

"""simple docstring"""
import os
from collections import deque

import torch
from torch.utils.data import Dataset

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







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


        """simple docstring"""

        assert os.path.isdir(_lowerCamelCase	)

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



  def __len__(self	):


        """simple docstring"""

        return len(self.documents	)




  def __getitem__(self							,       _lowerCamelCase	):


        """simple docstring"""

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




def 					a__						(						lowerCAmelCase    )	-> int:
      UpperCAmelCase__			:							Optional[Any]							=    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
      UpperCAmelCase__			:							Optional[int]							=    [_add_missing_period(lowerCAmelCase    ) for line in nonempty_lines]

      # gather article lines
      UpperCAmelCase__			:							Optional[Any]							=    []
      UpperCAmelCase__			:							Optional[int]							=    deque(lowerCAmelCase    )
      while True:
            try:
                  UpperCAmelCase__			:							str							=    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
      UpperCAmelCase__			:							List[str]							=    list(filter(lambda lowerCAmelCase    : not t.startswith("""@highlight"""    )			,	lowerCAmelCase    )    )

      return story_lines, summary_lines




def 					a__						(						lowerCAmelCase    )	-> str:
      UpperCAmelCase__			:							Optional[Any]							=    [""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
      if line.startswith("""@highlight"""    ):
            return line
      if line[-1] in END_TOKENS:
            return line
      return line + "."




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> List[str]:
      if len(lowerCAmelCase    ) > block_size:
            return sequence[:block_size]
      else:
            sequence.extend([pad_token_id] * (block_size - len(lowerCAmelCase    ))    )
            return sequence




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> List[Any]:
      UpperCAmelCase__			:							Tuple							=    torch.ones_like(lowerCAmelCase    )
      UpperCAmelCase__			:							str							=    sequence == pad_token_id
      UpperCAmelCase__			:							int							=    0
      return mask




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Optional[int]:
      UpperCAmelCase__			:							List[Any]							=    [tokenizer.encode(lowerCAmelCase    ) for line in story_lines]
      UpperCAmelCase__			:							List[Any]							=    [token for sentence in story_lines_token_ids for token in sentence]
      UpperCAmelCase__			:							Optional[Any]							=    [tokenizer.encode(lowerCAmelCase    ) for line in summary_lines]
      UpperCAmelCase__			:							Optional[Any]							=    [token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids





def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Optional[int]:
      UpperCAmelCase__			:							Optional[int]							=    []
      for sequence in batch:
            UpperCAmelCase__			:							Any							=    -1
            UpperCAmelCase__			:							Dict							=    []
            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    )




"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""							,       return_dict=_lowerCamelCase	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    AutoTokenizer.from_pretrained("""google/mt5-small"""	)

        UpperCAmelCase__			:							Any							=    tokenizer("""Hello there"""							,       return_tensors="""pt"""	).input_ids
        UpperCAmelCase__			:							str							=    tokenizer("""Hi I am"""							,       return_tensors="""pt"""	).input_ids

        UpperCAmelCase__			:							Tuple							=    model(input_ids.to(_lowerCamelCase	)							,       labels=labels.to(_lowerCamelCase	)	).loss
        UpperCAmelCase__			:							Union[str, Any]							=    -(labels.shape[-1] * loss.item())

        UpperCAmelCase__			:							List[str]							=    -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 1e-4	)

