

'''simple docstring'''





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,
)


UpperCAmelCase__ :     Dict				    =    logging.getLogger(__name__)







@dataclass(frozen=UpperCAmelCase       )
class        a__       :







  """simple docstring"""




  UpperCAmelCase__      : str
  UpperCAmelCase__      : str
  UpperCAmelCase__      : Optional[str]			       =None
  UpperCAmelCase__      : Optional[str]			       =None
  UpperCAmelCase__      : Optional[str]			       =None







@dataclass(frozen=UpperCAmelCase       )
class        a__       :







  """simple docstring"""




  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       ):







    """simple docstring"""




    UpperCAmelCase__      : List[InputFeatures]


    def __init__(			self    : int				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : PreTrainedTokenizer				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : Dict=False				,				UpperCAmelCase__    : bool = False				,				)					->int:
          """simple docstring"""







          SCREAMING_SNAKE_CASE :      List[Any]       =   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 :      Tuple       =   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 :      List[str]       =   label_list

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

                      SCREAMING_SNAKE_CASE :      Optional[Any]       =   (
                          processor.get_dev_examples(UpperCAmelCase__		) if evaluate else processor.get_train_examples(UpperCAmelCase__		)
                      )

                      logger.info("""Training examples: %s"""				,				len(UpperCAmelCase__		)		)
                      SCREAMING_SNAKE_CASE :      str       =   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    : str		)					->Dict:
          """simple docstring"""







          return len(self.features		)


    def __getitem__(			self    : List[str]				,				UpperCAmelCase__    : Union[str, Any]		)					->InputFeatures:
          """simple docstring"""







          return self.features[i]




    def  _lowercase      (			self    : List[Any]		)					->Optional[int]:
          """simple docstring"""







          return self.label_list


if is_tf_available():
  import tensorflow as tf







  class        a__       :







    """simple docstring"""




    UpperCAmelCase__      : List[InputFeatures]


    def __init__(			self    : List[Any]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : PreTrainedTokenizer				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Optional[int] = 1_2_8				,				UpperCAmelCase__    : str=False				,				UpperCAmelCase__    : bool = False				,				)					->str:
          """simple docstring"""







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

          SCREAMING_SNAKE_CASE :      List[str]       =   processor.get_dev_examples(UpperCAmelCase__		) if evaluate else processor.get_train_examples(UpperCAmelCase__		)
          SCREAMING_SNAKE_CASE :      Any       =   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 :      Union[str, Any]       =   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  _lowercase      (			self    : List[Any]		)					->Any:
          """simple docstring"""







          return self.dataset


    def __len__(			self    : List[Any]		)					->List[str]:
          """simple docstring"""







          return len(self.features		)


    def __getitem__(			self    : Optional[int]				,				UpperCAmelCase__    : Any		)					->InputFeatures:
          """simple docstring"""







          return self.features[i]




    def  _lowercase      (			self    : List[str]		)					->Optional[int]:
          """simple docstring"""







          return self.label_list







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  def  _lowercase      (			self    : List[Any]				,				UpperCAmelCase__    : Optional[Any]		)					->Dict:
        """simple docstring"""







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


  def  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : List[str]		)					->Dict:
        """simple docstring"""







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


  def  _lowercase      (			self    : Tuple		)					->Tuple:
        """simple docstring"""







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




  def  _lowercase      (			self    : str				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : List[Any]		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   []
        for i, line in enumerate(UpperCAmelCase__		):
              if i == 0:
                    continue
              SCREAMING_SNAKE_CASE :      List[Any]       =   """%s-%s""" % (set_type, line[0])
              SCREAMING_SNAKE_CASE :      Dict       =   line[5]
              SCREAMING_SNAKE_CASE :      Dict       =   line[6]
              SCREAMING_SNAKE_CASE :      str       =   line[7][2:] if line[7].startswith("""ex"""		) else line[7]
              SCREAMING_SNAKE_CASE :      Dict       =   line[0]
              examples.append(InputExample(guid=UpperCAmelCase__				,				text_a=UpperCAmelCase__				,				text_b=UpperCAmelCase__				,				label=UpperCAmelCase__				,				pairID=UpperCAmelCase__		)		)
        return examples






def 					__lowercase      (     _A						,    _A						,    _A						,    _A						,    )     ->						int:
      SCREAMING_SNAKE_CASE :      Tuple       =   {label: i for i, label in enumerate(_A			)}

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

            SCREAMING_SNAKE_CASE :      Any       =   tokenizer(
                example.text_a						,    example.text_b						,    add_special_tokens=_A						,    max_length=_A						,    padding="""max_length"""						,    truncation=_A						,    return_overflowing_tokens=_A						,    )

            SCREAMING_SNAKE_CASE :      Any       =   label_map[example.label] if example.label in label_map else 0

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

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

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

      return features


UpperCAmelCase__ :     Dict				    =    {
    """hans""": 3,
}







UpperCAmelCase__ :     Any				    =    {
    """hans""": HansProcessor,
}







from __future__ import annotations






def 					__lowercase      (     _A						,    _A			)     ->						list[str]:
      if nth_term == "":
            return [""]
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      list[str]       =   []
      for temp in range(int(_A			)			):
            series.append(F"1 / {pow(temp + 1						,    int(_A			)			)}" if series else """1"""			)
      return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase__ :     Dict				    =    int(input("""Enter the last number (nth term) of the P-Series"""))
  UpperCAmelCase__ :     Optional[int]				    =    int(input("""Enter the power for  P-Series"""))
  print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
  print(p_series(nth_term, power))




