
"""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__		(			__snake_case	)							->		List[str]:


					"""simple docstring"""





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



def       lowerCamelCase__		(			__snake_case	)							->		List[str]:


					"""simple docstring"""





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






class     _UpperCAmelCase(    lowerCamelCase	):
		lowercase__						=      'sigmoid'
		lowercase__						=      'softmax'
		lowercase__						=      '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     _UpperCAmelCase(    lowerCamelCase	):
		lowercase__						=      False
		lowercase__						=      ClassificationFunction.NONE



		def __init__(					self				,		**__a)       ->       Dict:






							'''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 						UpperCAmelCase							(					self				,		__a=None				,		__a=None				,		__a=""				,		**__a)       ->       List[str]:






							'''simple docstring'''
							# 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(__a				,		__a) 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`.'''				,		__a				,		)
												if return_all_scores:
																	_UpperCamelCase							 =  None
												else:
																	_UpperCamelCase							 =  1

							if isinstance(__a				,		__a):
												_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				,		*__a				,		**__a)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  super().__call__(*__a				,		**__a)
							# TODO try and retrieve it in a nicer way from _sanitize_parameters.
							_UpperCamelCase							 =  '''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 						UpperCAmelCase							(					self				,		__a				,		**__a)       ->       Dict[str, GenericTensor]:






							'''simple docstring'''
							_UpperCamelCase							 =  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 						UpperCAmelCase							(					self				,		__a)       ->       Tuple:






							'''simple docstring'''
							return self.model(**__a)




		def 						UpperCAmelCase							(					self				,		__a				,		__a=None				,		__a=1				,		__a=True)       ->       Any:






							'''simple docstring'''
							# `_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(__a)
							elif function_to_apply == ClassificationFunction.SOFTMAX:
												_UpperCamelCase							 =  softmax(__a)
							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(__a)
							]
							if not _legacy:
												dict_scores.sort(key=lambda __a: x["score"]				,		reverse=__a)
												if top_k is not None:
																	_UpperCamelCase							 =  dict_scores[:top_k]
							return dict_scores








"""simple docstring"""





from __future__ import annotations



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int | float:


					"""simple docstring"""





					if len(__snake_case	) == 0:
										raise ValueError('''find_max() arg is an empty sequence'''	)
					if (
					    left >= len(__snake_case	)
					    or left < -len(__snake_case	)
					    or right >= len(__snake_case	)
					    or right < -len(__snake_case	)
					):
										raise IndexError('''list index out of range'''	)
					if left == right:
										return nums[left]
					_UpperCamelCase							 =  (left + right) >> 1  # the middle
					_UpperCamelCase							 =  find_max(__snake_case,     __snake_case,     __snake_case	)  # find max in range[left, mid]
					_UpperCamelCase							 =  find_max(__snake_case,     mid + 1,     __snake_case	)  # find max in range[mid + 1, right]

					return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)






