



'''simple docstring'''
from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


snake_case_ :	int         =  logging.get_logger(__name__)






class 							lowercase__	(							lowercase   ):





	def 	UpperCamelCase_  ( self					:				List[str] ,lowerCamelCase__					:				str ):







							'''simple docstring'''
							if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
													_UpperCamelCase					:		Union[str, Any] 				=   [label.strip() for label in labels.split(',' ) if label.strip()]
							return labels





	def __call__( self					:				Optional[Any] ,lowerCamelCase__					:				Optional[Any] ,lowerCamelCase__					:				Tuple ,lowerCamelCase__					:				Dict ):







							'''simple docstring'''
							if len(lowerCamelCase__ ) == 0 or len(lowerCamelCase__ ) == 0:
													raise ValueError('You must include at least one label and at least one sequence.' )
							if hypothesis_template.format(labels[0] ) == hypothesis_template:
													raise ValueError(
													    (
													        'The provided hypothesis_template "{}" was not able to be formatted with the target labels. '
													        'Make sure the passed template includes formatting syntax such as {{}} where the label should go.'
													    ).format(lowerCamelCase__ ) )

							if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
													_UpperCamelCase					:		Dict 				=   [sequences]

							_UpperCamelCase					:		str 				=   []
							for sequence in sequences:
													sequence_pairs.extend([[sequence, hypothesis_template.format(lowerCamelCase__ )] for label in labels] )

							return sequence_pairs, sequences






@add_end_docstrings(lowercase   )
class 							lowercase__	(							lowercase   ):





	def __init__( self					:				str ,lowerCamelCase__					:				str=ZeroShotClassificationArgumentHandler() ,*lowerCamelCase__					:				Dict ,**lowerCamelCase__					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   args_parser
							super().__init__(*lowerCamelCase__ ,**lowerCamelCase__ )
							if self.entailment_id == -1:
													logger.warning(
													    'Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '
													    '-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.' )





	@property
	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							for label, ind in self.model.config.labelaid.items():
													if label.lower().startswith('entail' ):
																			return ind
							return -1





	def 	UpperCamelCase_  ( self					:				int ,lowerCamelCase__					:				Union[str, Any] ,lowerCamelCase__					:				List[str]=True ,lowerCamelCase__					:				Union[str, Any]=True ,lowerCamelCase__					:				Optional[int]=TruncationStrategy.ONLY_FIRST ,**lowerCamelCase__					:				Union[str, Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   self.framework
							if self.tokenizer.pad_token is None:
													# Override for tokenizers not supporting padding
													logger.error(
													    'Tokenizer was not supporting padding necessary for zero-shot, attempting to use '
													    ' `pad_token=eos_token`' )
													_UpperCamelCase					:		List[str] 				=   self.tokenizer.eos_token
							try:
													_UpperCamelCase					:		Optional[Any] 				=   self.tokenizer(
													    lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ ,return_tensors=lowerCamelCase__ ,padding=lowerCamelCase__ ,truncation=lowerCamelCase__ ,)
							except Exception as e:
													if "too short" in str(lowerCamelCase__ ):
																			# tokenizers might yell that we want to truncate
																			# to a value that is not even reached by the input.
																			# In that case we don't want to truncate.
																			# It seems there's not a really better way to catch that
																			# exception.

																			_UpperCamelCase					:		int 				=   self.tokenizer(
																			    lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ ,return_tensors=lowerCamelCase__ ,padding=lowerCamelCase__ ,truncation=TruncationStrategy.DO_NOT_TRUNCATE ,)
													else:
																			raise e

							return inputs





	def 	UpperCamelCase_  ( self					:				Any ,**lowerCamelCase__					:				str ):







							'''simple docstring'''
							if kwargs.get('multi_class' ,lowerCamelCase__ ) is not None:
													_UpperCamelCase					:		Union[str, Any] 				=   kwargs['multi_class']
													logger.warning(
													    'The `multi_class` argument has been deprecated and renamed to `multi_label`. '
													    '`multi_class` will be removed in a future version of Transformers.' )
							_UpperCamelCase					:		Tuple 				=   {}
							if "candidate_labels" in kwargs:
													_UpperCamelCase					:		Optional[int] 				=   self._args_parser._parse_labels(kwargs['candidate_labels'] )
							if "hypothesis_template" in kwargs:
													_UpperCamelCase					:		List[Any] 				=   kwargs['hypothesis_template']

							_UpperCamelCase					:		int 				=   {}
							if "multi_label" in kwargs:
													_UpperCamelCase					:		Dict 				=   kwargs['multi_label']
							return preprocess_params, {}, postprocess_params





	def __call__( self					:				Optional[int] ,lowerCamelCase__					:				Union[str, List[str]] ,*lowerCamelCase__					:				Optional[int] ,**lowerCamelCase__					:				List[str] ,):







							'''simple docstring'''
							if len(lowerCamelCase__ ) == 0:
													pass
							elif len(lowerCamelCase__ ) == 1 and "candidate_labels" not in kwargs:
													_UpperCamelCase					:		List[Any] 				=   args[0]
							else:
													raise ValueError(F'Unable to understand extra arguments {args}' )

							return super().__call__(lowerCamelCase__ ,**lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ,lowerCamelCase__					:				Union[str, Any] ,lowerCamelCase__					:				Optional[Any]=None ,lowerCamelCase__					:				Optional[Any]="This example is {}." ):







							'''simple docstring'''
							_UpperCamelCase				,			_UpperCamelCase					:		Optional[int] 				=   self._args_parser(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )

							for i, (candidate_label, sequence_pair) in enumerate(zip(lowerCamelCase__ ,lowerCamelCase__ ) ):
													_UpperCamelCase					:		List[str] 				=   self._parse_and_tokenize([sequence_pair] )

													yield {
													    "candidate_label": candidate_label,
													    "sequence": sequences[0],
													    "is_last": i == len(lowerCamelCase__ ) - 1,
													    **model_input,
													}





	def 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				Union[str, Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Dict 				=   inputs['candidate_label']
							_UpperCamelCase					:		List[Any] 				=   inputs['sequence']
							_UpperCamelCase					:		Tuple 				=   {k: inputs[k] for k in self.tokenizer.model_input_names}
							_UpperCamelCase					:		int 				=   self.model(**lowerCamelCase__ )

							_UpperCamelCase					:		Optional[int] 				=   {
							    'candidate_label': candidate_label,
							    'sequence': sequence,
							    'is_last': inputs['is_last'],
							    **outputs,
							}
							return model_outputs





	def 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				int ,lowerCamelCase__					:				Optional[int]=False ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   [outputs['candidate_label'] for outputs in model_outputs]
							_UpperCamelCase					:		List[str] 				=   [outputs['sequence'] for outputs in model_outputs]
							_UpperCamelCase					:		Optional[Any] 				=   np.concatenate([output['logits'].numpy() for output in model_outputs] )
							_UpperCamelCase					:		str 				=   logits.shape[0]
							_UpperCamelCase					:		Optional[Any] 				=   len(lowerCamelCase__ )
							_UpperCamelCase					:		str 				=   N // n
							_UpperCamelCase					:		Optional[Any] 				=   logits.reshape((num_sequences, n, -1) )

							if multi_label or len(lowerCamelCase__ ) == 1:
													# softmax over the entailment vs. contradiction dim for each label independently
													_UpperCamelCase					:		Optional[Any] 				=   self.entailment_id
													_UpperCamelCase					:		str 				=   -1 if entailment_id == 0 else 0
													_UpperCamelCase					:		str 				=   reshaped_outputs[..., [contradiction_id, entailment_id]]
													_UpperCamelCase					:		Union[str, Any] 				=   np.exp(lowerCamelCase__ ) / np.exp(lowerCamelCase__ ).sum(-1 ,keepdims=lowerCamelCase__ )
													_UpperCamelCase					:		List[str] 				=   scores[..., 1]
							else:
													# softmax the "entailment" logits over all candidate labels
													_UpperCamelCase					:		Dict 				=   reshaped_outputs[..., self.entailment_id]
													_UpperCamelCase					:		Union[str, Any] 				=   np.exp(lowerCamelCase__ ) / np.exp(lowerCamelCase__ ).sum(-1 ,keepdims=lowerCamelCase__ )

							_UpperCamelCase					:		Tuple 				=   list(reversed(scores[0].argsort() ) )
							return {
							    "sequence": sequences[0],
							    "labels": [candidate_labels[i] for i in top_inds],
							    "scores": scores[0, top_inds].tolist(),
							}








"""simple docstring"""




def     a__ (				snake_case__				)						->					list:

			if len(snake_case__				) < 2:
						return collection

			def circle_sort_util(snake_case__       , snake_case__       , snake_case__				) -> bool:
						lowerCamelCase			      =					False

						if low == high:
									return swapped

						lowerCamelCase			      =					low
						lowerCamelCase			      =					high

						while left < right:
									if collection[left] > collection[right]:
												lowerCamelCase      ,   lowerCamelCase			      =					(
												    collection[right],
												    collection[left],
												)
												lowerCamelCase			      =					True

									left += 1
									right -= 1

						if left == right and collection[left] > collection[right + 1]:
									lowerCamelCase      ,   lowerCamelCase			      =					(
									    collection[right + 1],
									    collection[left],
									)

									lowerCamelCase			      =					True

						lowerCamelCase			      =					low + int((high - low) / 2				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , snake_case__       , snake_case__				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , mid + 1       , snake_case__				)

						return swapped or left_swap or right_swap

			lowerCamelCase			      =					True

			while is_not_sorted is True:
						lowerCamelCase			      =					circle_sort_util(snake_case__       , 0       , len(snake_case__				) - 1				)

			return collection


if __name__ == "__main__":
					lowerCAmelCase							:			Tuple     							=							input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase							:			List[Any]     							=							[int(item) for item in user_input.split(""",""")]
					print(circle_sort(unsorted))



