




'''simple docstring'''



import os
from collections import deque

import torch
from torch.utils.data import Dataset






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A="",							A="train"  ):







             '''simple docstring'''







             assert os.path.isdir(A  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[]
             SCREAMING_SNAKE_CASE   :      str    					=						os.listdir(A  )
             for story_filename in story_filenames_list:
                   if "summary" in story_filename:
                         continue
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						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'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.documents[idx]
             SCREAMING_SNAKE_CASE   :      List[str]    					=						document_path.split('/'  )[-1]
             with open(A,							encoding='utf-8'  ) as source:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						source.read()
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						process_story(A  )
             return document_name, story_lines, summary_lines




def   lowercase__(			__UpperCamelCase:					Optional[Any]    ):

      """simple docstring"""






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

      # for some unknown reason some lines miss a period, add it
      SCREAMING_SNAKE_CASE   :      List[str]    					=						[_add_missing_period(__UpperCamelCase    ) for line in nonempty_lines]

      # gather article lines
      SCREAMING_SNAKE_CASE   :      Tuple    					=						[]
      SCREAMING_SNAKE_CASE   :      int    					=						deque(__UpperCamelCase    )
      while True:
            try:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						lines.popleft()
                  if element.startswith('@highlight'    ):
                        break
                  story_lines.append(__UpperCamelCase    )
            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
      SCREAMING_SNAKE_CASE   :      int    					=						list(filter(lambda __UpperCamelCase    : not t.startswith('@highlight'    )      ,__UpperCamelCase    )    )

      return story_lines, summary_lines




def   lowercase__(			__UpperCamelCase:					Dict    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Optional[Any]    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					Dict    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						torch.ones_like(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						sequence == pad_token_id
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
      return mask




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						[tokenizer.encode(__UpperCamelCase    ) for line in story_lines]
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[token for sentence in story_lines_token_ids for token in sentence]
      SCREAMING_SNAKE_CASE   :      str    					=						[tokenizer.encode(__UpperCamelCase    ) for line in summary_lines]
      SCREAMING_SNAKE_CASE   :      Dict    					=						[token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids





def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[]
      for sequence in batch:
            SCREAMING_SNAKE_CASE   :      Dict    					=						-1
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]
            for s in sequence:
                  if s == separator_token_id:
                        sentence_num += 1
                  embeddings.append(sentence_num % 2    )
            batch_embeddings.append(__UpperCamelCase    )
      return torch.tensor(__UpperCamelCase    )






_lowercase							:   Dict						=						"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
				from .utils import rich



