



"""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   snake_case__     (	__lowerCamelCase       :					int			):
			"""simple docstring"""
			return 1.0 / (1.0 + np.exp(-_outputs			))






def   snake_case__     (	__lowerCamelCase       :					List[str]			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[int]				=np.max(_outputs							,	axis=-1							,	keepdims=__lowerCamelCase			)
			lowerCamelCase__	:	Any				=np.exp(_outputs - maxes			)
			return shifted_exp / shifted_exp.sum(axis=-1							,	keepdims=__lowerCamelCase			)

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							'sigmoid'
					_a         =							'softmax'
					_a         =							'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  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							False
					_a         =							ClassificationFunction.NONE



					def __init__( self							:						Optional[int],						**lowerCamelCase							:						str  )->      List[str]:
								super().__init__(**lowerCamelCase  )

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



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						Tuple=None,						lowerCamelCase							:						Optional[Any]=None,						lowerCamelCase							:						int="",						**lowerCamelCase							:						Union[str, Any]  )->      Any:
								# Using "" as default argument because we're going to use `top_k=None` in user code to declare
								# "No top_k"
								lowerCamelCase__	:	Union[str, Any]				=tokenizer_kwargs

								lowerCamelCase__	:	Optional[int]				={}
								if hasattr(self.model.config,						'''return_all_scores'''  ) and return_all_scores is None:
											lowerCamelCase__	:	Dict				=self.model.config.return_all_scores

								if isinstance(lowerCamelCase,						lowerCamelCase  ) or top_k is None:
											lowerCamelCase__	:	str				=top_k
											lowerCamelCase__	:	str				=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:
														lowerCamelCase__	:	int				=None
											else:
														lowerCamelCase__	:	Optional[int]				=1

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

								if function_to_apply is not None:
											lowerCamelCase__	:	List[str]				=function_to_apply
								return preprocess_params, {}, postprocess_params



					def __call__( self							:						int,						*lowerCamelCase							:						Dict,						**lowerCamelCase							:						Tuple  )->      Union[str, Any]:
								lowerCamelCase__	:	str				=super().__call__(*lowerCamelCase,						**lowerCamelCase  )
								# TODO try and retrieve it in a nicer way from _sanitize_parameters.
								lowerCamelCase__	:	Union[str, Any]				='''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 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						str,						**lowerCamelCase							:						Dict  )->      Dict[str, GenericTensor]:
								lowerCamelCase__	:	Union[str, Any]				=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 	snake_case ( self							:						Optional[Any],						lowerCamelCase							:						Tuple  )->      int:
								return self.model(**lowerCamelCase  )




					def 	snake_case ( self							:						Optional[Any],						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Optional[Any]=None,						lowerCamelCase							:						Optional[Any]=1,						lowerCamelCase							:						List[Any]=True  )->      List[str]:
								# `_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:
														lowerCamelCase__	:	List[str]				=ClassificationFunction.SIGMOID
											elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
														lowerCamelCase__	:	Optional[int]				=ClassificationFunction.SOFTMAX
											elif hasattr(self.model.config,						'''function_to_apply'''  ) and function_to_apply is None:
														lowerCamelCase__	:	Tuple				=self.model.config.function_to_apply
											else:
														lowerCamelCase__	:	str				=ClassificationFunction.NONE

								lowerCamelCase__	:	int				=model_outputs['''logits'''][0]
								lowerCamelCase__	:	Tuple				=outputs.numpy()

								if function_to_apply == ClassificationFunction.SIGMOID:
											lowerCamelCase__	:	List[str]				=sigmoid(lowerCamelCase  )
								elif function_to_apply == ClassificationFunction.SOFTMAX:
											lowerCamelCase__	:	int				=softmax(lowerCamelCase  )
								elif function_to_apply == ClassificationFunction.NONE:
											lowerCamelCase__	:	List[str]				=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()}

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









"""simple docstring"""






_lowercase				:				str   		=		0  # The first color of the flag.
_lowercase				:				Dict   		=		1  # The second color of the flag.
_lowercase				:				Tuple   		=		2  # The third color of the flag.
_lowercase				:				Optional[int]   		=		(red, white, blue)






def   snake_case__     (	__lowerCamelCase       :					list			):
			"""simple docstring"""
			if not sequence:
						return []
			if len(__lowerCamelCase			) == 1:
						return list(__lowerCamelCase			)
			lowerCamelCase__	:	List[Any]				=0
			lowerCamelCase__	:	Dict				=len(__lowerCamelCase			) - 1
			lowerCamelCase__	:	Tuple				=0
			while mid <= high:
						if sequence[mid] == colors[0]:
									lowerCamelCase__			,     lowerCamelCase__	:	Optional[Any]				=sequence[mid], sequence[low]
									low += 1
									mid += 1
						elif sequence[mid] == colors[1]:
									mid += 1
						elif sequence[mid] == colors[2]:
									lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=sequence[high], sequence[mid]
									high -= 1
						else:
									lowerCamelCase__	:	Dict				=f'''The elements inside the sequence must contains only {colors} values'''
									raise ValueError(__lowerCamelCase			)
			return sequence


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_lowercase				:				Optional[Any]   		=		input("Enter numbers separated by commas:\n").strip()
		_lowercase				:				int   		=		[int(item.strip()) for item in user_input.split(",")]
		print(f'{dutch_national_flag_sort(unsorted)}')




