
"""simple docstring"""





def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case=False	)							->		Tuple:


					"""simple docstring"""





					if isinstance(__snake_case,     __snake_case	) and isinstance(__snake_case,     __snake_case	):
										_UpperCamelCase							 =  len(set_a.intersection(__snake_case	)	)

										if alternative_union:
															_UpperCamelCase							 =  len(__snake_case	) + len(__snake_case	)
										else:
															_UpperCamelCase							 =  len(set_a.union(__snake_case	)	)

										return intersection / union

					if isinstance(__snake_case,     (list, tuple)	) and isinstance(__snake_case,     (list, tuple)	):
										_UpperCamelCase							 =  [element for element in set_a if element in set_b]

										if alternative_union:
															_UpperCamelCase							 =  len(__snake_case	) + len(__snake_case	)
															return len(__snake_case	) / union
										else:
															_UpperCamelCase							 =  set_a + [element for element in set_b if element not in set_a]
															return len(__snake_case	) / len(__snake_case	)

										return len(__snake_case	) / len(__snake_case	)
					return None


if __name__ == "__main__":
				_a														=		{"""a""", """b""", """c""", """d""", """e"""}
				_a														=		{"""c""", """d""", """e""", """f""", """h""", """i"""}
				print(jaccard_similarity(set_a, set_b))








"""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														=		logging.get_logger(__name__)






class     _UpperCAmelCase(    lowerCamelCase	):



		def 						UpperCAmelCase							(					self				,		__a)       ->       Tuple:






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




		def __call__(					self				,		__a				,		__a				,		__a)       ->       Optional[int]:






							'''simple docstring'''
							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):
												_UpperCamelCase							 =  [sequences]

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

							return sequence_pairs, sequences






@add_end_docstrings(lowerCamelCase	)
class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a=ZeroShotClassificationArgumentHandler()				,		*__a				,		**__a)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  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 						UpperCAmelCase							(					self)       ->       List[str]:






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



		def 						UpperCAmelCase							(					self				,		__a				,		__a=True				,		__a=True				,		__a=TruncationStrategy.ONLY_FIRST				,		**__a)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  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							 =  self.tokenizer.eos_token
							try:
												_UpperCamelCase							 =  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.

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

							return inputs



		def 						UpperCAmelCase							(					self				,		**__a)       ->       Any:






							'''simple docstring'''
							if kwargs.get('''multi_class'''				,		__a) is not None:
												_UpperCamelCase							 =  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							 =  {}
							if "candidate_labels" in kwargs:
												_UpperCamelCase							 =  self._args_parser._parse_labels(kwargs['''candidate_labels'''])
							if "hypothesis_template" in kwargs:
												_UpperCamelCase							 =  kwargs['''hypothesis_template''']

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



		def __call__(					self				,		__a				,		*__a				,		**__a				,		)       ->       int:






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

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



		def 						UpperCAmelCase							(					self				,		__a				,		__a=None				,		__a="This example is {}.")       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  self._args_parser(__a				,		__a				,		__a)

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

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



		def 						UpperCAmelCase							(					self				,		__a)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  inputs['''candidate_label''']
							_UpperCamelCase							 =  inputs['''sequence''']
							_UpperCamelCase							 =  {k: inputs[k] for k in self.tokenizer.model_input_names}
							_UpperCamelCase							 =  self.model(**__a)

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




		def 						UpperCAmelCase							(					self				,		__a				,		__a=False)       ->       Dict:






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

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

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






