
import os
from collections import deque

import torch
from torch.utils.data import Dataset




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      def __init__(      self		,						lowerCamelCase=""		,						lowerCamelCase="train"			):
          assert os.path.isdir(lowerCamelCase			)

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



      def __getitem__(      self		,						lowerCamelCase			):
          snake_case__    =					self.documents[idx]
          snake_case__    =					document_path.split("/"			)[-1]
          with open(lowerCamelCase		,						encoding="utf-8"			) as source:
              snake_case__    =					source.read()
              snake_case__    ,       snake_case__    =					process_story(lowerCamelCase			)
          return document_name, story_lines, summary_lines





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					list(filter(lambda __lowerCAmelCase				: len(__lowerCAmelCase				) != 0     ,		[line.strip() for line in raw_story.split("\n"				)]				)				)

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

    # gather article lines
    snake_case__    =					[]
    snake_case__    =					deque(__lowerCAmelCase				)
    while True:
        try:
            snake_case__    =					lines.popleft()
            if element.startswith("@highlight"				):
                break
            story_lines.append(__lowerCAmelCase				)
        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__    =					list(filter(lambda __lowerCAmelCase				: not t.startswith("@highlight"				)     ,		__lowerCAmelCase				)				)

    return story_lines, summary_lines





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





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    if len(__lowerCAmelCase				) > block_size:
        return sequence[:block_size]
    else:
        sequence.extend([pad_token_id] * (block_size - len(__lowerCAmelCase				))				)
        return sequence





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					torch.ones_like(__lowerCAmelCase				)
    snake_case__    =					sequence == pad_token_id
    snake_case__    =					0
    return mask





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					[tokenizer.encode(__lowerCAmelCase				) for line in story_lines]
    snake_case__    =					[token for sentence in story_lines_token_ids for token in sentence]
    snake_case__    =					[tokenizer.encode(__lowerCAmelCase				) for line in summary_lines]
    snake_case__    =					[token for sentence in summary_lines_token_ids for token in sentence]

    return story_token_ids, summary_token_ids





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





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if any(not isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				) or x < 0 for x in sequence				):
        raise TypeError("Sequence must be list of non-negative integers"				)
    for _ in range(len(__lowerCAmelCase				)				):
        for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase     ,		sequence[1:]				)				):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]



