase	):
																														model_names.append(_lowercase	)
																								else:
																														model_names.extend(list(_lowercase	)	)

            # Add pipeline tag and auto model class for those models
																		table.update({model_name: (pipeline_tag, cls) for model_name in model_names}	)

						return table
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			get_frameworks_table()
						snake_case_ :str						=			Dataset.from_pandas(_lowercase	)

						snake_case_ :List[Any]						=			hf_hub_download(
						    """huggingface/transformers-metadata""",					"""pipeline_tags.json""",					repo_type="""dataset""",					token=_lowercase	)
						snake_case_ :List[str]						=			Dataset.from_json(_lowercase	)
						snake_case_ :int						=			{
						    tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
						    for i in range(len(_lowercase	)	)
						}
						snake_case_ :Optional[int]						=			update_pipeline_and_auto_class_table(_lowercase	)

						# Sort the model classes to avoid some nondeterministic updates to create false update commits.
						snake_case_ :Tuple						=			sorted(table.keys()	)
						snake_case_ :Tuple						=			pd.DataFrame(
						    {
						        """model_class""": model_classes,
						        """pipeline_tag""": [table[m][0] for m in model_classes],
						        """auto_class""": [table[m][1] for m in model_classes],
						    }	)
						snake_case_ :Union[str, Any]						=			Dataset.from_pandas(_lowercase	)

						with tempfile.TemporaryDirectory() as tmp_dir:
												frameworks_dataset.to_json(os.path.join(_lowercase,					"""frameworks.json"""	)	)
												tags_dataset.to_json(os.path.join(_lowercase,					"""pipeline_tags.json"""	)	)

												if commit_sha is not None:
																		snake_case_ :Union[str, Any]						=			(
																		    f"""Update with commit {commit_sha}\n\nSee: """
																		    f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
																		)
												else:
																		snake_case_ :List[Any]						=			"""Update"""

												upload_folder(
												    repo_id="""huggingface/transformers-metadata""",					folder_path=_lowercase,					repo_type="""dataset""",					token=_lowercase,					commit_message=_lowercase,					)
def    A_  (			):







						'''simple docstring'''


						snake_case_ :List[Any]						=			{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
						snake_case_ :Dict						=			transformers_module.pipelines.SUPPORTED_TASKS
						snake_case_ :List[str]						=			[]
						for key in pipeline_tasks:
												if key not in in_table:
																		snake_case_ :int						=			pipeline_tasks[key]["""pt"""]
																		if isinstance(_lowercase,					(list, tuple)	):
																								snake_case_ :Any						=			model[0]
																		snake_case_ :str						=			model.__name__
																		if model not in in_table.values():
																								missing.append(_lowercase	)

						if len(_lowercase	) > 0:
												snake_case_ :Optional[int]						=			""", """.join(_lowercase	)
												raise ValueError(
												    """The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
												    f"""`utils/update_metadata.py`: {msg}. Please add them!"""	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.")
						parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.")
						parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.")
						__a      =				parser.parse_args()

						if args.check_only:
												check_pipeline_tags()
						else:
												update_metadata(args.token, args.commit_sha)
