




"""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


a_					:     Union[str, Any]					    =  logging.get_logger(__name__)

class __lowercase(   __snake_case					):




					'''simple docstring'''


					def      snake_case_					(     self    ,						__a  ):






								if isinstance(__a    ,						__a  ):
											__lowerCamelCase       :						Tuple													=					[label.strip() for label in labels.split(','  ) if label.strip()]
								return labels





					def __call__(     self    ,						__a    ,						__a    ,						__a  ):






								if len(__a  ) == 0 or len(__a  ) == 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(__a  )  )

								if isinstance(__a    ,						__a  ):
											__lowerCamelCase       :						Dict													=					[sequences]

								__lowerCamelCase       :						Dict													=					[]
								for sequence in sequences:
											sequence_pairs.extend([[sequence, hypothesis_template.format(__a  )] for label in labels]  )

								return sequence_pairs, sequences



@add_end_docstrings(__snake_case					)
class __lowercase(   __snake_case					):




					'''simple docstring'''


					def __init__(     self    ,						__a=ZeroShotClassificationArgumentHandler()    ,						*__a    ,						**__a  ):
								__lowerCamelCase       :						Any													=					args_parser
								super().__init__(*__a    ,						**__a  )
								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      snake_case_					(     self  ):






								for label, ind in self.model.config.labelaid.items():
											if label.lower().startswith('entail'  ):
														return ind
								return -1
					def      snake_case_					(     self    ,						__a    ,						__a=True    ,						__a=True    ,						__a=TruncationStrategy.ONLY_FIRST    ,						**__a  ):
								__lowerCamelCase       :						Optional[Any]													=					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`'  )
											__lowerCamelCase       :						int													=					self.tokenizer.eos_token
								try:
											__lowerCamelCase       :						Optional[int]													=					self.tokenizer(
											    __a    ,						add_special_tokens=__a    ,						return_tensors=__a    ,						padding=__a    ,						truncation=__a    ,						)
								except Exception as e:
											if "too short" in str(__a  ):
														# 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.

														__lowerCamelCase       :						Dict													=					self.tokenizer(
														    __a    ,						add_special_tokens=__a    ,						return_tensors=__a    ,						padding=__a    ,						truncation=TruncationStrategy.DO_NOT_TRUNCATE    ,						)
											else:
														raise e

								return inputs
					def      snake_case_					(     self    ,						**__a  ):






								if kwargs.get('multi_class'    ,						__a  ) is not None:
											__lowerCamelCase       :						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.'  )
								__lowerCamelCase       :						str													=					{}
								if "candidate_labels" in kwargs:
											__lowerCamelCase       :						Optional[int]													=					self._args_parser._parse_labels(kwargs['candidate_labels']  )
								if "hypothesis_template" in kwargs:
											__lowerCamelCase       :						List[str]													=					kwargs['''hypothesis_template''']

								__lowerCamelCase       :						int													=					{}
								if "multi_label" in kwargs:
											__lowerCamelCase       :						Tuple													=					kwargs['''multi_label''']
								return preprocess_params, {}, postprocess_params
					def __call__(     self    ,						__a    ,						*__a    ,						**__a    ,						):






								if len(__a  ) == 0:
											pass
								elif len(__a  ) == 1 and "candidate_labels" not in kwargs:
											__lowerCamelCase       :						Tuple													=					args[0]
								else:
											raise ValueError(f'''Unable to understand extra arguments {args}'''  )

								return super().__call__(__a    ,						**__a  )
					def      snake_case_					(     self    ,						__a    ,						__a=None    ,						__a="This example is {}."  ):
								__lowerCamelCase       :						Optional[Any]													=					self._args_parser(__a    ,						__a    ,						__a  )

								for i, (candidate_label, sequence_pair) in enumerate(zip(__a    ,						__a  )  ):
											__lowerCamelCase       :						str													=					self._parse_and_tokenize([sequence_pair]  )

											yield {
											    "candidate_label": candidate_label,
											    "sequence": sequences[0],
											    "is_last": i == len(__a  ) - 1,
											    **model_input,
											}
					def      snake_case_					(     self    ,						__a  ):
								__lowerCamelCase       :						Optional[int]													=					inputs['''candidate_label''']
								__lowerCamelCase       :						int													=					inputs['''sequence''']
								__lowerCamelCase       :						Dict													=					{k: inputs[k] for k in self.tokenizer.model_input_names}
								__lowerCamelCase       :						str													=					self.model(**__a  )

								__lowerCamelCase       :						Union[str, Any]													=					{
								    '''candidate_label''': candidate_label,
								    '''sequence''': sequence,
								    '''is_last''': inputs['''is_last'''],
								    **outputs,
								}
								return model_outputs





					def      snake_case_					(     self    ,						__a    ,						__a=False  ):
								__lowerCamelCase       :						Union[str, Any]													=					[outputs['''candidate_label'''] for outputs in model_outputs]
								__lowerCamelCase       :						int													=					[outputs['''sequence'''] for outputs in model_outputs]
								__lowerCamelCase       :						Tuple													=					np.concatenate([output['logits'].numpy() for output in model_outputs]  )
								__lowerCamelCase       :						int													=					logits.shape[0]
								__lowerCamelCase       :						Union[str, Any]													=					len(__a  )
								__lowerCamelCase       :						Dict													=					N // n
								__lowerCamelCase       :						Optional[int]													=					logits.reshape((num_sequences, n, -1)  )

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

								__lowerCamelCase       :						Union[str, Any]													=					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'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		a[left_index]
    A			:  List[str]   =		left_index + 1
    for j in range(left_index + 1						,							snake_case__		):
        if a[j] < pivot:
            A,    A			:  Optional[int]   =		a[i], a[j]
            i += 1
    A,    A			:  str   =		a[i - 1], a[left_index]
    return i - 1







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    if left < right:
        A			:  Optional[Any]   =		random.randint(snake_case__						,							right - 1		)
        A,    A			:  List[str]   =		(
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        A			:  Any   =		partition(snake_case__						,							snake_case__						,							snake_case__		)
        quick_sort_random(
            snake_case__						,							snake_case__						,							snake_case__		)  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            snake_case__						,							pivot_index + 1						,							snake_case__		)  # recursive quicksort to the right of the pivot point







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  Any   =		input('''Enter numbers separated by a comma:\n'''		).strip()
    A			:  List[str]   =		[int(snake_case__		) for item in user_input.split(''','''		)]

    quick_sort_random(snake_case__						,							0						,							len(snake_case__		)		)

    print(snake_case__		)


if __name__ == "__main__":
      main()




