





"""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 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     if isinstance(__lowerCamelCase   ,		__lowerCamelCase    ):
         __A       :		Union[str, Any]							=	[label.strip() for label in labels.split(''','''    ) if label.strip()]
     return labels







 def __call__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''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    ):
         __A       :		str							=	[sequences]

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

     return sequence_pairs, sequences






@add_end_docstrings(SCREAMING_SNAKE_CASE__	)
class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 def __init__(       self   ,		__lowerCamelCase=ZeroShotClassificationArgumentHandler()   ,		*__lowerCamelCase   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		List[str]							=	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    ):



     '''simple docstring'''




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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=True   ,		__lowerCamelCase=True   ,		__lowerCamelCase=TruncationStrategy.ONLY_FIRST   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		List[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`'''    )
         __A       :		Optional[int]							=	self.tokenizer.eos_token
     try:
         __A       :		str							=	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.

             __A       :		Tuple							=	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   ,		**__lowerCamelCase    ):



     '''simple docstring'''




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

     __A       :		List[Any]							=	{}
     if "multi_label" in kwargs:
         __A       :		Optional[int]							=	kwargs['''multi_label''']
     return preprocess_params, {}, postprocess_params

 def __call__(       self   ,		__lowerCamelCase   ,		*__lowerCamelCase   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




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

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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		__lowerCamelCase="This example is {}."    ):



     '''simple docstring'''




     __A					,			__A       :		Optional[Any]							=	self._args_parser(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    )

     for i, (candidate_label, sequence_pair) in enumerate(zip(__lowerCamelCase   ,		__lowerCamelCase    )    ):
         __A       :		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   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		List[str]							=	inputs['''candidate_label''']
     __A       :		List[Any]							=	inputs['''sequence''']
     __A       :		List[Any]							=	{k: inputs[k] for k in self.tokenizer.model_input_names}
     __A       :		int							=	self.model(**__lowerCamelCase    )

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







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




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

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

     __A       :		List[str]							=	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 						__lowercase	(  snake_case_    :    str							,snake_case_    :    str		)  ->float:



    '''simple docstring'''







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

        return "".join(snake_case_		)

    # matching characters
    __A       :		int							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		Tuple							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		str							=	len(snake_case_		)

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

    if not match_count:
        __A       :		List[str]							=	0.0
    else:
        __A       :		Tuple							=	(
            1
            / 3
            * (
                match_count / len(snake_case_		)
                + match_count / len(snake_case_		)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __A       :		Tuple							=	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"""))




