




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,
)


_lowerCamelCase     :		int								=       logging.getLogger(__name__)


@dataclass(frozen=UpperCAmelCase__					)
class     UpperCamelCase_   :

    '''simple docstring'''






    UpperCAmelCase__							  =				42
    UpperCAmelCase__							  =				42
    UpperCAmelCase__							  =				None
    UpperCAmelCase__							  =				None
    UpperCAmelCase__							  =				None


@dataclass(frozen=UpperCAmelCase__					)
class     UpperCamelCase_   :

    '''simple docstring'''






    UpperCAmelCase__							  =				42
    UpperCAmelCase__							  =				None
    UpperCAmelCase__							  =				None
    UpperCAmelCase__							  =				None
    UpperCAmelCase__							  =				None


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


  class     UpperCamelCase_   (			UpperCAmelCase__					):

      '''simple docstring'''






      UpperCAmelCase__							  =				42


      def __init__(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     PreTrainedTokenizer ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Optional[int] = None ,			UpperCAmelCase__				:     List[str]=False ,			UpperCAmelCase__				:     bool = False ,			)						->Union[str, Any]:





             '''simple docstring'''

             A__							=   hans_processors[task]()

             A__							=   os.path.join(
                 UpperCAmelCase__ ,			'''cached_{}_{}_{}_{}'''.format(
                     '''dev''' if evaluate else '''train''' ,			tokenizer.__class__.__name__ ,			str(UpperCAmelCase__) ,			UpperCAmelCase__ ,			) ,			)
             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(UpperCAmelCase__):
                    if os.path.exists(UpperCAmelCase__) and not overwrite_cache:
                           logger.info(f"""Loading features from cached file {cached_features_file}""")
                           A__							=   torch.load(UpperCAmelCase__)
                    else:
                           logger.info(f"""Creating features from dataset file at {data_dir}""")

                           A__							=   (
                               processor.get_dev_examples(UpperCAmelCase__) if evaluate else processor.get_train_examples(UpperCAmelCase__)
                           )

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


      def __len__(      self				:     List[str])						->int:





             '''simple docstring'''

             return len(self.features)


      def __getitem__(      self				:     Any ,			UpperCAmelCase__				:     str)						->InputFeatures:





             '''simple docstring'''

             return self.features[i]





      def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Dict:





             '''simple docstring'''

             return self.label_list


if is_tf_available():
  import tensorflow as tf


  class     UpperCamelCase_   :

      '''simple docstring'''






      UpperCAmelCase__							  =				42


      def __init__(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     PreTrainedTokenizer ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Optional[int] = 128 ,			UpperCAmelCase__				:     Any=False ,			UpperCAmelCase__				:     bool = False ,			)						->Optional[Any]:





             '''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(UpperCAmelCase__) if evaluate else processor.get_train_examples(UpperCAmelCase__)
             A__							=   hans_convert_examples_to_features(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)

             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(UpperCAmelCase__)))

                           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(
                 UpperCAmelCase__ ,			(
                     {
                         '''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 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Tuple:





             '''simple docstring'''

             return self.dataset


      def __len__(      self				:     Tuple)						->Any:





             '''simple docstring'''

             return len(self.features)


      def __getitem__(      self				:     List[str] ,			UpperCAmelCase__				:     int)						->InputFeatures:





             '''simple docstring'''

             return self.features[i]





      def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Union[str, Any]:





             '''simple docstring'''

             return self.label_list


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Union[str, Any])						->int:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     Tuple)						->List[str]:





           '''simple docstring'''

           A__							=   []
           for i, line in enumerate(UpperCAmelCase__):
                  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=UpperCAmelCase__ ,			text_a=UpperCAmelCase__ ,			text_b=UpperCAmelCase__ ,			label=UpperCAmelCase__ ,			pairID=UpperCAmelCase__))
           return examples







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    )       ->	Optional[int]:



       """simple docstring"""

       A__							=   {label: i for i, label in enumerate(lowercase_						)}

       A__							=   []
       for ex_index, example in tqdm.tqdm(enumerate(lowercase_						)			,    desc='''convert examples to features'''						):
              if ex_index % 10_000 == 0:
                     logger.info('''Writing example %d''' % (ex_index)						)

              A__							=   tokenizer(
                  example.text_a			,    example.text_b			,    add_special_tokens=lowercase_			,    max_length=lowercase_			,    padding='''max_length'''			,    truncation=lowercase_			,    return_overflowing_tokens=lowercase_			,    )

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

              A__							=   int(example.pairID						)

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

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

       return features


_lowerCamelCase     :		int								=       {
    """hans""": 3,
}
_lowerCamelCase     :		int								=       {
    """hans""": HansProcessor,
}








def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       return " ".join(
           ''''''.join(word[::-1]						) if len(lowercase_						) > 4 else word for word in sentence.split()						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(reverse_long_words("""Hey wollef sroirraw"""))


