
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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Dict       )  ->     int:
						return 1.0 / (1.0 + np.exp(-_outputs       ))




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]       )  ->     Tuple:
						_lowercase					       =					np.max(_outputs		,   axis=-1		,   keepdims=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					np.exp(_outputs - maxes       )
						return shifted_exp / shifted_exp.sum(axis=-1		,   keepdims=SCREAMING_SNAKE_CASE_       )



class    a_ (			__lowerCamelCase    ):
			a	:			Dict					   =							'sigmoid'
			a	:			Any					   =							'softmax'
			a	:			Union[str, Any]					   =							'none'







@add_end_docstrings(
    __lowerCamelCase					,			R'''\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    '''					,			)
class    a_ (			__lowerCamelCase    ):
			a	:			List[Any]					   =							False
			a	:			Any					   =							ClassificationFunction.NONE






			def __init__(      self				,			**__UpperCamelCase  ):
									super().__init__(**lowerCamelCase__  )

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=""				,			**__UpperCamelCase  ):
									_lowercase					       =					tokenizer_kwargs

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

									if isinstance(lowerCamelCase__				,			lowerCamelCase__  ) or top_k is None:
															_lowercase					       =					top_k
															_lowercase					       =					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`."""				,			lowerCamelCase__				,			)
															if return_all_scores:
																					_lowercase					       =					None
															else:
																					_lowercase					       =					1

									if isinstance(lowerCamelCase__				,			lowerCamelCase__  ):
															_lowercase					       =					ClassificationFunction[function_to_apply.upper()]

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






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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					self.framework
									if isinstance(lowerCamelCase__				,			lowerCamelCase__  ):
															return self.tokenizer(**lowerCamelCase__				,			return_tensors=lowerCamelCase__				,			**lowerCamelCase__  )
									elif isinstance(lowerCamelCase__				,			lowerCamelCase__  ) and len(lowerCamelCase__  ) == 1 and isinstance(inputs[0]				,			lowerCamelCase__  ) 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=lowerCamelCase__				,			**lowerCamelCase__  )
									elif isinstance(lowerCamelCase__				,			lowerCamelCase__  ):
															# 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(lowerCamelCase__				,			return_tensors=lowerCamelCase__				,			**lowerCamelCase__  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									return self.model(**lowerCamelCase__  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase=None				,			__UpperCamelCase=1				,			__UpperCamelCase=True  ):





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

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

									if function_to_apply == ClassificationFunction.SIGMOID:
															_lowercase					       =					sigmoid(lowerCamelCase__  )
									elif function_to_apply == ClassificationFunction.SOFTMAX:
															_lowercase					       =					softmax(lowerCamelCase__  )
									elif function_to_apply == ClassificationFunction.NONE:
															_lowercase					       =					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()}

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


def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 100   ):
      '''simple docstring'''







      A_							       =		0
      A_							       =		0
      for i in range(1		,				n + 1   ):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
      print(f'{solution() = }')





