




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:






    """simple docstring"""





    lowerCAmelCase         = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()  )}  )
    lowerCAmelCase         = field(
        metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}  )
    lowerCAmelCase         = 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.'''
            )
        }							,		)
    lowerCAmelCase         = field(
        default=_a							,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}  )





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				int  =     self.task_name.lower()


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''train'''
    lowerCAmelCase         = '''dev'''
    lowerCAmelCase         = '''test'''


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = 42
    lowerCAmelCase         = 42
    lowerCAmelCase         = 42





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = Split.train  ,_SCREAMING_SNAKE_CASE = None  ,)					->						str:
         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'''  ,_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				List[str]  =     args
         UpperCAmelCase_     :				Optional[Any]  =     glue_processors[args.task_name]()
         UpperCAmelCase_     :				int  =     glue_output_modes[args.task_name]
         if isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              try:
                   UpperCAmelCase_     :				Any  =     Split[mode]
              except KeyError:
                   raise KeyError('''mode is not a valid split name'''    )
        # Load data features from cache or dataset file
         UpperCAmelCase_     :				List[str]  =     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}'''  ,)
         UpperCAmelCase_     :				Any  =     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)
              UpperCAmelCase_,       UpperCAmelCase_     :				str  =     label_list[2], label_list[1]
         UpperCAmelCase_     :				str  =     label_list

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         UpperCAmelCase_     :				Dict  =     cached_features_file + '''.lock'''
         with FileLock(_SCREAMING_SNAKE_CASE    ):
              if os.path.exists(_SCREAMING_SNAKE_CASE    ) and not args.overwrite_cache:
                   UpperCAmelCase_     :				List[Any]  =     time.time()
                   UpperCAmelCase_     :				Dict  =     torch.load(_SCREAMING_SNAKE_CASE    )
                   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:
                        UpperCAmelCase_     :				List[str]  =     self.processor.get_dev_examples(args.data_dir    )
                   elif mode == Split.test:
                        UpperCAmelCase_     :				Tuple  =     self.processor.get_test_examples(args.data_dir    )
                   else:
                        UpperCAmelCase_     :				int  =     self.processor.get_train_examples(args.data_dir    )
                   if limit_length is not None:
                        UpperCAmelCase_     :				Optional[int]  =     examples[:limit_length]
                   UpperCAmelCase_     :				int  =     glue_convert_examples_to_features(
                       _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,max_length=args.max_seq_length  ,label_list=_SCREAMING_SNAKE_CASE  ,output_mode=self.output_mode  ,)
                   UpperCAmelCase_     :				Union[str, Any]  =     time.time()
                   torch.save(self.features  ,_SCREAMING_SNAKE_CASE    )
                   # ^ 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    )					->						Tuple:
         return len(self.features    )





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





    def 				a__ ( self    )					->						Any:
         return self.label_list




import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_=""    ,a_="train"						):







						"""simple docstring"""
						assert os.path.isdir(a_						)

						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  os.listdir(a_						)
						for story_filename in story_filenames_list:
										if "summary" in story_filename:
														continue
										lowerCAmelCase__		  =  os.path.join(a_    ,a_						)
										if not os.path.isfile(a_						):
														continue
										self.documents.append(a_						)
		def __len__(							self						):







						"""simple docstring"""
						return len(self.documents						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.documents[idx]
						lowerCAmelCase__		  =  document_path.split('/'						)[-1]
						with open(a_    ,encoding='utf-8'						) as source:
										lowerCAmelCase__		  =  source.read()
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  process_story(a_						)
						return document_name, story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: len(snake_case__					) != 0    ,			[line.strip() for line in raw_story.split('\n'					)]					)					)

				# for some unknown reason some lines miss a period, add it
				lowerCAmelCase__		  =  [_add_missing_period(snake_case__					) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  deque(snake_case__					)
				while True:
								try:
												lowerCAmelCase__		  =  lines.popleft()
												if element.startswith('@highlight'					):
																break
												story_lines.append(snake_case__					)
								except IndexError:
												# if "@highlight" is absent from the file we pop
												# all elements until there is None, raising an exception.
												return story_lines, []

    # gather summary lines
				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: not t.startswith('@highlight'					)    ,			snake_case__					)					)

				return story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
				if line.startswith('@highlight'					):
								return line
				if line[-1] in END_TOKENS:
								return line
				return line + "."







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				if len(snake_case__					) > block_size:
								return sequence[:block_size]
				else:
								sequence.extend([pad_token_id] * (block_size - len(snake_case__					))					)
								return sequence







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.ones_like(snake_case__					)
				lowerCAmelCase__		  =  sequence == pad_token_id
				lowerCAmelCase__		  =  0
				return mask







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in story_lines]
				lowerCAmelCase__		  =  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in summary_lines]
				lowerCAmelCase__		  =  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				for sequence in batch:
								lowerCAmelCase__		  =  -1
								lowerCAmelCase__		  =  []
								for s in sequence:
												if s == separator_token_id:
																sentence_num += 1
												embeddings.append(sentence_num % 2					)
								batch_embeddings.append(snake_case__					)
				return torch.tensor(snake_case__					)
