





'''simple docstring'''

import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


a   	=    logging.get_logger(__name__)







@dataclass
class __a			:
   __UpperCamelCase :			str						   =						field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()      )}      )
   __UpperCamelCase :			str						   =						field(
       metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}      )
   __UpperCamelCase :			int						   =						field(
       default=128, metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       }, )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Overwrite the cached training and evaluation sets'}      )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.task_name.lower()







class __a			( _snake_case      ):
   __UpperCamelCase :			Union[str, Any]						   =						'train'
   __UpperCamelCase :			Union[str, Any]						   =						'dev'
   __UpperCamelCase :			List[str]						   =						'test'







class __a			( _snake_case      ):
   __UpperCamelCase :			GlueDataTrainingArguments
   __UpperCamelCase :			str
   __UpperCamelCase :			List[InputFeatures]





   def __init__(       self					:      List[str]					,lowerCamelCase					:      GlueDataTrainingArguments					,lowerCamelCase					:      PreTrainedTokenizerBase					,lowerCamelCase					:      Optional[int] = None					,lowerCamelCase					:      Union[str, Split] = Split.train					,lowerCamelCase					:      Optional[str] = None					,):

      '''simple docstring'''

      warnings.warn(
          """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
          """library. You can have a look at this example script for pointers: """
          """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""					,lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							args
      __SCREAMING_SNAKE_CASE   				=							glue_processors[args.task_name]()
      __SCREAMING_SNAKE_CASE   				=							glue_output_modes[args.task_name]
      if isinstance(lowerCamelCase					,lowerCamelCase     ):
         try:
            __SCREAMING_SNAKE_CASE   				=							Split[mode]
         except KeyError:
            raise KeyError("""mode is not a valid split name"""     )
        # Load data features from cache or dataset file
      __SCREAMING_SNAKE_CASE   				=							os.path.join(
          cache_dir if cache_dir is not None else args.data_dir					,f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""					,)
      __SCREAMING_SNAKE_CASE   				=							self.processor.get_labels()
      if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
          "RobertaTokenizer",
          "RobertaTokenizerFast",
          "XLMRobertaTokenizer",
          "BartTokenizer",
          "BartTokenizerFast",
      ):
         # HACK(label indices are swapped in RoBERTa pretrained model)
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							label_list[2], label_list[1]
      __SCREAMING_SNAKE_CASE   				=							label_list

      # Make sure only the first process in distributed training processes the dataset,
      # and the others will use the cache.
      __SCREAMING_SNAKE_CASE   				=							cached_features_file + """.lock"""
      with FileLock(lowerCamelCase     ):
         if os.path.exists(lowerCamelCase     ) and not args.overwrite_cache:
            __SCREAMING_SNAKE_CASE   				=							time.time()
            __SCREAMING_SNAKE_CASE   				=							torch.load(lowerCamelCase     )
            logger.info(
                f"""Loading features from cached file {cached_features_file} [took %.3f s]"""					,time.time() - start     )
         else:
            logger.info(f"""Creating features from dataset file at {args.data_dir}"""     )

            if mode == Split.dev:
               __SCREAMING_SNAKE_CASE   				=							self.processor.get_dev_examples(args.data_dir     )
            elif mode == Split.test:
               __SCREAMING_SNAKE_CASE   				=							self.processor.get_test_examples(args.data_dir     )
            else:
               __SCREAMING_SNAKE_CASE   				=							self.processor.get_train_examples(args.data_dir     )
            if limit_length is not None:
               __SCREAMING_SNAKE_CASE   				=							examples[:limit_length]
            __SCREAMING_SNAKE_CASE   				=							glue_convert_examples_to_features(
                lowerCamelCase					,lowerCamelCase					,max_length=args.max_seq_length					,label_list=lowerCamelCase					,output_mode=self.output_mode					,)
            __SCREAMING_SNAKE_CASE   				=							time.time()
            torch.save(self.features					,lowerCamelCase     )
            # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
            logger.info(
                f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""     )





   def __len__(       self					:      Optional[Any]     ):

      '''simple docstring'''

      return len(self.features     )





   def __getitem__(       self					:      Tuple					,lowerCamelCase					:      Dict     ):

      '''simple docstring'''

      return self.features[i]





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      return self.label_list









'''simple docstring'''






def    _snake_case  (	A					,       A			)		->       int:
							return x if y == 0 else greatest_common_divisor(A					,       x % y			)


def    _snake_case  (	A					,       A			)		->       int:
							return (x * y) // greatest_common_divisor(A					,       A			)


def    _snake_case  (	A = 20			)		->       int:
							lowerCAmelCase__			  =      1
							for i in range(1					,       n + 1			):
														lowerCAmelCase__			  =      lcm(A					,       A			)
							return g


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