



import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


__snake_case						:Union[str, Any]						=					logging.getLogger(__name__)

@dataclass(frozen=__UpperCAmelCase   )
class    _A	:
       UpperCamelCase__  :   str
       UpperCamelCase__  :   str
       UpperCamelCase__  :   Optional[str]						 =						None
       UpperCamelCase__  :   Optional[str]						 =						None
       UpperCamelCase__  :   Optional[str]						 =						None


@dataclass(frozen=__UpperCAmelCase   )
class    _A	:
       UpperCamelCase__  :   List[int]
       UpperCamelCase__  :   Optional[List[int]]						 =						None
       UpperCamelCase__  :   Optional[List[int]]						 =						None
       UpperCamelCase__  :   Optional[Union[int, float]]						 =						None
       UpperCamelCase__  :   Optional[int]						 =						None


if is_torch_available():
 import torch
 from torch.utils.data import Dataset

 class    _A	(     __UpperCAmelCase   ):
        UpperCamelCase__  :   List[InputFeatures]



        def __init__(		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					PreTrainedTokenizer					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					int=False					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		):


            '''simple docstring'''

            __a							=    hans_processors[task]()

            __a							=    os.path.join(
                __SCREAMING_SNAKE_CASE					,		'''cached_{}_{}_{}_{}'''.format(
                    '''dev''' if evaluate else '''train'''					,		tokenizer.__class__.__name__					,		str(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE					,		)					,		)
            __a							=    processor.get_labels()
            if tokenizer.__class__ in (
                RobertaTokenizer,
                RobertaTokenizerFast,
                XLMRobertaTokenizer,
                BartTokenizer,
                BartTokenizerFast,
            ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                __a	,       __a							=    label_list[2], label_list[1]
            __a							=    label_list

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            __a							=    cached_features_file + '''.lock'''
            with FileLock(__SCREAMING_SNAKE_CASE):
                if os.path.exists(__SCREAMING_SNAKE_CASE) and not overwrite_cache:
                    logger.info(F'Loading features from cached file {cached_features_file}')
                    __a							=    torch.load(__SCREAMING_SNAKE_CASE)
                else:
                    logger.info(F'Creating features from dataset file at {data_dir}')

                    __a							=    (
                        processor.get_dev_examples(__SCREAMING_SNAKE_CASE) if evaluate else processor.get_train_examples(__SCREAMING_SNAKE_CASE)
                    )

                    logger.info('''Training examples: %s'''					,		len(__SCREAMING_SNAKE_CASE))
                    __a							=    hans_convert_examples_to_features(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                    logger.info('''Saving features into cached file %s'''					,		__SCREAMING_SNAKE_CASE)
                    torch.save(self.features					,		__SCREAMING_SNAKE_CASE)



        def __len__(		self							:					Dict):


            '''simple docstring'''

            return len(self.features)



        def __getitem__(		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


            '''simple docstring'''

            return self.features[i]





        def 		_lowerCamelCase      (		self							:					Tuple):


            '''simple docstring'''

            return self.label_list


if is_tf_available():
 import tensorflow as tf

 class    _A	:
        UpperCamelCase__  :   List[InputFeatures]



        def __init__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					PreTrainedTokenizer					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = 128					,		__SCREAMING_SNAKE_CASE							:					List[Any]=False					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		):


            '''simple docstring'''

            __a							=    hans_processors[task]()
            __a							=    processor.get_labels()
            if tokenizer.__class__ in (
                RobertaTokenizer,
                RobertaTokenizerFast,
                XLMRobertaTokenizer,
                BartTokenizer,
                BartTokenizerFast,
            ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                __a	,       __a							=    label_list[2], label_list[1]
            __a							=    label_list

            __a							=    processor.get_dev_examples(__SCREAMING_SNAKE_CASE) if evaluate else processor.get_train_examples(__SCREAMING_SNAKE_CASE)
            __a							=    hans_convert_examples_to_features(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

            def gen():
                for ex_index, ex in tqdm.tqdm(enumerate(self.features)					,		desc='''convert examples to features'''):
                    if ex_index % 10_000 == 0:
                        logger.info('''Writing example %d of %d''' % (ex_index, len(__SCREAMING_SNAKE_CASE)))

                    yield (
                        {
                            "example_id": 0,
                            "input_ids": ex.input_ids,
                            "attention_mask": ex.attention_mask,
                            "token_type_ids": ex.token_type_ids,
                        },
                        ex.label,
                    )

            __a							=    tf.data.Dataset.from_generator(
                __SCREAMING_SNAKE_CASE					,		(
                    {
                        '''example_id''': tf.intaa,
                        '''input_ids''': tf.intaa,
                        '''attention_mask''': tf.intaa,
                        '''token_type_ids''': tf.intaa,
                    },
                    tf.intaa,
                )					,		(
                    {
                        '''example_id''': tf.TensorShape([]),
                        '''input_ids''': tf.TensorShape([None, None]),
                        '''attention_mask''': tf.TensorShape([None, None]),
                        '''token_type_ids''': tf.TensorShape([None, None]),
                    },
                    tf.TensorShape([]),
                )					,		)



        def 		_lowerCamelCase      (		self							:					Optional[int]):


            '''simple docstring'''

            return self.dataset



        def __len__(		self							:					Any):


            '''simple docstring'''

            return len(self.features)



        def __getitem__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


            '''simple docstring'''

            return self.features[i]





        def 		_lowerCamelCase      (		self							:					str):


            '''simple docstring'''

            return self.label_list

class    _A	(     __UpperCAmelCase   ):



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return self._create_examples(self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE					,		'''heuristics_train_set.txt'''))					,		'''train''')



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           return self._create_examples(self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE					,		'''heuristics_evaluation_set.txt'''))					,		'''dev''')



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           return ["contradiction", "entailment", "neutral"]





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    []
           for i, line in enumerate(__SCREAMING_SNAKE_CASE):
               if i == 0:
                   continue
               __a							=    '''%s-%s''' % (set_type, line[0])
               __a							=    line[5]
               __a							=    line[6]
               __a							=    line[7][2:] if line[7].startswith('''ex''') else line[7]
               __a							=    line[0]
               examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE					,		text_a=__SCREAMING_SNAKE_CASE					,		text_b=__SCREAMING_SNAKE_CASE					,		label=__SCREAMING_SNAKE_CASE					,		pairID=__SCREAMING_SNAKE_CASE))
           return examples





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			):
    __a							=    {label: i for i, label in enumerate(_UpperCAmelCase    )}

    __a							=    []
    for ex_index, example in tqdm.tqdm(enumerate(_UpperCAmelCase    )       ,			desc='''convert examples to features'''    ):
        if ex_index % 10000 == 0:
            logger.info('''Writing example %d''' % (ex_index)    )

        __a							=    tokenizer(
            example.text_a       ,			example.text_b       ,			add_special_tokens=_UpperCAmelCase       ,			max_length=_UpperCAmelCase       ,			padding='''max_length'''       ,			truncation=_UpperCAmelCase       ,			return_overflowing_tokens=_UpperCAmelCase       ,			)

        __a							=    label_map[example.label] if example.label in label_map else 0

        __a							=    int(example.pairID    )

        features.append(InputFeatures(**_UpperCAmelCase       ,			label=_UpperCAmelCase       ,			pairID=_UpperCAmelCase    )    )

    for i, example in enumerate(examples[:5]    ):
        logger.info('''*** Example ***'''    )
        logger.info(f'guid: {example}'    )
        logger.info(f'features: {features[i]}'    )

    return features


__snake_case						:Union[str, Any]						=					{
    '''hans''': 3,
}
__snake_case						:Tuple						=					{
    '''hans''': HansProcessor,
}










def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):

    def get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    ) -> str:
        __a							=    []
        __a							=    min(len(_stra    )       ,			len(_stra    )    ) // 2
        for i, l in enumerate(_stra    ):
            __a							=    int(max(0       ,			i - limit    )    )
            __a							=    int(min(i + limit + 1       ,			len(_stra    )    )    )
            if l in _stra[left:right]:
                matched.append(_UpperCAmelCase    )
                __a							=    f'{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}'

        return "".join(_UpperCAmelCase    )

    # matching characters
    __a							=    get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    len(_UpperCAmelCase    )

    # transposition
    __a							=    (
        len([(ca, ca) for ca, ca in zip(_UpperCAmelCase       ,			_UpperCAmelCase    ) if ca != ca]    ) // 2
    )

    if not match_count:
        __a							=    0.0
    else:
        __a							=    (
            1
            / 3
            * (
                match_count / len(_UpperCAmelCase    )
                + match_count / len(_UpperCAmelCase    )
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __a							=    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'''))





