



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						    =  logging.getLogger(__name__)

@dataclass(frozen=UpperCamelCase )
class       _SCREAMING_SNAKE_CASE	:
     lowerCAmelCase					=			42
     lowerCAmelCase					=			42
     lowerCAmelCase					=			None
     lowerCAmelCase					=			None
     lowerCAmelCase					=			None






@dataclass(frozen=UpperCamelCase )
class       _SCREAMING_SNAKE_CASE	:
     lowerCAmelCase					=			42
     lowerCAmelCase					=			None
     lowerCAmelCase					=			None
     lowerCAmelCase					=			None
     lowerCAmelCase					=			None


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

      class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
           lowerCAmelCase					=			42





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

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

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            __SCREAMING_SNAKE_CASE       :    str       =	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}"""				)
              __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	torch.load(UpperCamelCase				)
             else:
              logger.info(F"""Creating features from dataset file at {data_dir}"""				)

              __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(
                  processor.get_dev_examples(UpperCamelCase				) if evaluate else processor.get_train_examples(UpperCamelCase				)
              )

              logger.info("Training examples: %s"     ,  len(UpperCamelCase				)				)
              __SCREAMING_SNAKE_CASE       :    Any       =	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 :							Tuple				)->Any:
            return len(self.features				)





           def __getitem__(       self :							Union[str, Any]     ,  UpperCamelCase :							int				)->InputFeatures:
            return self.features[i]






           def 	__snake_case    (       self :							int				)->Tuple:
            return self.label_list


if is_tf_available():
      import tensorflow as tf

      class       _SCREAMING_SNAKE_CASE	:
           lowerCAmelCase					=			42





           def __init__(       self :							Dict     ,  UpperCamelCase :							str     ,  UpperCamelCase :							PreTrainedTokenizer     ,  UpperCamelCase :							str     ,  UpperCamelCase :							Optional[int] = 1_2_8     ,  UpperCamelCase :							Union[str, Any]=False     ,  UpperCamelCase :							bool = False     ,  )->str:
            __SCREAMING_SNAKE_CASE       :    str       =	hans_processors[task]()
            __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	processor.get_labels()
            if tokenizer.__class__ in (
                RobertaTokenizer,
                RobertaTokenizerFast,
                XLMRobertaTokenizer,
                BartTokenizer,
                BartTokenizerFast,
            ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             __SCREAMING_SNAKE_CASE       :    List[str]       =	label_list[2], label_list[1]
            __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	label_list

            __SCREAMING_SNAKE_CASE       :    str       =	processor.get_dev_examples(UpperCamelCase				) if evaluate else processor.get_train_examples(UpperCamelCase				)
            __SCREAMING_SNAKE_CASE       :    str       =	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 % 1_0_0_0_0 == 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,
              )

            __SCREAMING_SNAKE_CASE       :    List[str]       =	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 	__snake_case    (       self :							List[Any]				)->str:
            return self.dataset





           def __len__(       self :							Tuple				)->List[str]:
            return len(self.features				)





           def __getitem__(       self :							Optional[Any]     ,  UpperCamelCase :							Tuple				)->InputFeatures:
            return self.features[i]






           def 	__snake_case    (       self :							List[Any]				)->Optional[int]:
            return self.label_list

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Union[str, Any]				)->Tuple:
      return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase     ,  "heuristics_train_set.txt"				)				)     ,  "train"				)





     def 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							Optional[int]				)->Any:
      return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase     ,  "heuristics_evaluation_set.txt"				)				)     ,  "dev"				)





     def 	__snake_case    (       self :							Optional[int]				)->Tuple:
      return ["contradiction", "entailment", "neutral"]






     def 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							str     ,  UpperCamelCase :							Any				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[]
      for i, line in enumerate(UpperCamelCase				):
       if i == 0:
        continue
       __SCREAMING_SNAKE_CASE       :    str       =	"%s-%s" % (set_type, line[0])
       __SCREAMING_SNAKE_CASE       :    List[str]       =	line[5]
       __SCREAMING_SNAKE_CASE       :    List[str]       =	line[6]
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	line[7][2:] if line[7].startswith("ex"				) else line[7]
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	line[0]
       examples.append(InputExample(guid=UpperCamelCase     ,  text_a=UpperCamelCase     ,  text_b=UpperCamelCase     ,  label=UpperCamelCase     ,  pairID=UpperCamelCase				)				)
      return examples






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[InputExample]		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     PreTrainedTokenizer		,	):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	{label: i for i, label in enumerate(__lowerCamelCase     )}

 __SCREAMING_SNAKE_CASE       :    Any       =	[]
 for ex_index, example in tqdm.tqdm(enumerate(__lowerCamelCase     )		,	desc="convert examples to features"     ):
  if ex_index % 10000 == 0:
   logger.info("Writing example %d" % (ex_index)     )

  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tokenizer(
      example.text_a		,	example.text_b		,	add_special_tokens=__lowerCamelCase		,	max_length=__lowerCamelCase		,	padding="max_length"		,	truncation=__lowerCamelCase		,	return_overflowing_tokens=__lowerCamelCase		,	)

  __SCREAMING_SNAKE_CASE       :    List[str]       =	label_map[example.label] if example.label in label_map else 0

  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(example.pairID     )

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

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

 return features


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






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











from math import sqrt






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5		,	int(sqrt(__lowerCamelCase     ) + 1     )		,	6     ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int = 10001     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	0
 __SCREAMING_SNAKE_CASE       :    Dict       =	1
 while count != nth and number < 3:
  number += 1
  if is_prime(__lowerCamelCase     ):
   count += 1
 while count != nth:
  number += 2
  if is_prime(__lowerCamelCase     ):
   count += 1
 return number


if __name__ == "__main__":
      print(f'''{solution() = }''')






