






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__   :						Optional[Any]													=   logging.getLogger(__name__)


@dataclass(frozen=UpperCAmelCase__     )
class     _a							:






  """simple docstring"""





  A_						       = 42
  A_						       = 42
  A_						       = None
  A_						       = None
  A_						       = None




@dataclass(frozen=UpperCAmelCase__     )
class     _a							:






  """simple docstring"""





  A_						       = 42
  A_						       = None
  A_						       = None
  A_						       = None
  A_						       = None


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


 class     _a							(	UpperCAmelCase__     ):






   """simple docstring"""





   A_						       = 42




   def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase=False		,		_UpperCAmelCase = False		,		)      ->			Optional[Any]:
      UpperCamelCase_						=  hans_processors[task]()

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

      # Make sure only the first process in distributed training processes the dataset,
      # and the others will use the cache.
      UpperCamelCase_						=  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}"""			)
            UpperCamelCase_						=  torch.load(_UpperCAmelCase			)
         else:
            logger.info(f"""Creating features from dataset file at {data_dir}"""			)

            UpperCamelCase_						=  (
                processor.get_dev_examples(_UpperCAmelCase			) if evaluate else processor.get_train_examples(_UpperCAmelCase			)
            )

            logger.info('Training examples: %s'		,		len(_UpperCAmelCase			)			)
            UpperCamelCase_						=  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			)      ->			int:
      return len(self.features			)




   def __getitem__(  self		,		_UpperCAmelCase			)      ->			InputFeatures:
      return self.features[i]




   def  _UpperCAmelCase  (  self			)      ->			List[Any]:
      return self.label_list


if is_tf_available():
 import tensorflow as tf


 class     _a							:






   """simple docstring"""





   A_						       = 42




   def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = 128		,		_UpperCAmelCase=False		,		_UpperCAmelCase = False		,		)      ->			Dict:
      UpperCamelCase_						=  hans_processors[task]()
      UpperCamelCase_						=  processor.get_labels()
      if tokenizer.__class__ in (
          RobertaTokenizer,
          RobertaTokenizerFast,
          XLMRobertaTokenizer,
          BartTokenizer,
          BartTokenizerFast,
      ):
         # HACK(label indices are swapped in RoBERTa pretrained model)
         UpperCamelCase_		,   UpperCamelCase_						=  label_list[2], label_list[1]
      UpperCamelCase_						=  label_list

      UpperCamelCase_						=  processor.get_dev_examples(_UpperCAmelCase			) if evaluate else processor.get_train_examples(_UpperCAmelCase			)
      UpperCamelCase_						=  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 % 10000 == 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,
            )

      UpperCamelCase_						=  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  _UpperCAmelCase  (  self			)      ->			List[Any]:
      return self.dataset




   def __len__(  self			)      ->			str:
      return len(self.features			)




   def __getitem__(  self		,		_UpperCAmelCase			)      ->			InputFeatures:
      return self.features[i]




   def  _UpperCAmelCase  (  self			)      ->			List[str]:
      return self.label_list


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Optional[Any]:
     return self._create_examples(self._read_tsv(os.path.join(_UpperCAmelCase		,		'heuristics_train_set.txt'			)			)		,		'train'			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     return self._create_examples(self._read_tsv(os.path.join(_UpperCAmelCase		,		'heuristics_evaluation_set.txt'			)			)		,		'dev'			)




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     return ["contradiction", "entailment", "neutral"]




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






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   ):
   UpperCamelCase_						=  {label: i for i, label in enumerate(__lowercase)}

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

      UpperCamelCase_						=  tokenizer(
          example.text_a     ,   example.text_b     ,   add_special_tokens=__lowercase     ,   max_length=__lowercase     ,   padding='max_length'     ,   truncation=__lowercase     ,   return_overflowing_tokens=__lowercase     ,   )

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

      UpperCamelCase_						=  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


snake_case__   :						str													=   {
    """hans""": 3,
}

snake_case__   :						Optional[Any]													=   {
    """hans""": HansProcessor,
}







def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int | float | str				)			->						tuple[int, int]:
       try:
              SCREAMING_SNAKE_CASE_							:				int		=float(UpperCAmelCase_				)
       except ValueError:
              raise ValueError('''Please enter a valid number'''				)
       SCREAMING_SNAKE_CASE_							:				Any		=decimal - int(UpperCAmelCase_				)
       if fractional_part == 0:
              return int(UpperCAmelCase_				), 1
       else:
              SCREAMING_SNAKE_CASE_							:				Any		=len(str(UpperCAmelCase_				).split('''.'''				)[1]				)
              SCREAMING_SNAKE_CASE_							:				str		=int(decimal * (1_0**number_of_frac_digits)				)
              SCREAMING_SNAKE_CASE_							:				Any		=1_0**number_of_frac_digits
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=denominator, numerator
              while True:
                     SCREAMING_SNAKE_CASE_							:				Any		=dividend % divisor
                     if remainder == 0:
                            break
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=divisor, remainder
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=numerator / divisor, denominator / divisor
              return int(UpperCAmelCase_				), int(UpperCAmelCase_				)


if __name__ == "__main__":
 print(F"{decimal_to_fraction(2) = }")
 print(F"{decimal_to_fraction(89.0) = }")
 print(F"{decimal_to_fraction('67') = }")
 print(F"{decimal_to_fraction('45.0') = }")
 print(F"{decimal_to_fraction(1.5) = }")
 print(F"{decimal_to_fraction('6.25') = }")
 print(F"{decimal_to_fraction('78td') = }")
