






'''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:      Dict  				=				logging.get_logger(__name__)






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Optional[int]:
							if isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   Tuple        =			[label.strip() for label in labels.split(','       ) if label.strip()]
							return labels







	def __call__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Dict:
							if len(_lowercase       ) == 0 or len(_lowercase       ) == 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(_lowercase       )       )

							if isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   List[Any]        =			[sequences]

							lowercase_      :   List[Any]        =			[]
							for sequence in sequences:
													sequence_pairs.extend([[sequence, hypothesis_template.format(_lowercase       )] for label in labels]       )

							return sequence_pairs, sequences






@add_end_docstrings(UpperCAmelCase_				)
class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def __init__(							self  ,      _lowercase=ZeroShotClassificationArgumentHandler()  ,      *_lowercase  ,      **_lowercase       )						->  Union[str, Any]:
							lowercase_      :   Dict        =			args_parser
							super().__init__(*_lowercase  ,      **_lowercase       )
							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        lowerCamelCase__		(							self       )						->  Optional[int]:
							for label, ind in self.model.config.labelaid.items():
													if label.lower().startswith('entail'       ):
																			return ind
							return -1
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=TruncationStrategy.ONLY_FIRST  ,      **_lowercase       )						->  str:
							lowercase_      :   int        =			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`'       )
													lowercase_      :   Dict        =			self.tokenizer.eos_token
							try:
													lowercase_      :   str        =			self.tokenizer(
													    _lowercase  ,      add_special_tokens=_lowercase  ,      return_tensors=_lowercase  ,      padding=_lowercase  ,      truncation=_lowercase  ,      )
							except Exception as e:
													if "too short" in str(_lowercase       ):
																			# 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.

																			lowercase_      :   Tuple        =			self.tokenizer(
																			    _lowercase  ,      add_special_tokens=_lowercase  ,      return_tensors=_lowercase  ,      padding=_lowercase  ,      truncation=TruncationStrategy.DO_NOT_TRUNCATE  ,      )
													else:
																			raise e

							return inputs
	def        lowerCamelCase__		(							self  ,      **_lowercase       )						->  Dict:
							if kwargs.get('multi_class'  ,      _lowercase       ) is not None:
													lowercase_      :   List[str]        =			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.'       )
							lowercase_      :   int        =			{}
							if "candidate_labels" in kwargs:
													lowercase_      :   Optional[int]        =			self._args_parser._parse_labels(kwargs['candidate_labels']       )
							if "hypothesis_template" in kwargs:
													lowercase_      :   Dict        =			kwargs['hypothesis_template']

							lowercase_      :   Union[str, Any]        =			{}
							if "multi_label" in kwargs:
													lowercase_      :   str        =			kwargs['multi_label']
							return preprocess_params, {}, postprocess_params
	def __call__(							self  ,      _lowercase  ,      *_lowercase  ,      **_lowercase  ,      )						->  List[Any]:
							if len(_lowercase       ) == 0:
													pass
							elif len(_lowercase       ) == 1 and "candidate_labels" not in kwargs:
													lowercase_      :   str        =			args[0]
							else:
													raise ValueError(f"Unable to understand extra arguments {args}"       )

							return super().__call__(_lowercase  ,      **_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase=None  ,      _lowercase="This example is {}."       )						->  Dict:
							lowercase_  ,      lowercase_      :   int        =			self._args_parser(_lowercase  ,      _lowercase  ,      _lowercase       )

							for i, (candidate_label, sequence_pair) in enumerate(zip(_lowercase  ,      _lowercase       )       ):
													lowercase_      :   Any        =			self._parse_and_tokenize([sequence_pair]       )

													yield {
													    "candidate_label": candidate_label,
													    "sequence": sequences[0],
													    "is_last": i == len(_lowercase       ) - 1,
													    **model_input,
													}
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  int:
							lowercase_      :   Optional[Any]        =			inputs['candidate_label']
							lowercase_      :   Optional[int]        =			inputs['sequence']
							lowercase_      :   Optional[int]        =			{k: inputs[k] for k in self.tokenizer.model_input_names}
							lowercase_      :   List[str]        =			self.model(**_lowercase       )

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







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase=False       )						->  Any:
							lowercase_      :   List[str]        =			[outputs['candidate_label'] for outputs in model_outputs]
							lowercase_      :   Dict        =			[outputs['sequence'] for outputs in model_outputs]
							lowercase_      :   List[Any]        =			np.concatenate([output['logits'].numpy() for output in model_outputs]       )
							lowercase_      :   Tuple        =			logits.shape[0]
							lowercase_      :   Tuple        =			len(_lowercase       )
							lowercase_      :   List[Any]        =			N // n
							lowercase_      :   List[Any]        =			logits.reshape((num_sequences, n, -1)       )

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

							lowercase_      :   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'''

def 	_UpperCAmelCase    (  a		:	str						,			a		:	str							)			->			float:





						"""simple docstring"""

						def get_matched_characters(a		:	str						,			a		:	str							) -> str:
												lowercase_      :   Union[str, Any]        =			[]
												lowercase_      :   Tuple        =			min(len(_stra							)						,			len(_stra							)							) // 2
												for i, l in enumerate(_stra							):
																		lowercase_      :   Optional[int]        =			int(max(0						,			i - limit							)							)
																		lowercase_      :   Union[str, Any]        =			int(min(i + limit + 1						,			len(_stra							)							)							)
																		if l in _stra[left:right]:
																								matched.append(a							)
																								lowercase_      :   Union[str, Any]        =			f"{_stra[0:_stra.index(a							)]} {_stra[_stra.index(a							) + 1:]}"

												return "".join(a							)

						# matching characters
						lowercase_      :   Union[str, Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[int]        =			len(a							)

						# transposition
						lowercase_      :   Dict        =			(
						    len([(ca, ca) for ca, ca in zip(a						,			a							) if ca != ca]							) // 2
						)

						if not match_count:
												lowercase_      :   List[str]        =			0.0
						else:
												lowercase_      :   Any        =			(
												    1
												    / 3
												    * (
												        match_count / len(a							)
												        + match_count / len(a							)
												        + (match_count - transpositions) / match_count
												    )
												)

						# common prefix up to 4 characters
						lowercase_      :   Optional[Any]        =			0
						for ca, ca in zip(stra[:4]						,			stra[:4]							):
												if ca == ca:
																		prefix_len += 1
												else:
																		break

						return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(jaro_winkler("hello", "world"))
