"""simple docstring"""
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    lowerCAmelCase__    (			_UpperCamelCase       :				List[str]					)					->				Optional[Any]:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			_UpperCamelCase       :				Any					)					->				Optional[int]:





						"""simple docstring"""






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



class   lowerCAmelCase_    (			__lowerCamelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Tuple			  =						'''sigmoid'''
				_lowerCAmelCase     :    List[str]			  =						'''softmax'''
				_lowerCAmelCase     :    Tuple			  =						'''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   lowerCAmelCase_    (			__lowerCamelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						False
				_lowerCAmelCase     :    Any			  =						ClassificationFunction.NONE

				def __init__(				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										super().__init__(**a_    )

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

				def 		snake_case (				self		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=""		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			tokenizer_kwargs

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

										if isinstance(a_		,    a_    ) or top_k is None:
																snake_case								=			top_k
																snake_case								=			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`.'		,    a_		,    )
																if return_all_scores:
																						snake_case								=			None
																else:
																						snake_case								=			1

										if isinstance(a_		,    a_    ):
																snake_case								=			ClassificationFunction[function_to_apply.upper()]

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

				def __call__(				self		,    *lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.framework
										if isinstance(a_		,    a_    ):
																return self.tokenizer(**a_		,    return_tensors=a_		,    **a_    )
										elif isinstance(a_		,    a_    ) and len(a_    ) == 1 and isinstance(inputs[0]		,    a_    ) 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=a_		,    **a_    )
										elif isinstance(a_		,    a_    ):
																# 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(a_		,    return_tensors=a_		,    **a_    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self.model(**a_    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase=None		,    lowerCAmelCase=1		,    lowerCAmelCase=True    ):






										"""simple docstring"""

										if function_to_apply is None:
																if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																						snake_case								=			ClassificationFunction.SIGMOID
																elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																						snake_case								=			ClassificationFunction.SOFTMAX
																elif hasattr(self.model.config		,    'function_to_apply'    ) and function_to_apply is None:
																						snake_case								=			self.model.config.function_to_apply
																else:
																						snake_case								=			ClassificationFunction.NONE

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

										if function_to_apply == ClassificationFunction.SIGMOID:
																snake_case								=			sigmoid(a_    )
										elif function_to_apply == ClassificationFunction.SOFTMAX:
																snake_case								=			softmax(a_    )
										elif function_to_apply == ClassificationFunction.NONE:
																snake_case								=			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()}

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



"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			path_or_paths
										snake_case								=			split if split or isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else 'train'
										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass


