






import os
from collections import deque

import torch
from torch.utils.data import Dataset




class       snake_case__ (				a__):

       '''simple docstring'''



       def __init__(				self      ,    a__=""      ,    a__="train"     )			->   Optional[int]:






            '''simple docstring'''





            assert os.path.isdir(lowerCamelCase_     )

            __snake_case      :Optional[int] 							=    []
            __snake_case      :List[str] 							=    os.listdir(lowerCamelCase_     )
            for story_filename in story_filenames_list:
                 if "summary" in story_filename:
                      continue
                 __snake_case      :Union[str, Any] 							=    os.path.join(lowerCamelCase_      ,    lowerCamelCase_     )
                 if not os.path.isfile(lowerCamelCase_     ):
                      continue
                 self.documents.append(lowerCamelCase_     )



       def __len__(				self     )			->   List[Any]:






            '''simple docstring'''





            return len(self.documents     )



       def __getitem__(				self      ,    a__     )			->   Dict:






            '''simple docstring'''





            __snake_case      :List[str] 							=    self.documents[idx]
            __snake_case      :Optional[Any] 							=    document_path.split("""/"""     )[-1]
            with open(lowerCamelCase_      ,    encoding="""utf-8"""     ) as source:
                 __snake_case      :List[Any] 							=    source.read()
                 __snake_case							,			__snake_case      :List[str] 							=    process_story(lowerCamelCase_     )
            return document_name, story_lines, summary_lines






def 	UpperCamelCase	(							snake_case__  : Optional[int]	):




     '''simple docstring'''






     __snake_case      :Tuple 							=    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
     __snake_case      :List[Any] 							=    [_add_missing_period(snake_case__	) for line in nonempty_lines]

     # gather article lines
     __snake_case      :Union[str, Any] 							=    []
     __snake_case      :Any 							=    deque(snake_case__	)
     while True:
          try:
               __snake_case      :Any 							=    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
     __snake_case      :Any 							=    list(filter(lambda snake_case__	: not t.startswith("""@highlight"""	)						,snake_case__	)	)

     return story_lines, summary_lines






def 	UpperCamelCase	(							snake_case__  : Union[str, Any]	):




     '''simple docstring'''






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






def 	UpperCamelCase	(							snake_case__  : Tuple						,snake_case__  : Tuple						,snake_case__  : Optional[Any]	):




     '''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__  : str						,snake_case__  : str	):




     '''simple docstring'''






     __snake_case      :Optional[int] 							=    torch.ones_like(snake_case__	)
     __snake_case      :Dict 							=    sequence == pad_token_id
     __snake_case      :Optional[int] 							=    0
     return mask






def 	UpperCamelCase	(							snake_case__  : Optional[Any]						,snake_case__  : Dict						,snake_case__  : Optional[int]	):




     '''simple docstring'''






     __snake_case      :Dict 							=    [tokenizer.encode(snake_case__	) for line in story_lines]
     __snake_case      :List[Any] 							=    [token for sentence in story_lines_token_ids for token in sentence]
     __snake_case      :Union[str, Any] 							=    [tokenizer.encode(snake_case__	) for line in summary_lines]
     __snake_case      :str 							=    [token for sentence in summary_lines_token_ids for token in sentence]

     return story_token_ids, summary_token_ids






def 	UpperCamelCase	(							snake_case__  : Dict						,snake_case__  : Optional[int]	):




     '''simple docstring'''






     __snake_case      :Optional[Any] 							=    []
     for sequence in batch:
          __snake_case      :Union[str, Any] 							=    -1
          __snake_case      :Optional[Any] 							=    []
          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__	)






'''simple docstring'''






import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__UpperCAmelCase       							=				logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  bool      		= field(default=a__							,							metadata={"help": "Whether to use SortishSampler or not."}    )
							lowercase__     :  bool      		= field(
							    default=a__							,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}    )
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `max_length` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `num_beams` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[Union[str, Path, GenerationConfig]]      		= field(
							    default=a__							,							metadata={
							        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
							    }							,							)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      super().to_dict()
														for k, v in d.items():
																					if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      v.to_dict()
														return d