



'''simple docstring'''





import os
from collections import deque

import torch
from torch.utils.data import Dataset






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase=""  ,__UpperCAmelCase="train"      )    ->		Optional[int]:
      assert os.path.isdir(__UpperCAmelCase      )

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




  def __len__(    self      )    ->		Optional[Any]:
      return len(self.documents      )






  def __getitem__(    self  ,__UpperCAmelCase      )    ->		Tuple:
      lowerCAmelCase__ :  Tuple							      =					self.documents[idx]
      lowerCAmelCase__ :  List[str]							      =					document_path.split("""/"""      )[-1]
      with open(__UpperCAmelCase  ,encoding="""utf-8"""      ) as source:
          lowerCAmelCase__ :  Tuple							      =					source.read()
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					process_story(__UpperCAmelCase      )
      return document_name, story_lines, summary_lines







def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[int]							      =					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
    lowerCAmelCase__ :  List[Any]							      =					[_add_missing_period(UpperCamelCase			) for line in nonempty_lines]

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

    return story_lines, summary_lines
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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



    """simple docstring"""





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



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					torch.ones_like(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[Any]							      =					sequence == pad_token_id
    lowerCAmelCase__ :  Dict							      =					0
    return mask
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					[tokenizer.encode(UpperCamelCase			) for line in story_lines]
    lowerCAmelCase__ :  Tuple							      =					[token for sentence in story_lines_token_ids for token in sentence]
    lowerCAmelCase__ :  Optional[int]							      =					[tokenizer.encode(UpperCamelCase			) for line in summary_lines]
    lowerCAmelCase__ :  List[str]							      =					[token for sentence in summary_lines_token_ids for token in sentence]

    return story_token_ids, summary_token_ids




def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					[]
    for sequence in batch:
        lowerCAmelCase__ :  Optional[int]							      =					-1
        lowerCAmelCase__ :  Optional[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			)







'''simple docstring'''





import unittest

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


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_torch
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Tuple							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-base"""      )
      lowerCAmelCase__ :  Optional[int]							      =					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]      )
      # The dog is cute and lives in the garden house

      lowerCAmelCase__ :  str							      =					torch.Size((1, 12, 768)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Dict							      =					torch.tensor(
          [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]      )
      #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
      #  xlmr.eval()
      #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
      with torch.no_grad():
          lowerCAmelCase__ :  Optional[int]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )






  @slow
  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  Union[str, Any]							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-large"""      )
      lowerCAmelCase__ :  Optional[Any]							      =					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]      )
      # The dog is cute and lives in the garden house

      lowerCAmelCase__ :  Dict							      =					torch.Size((1, 12, 1024)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Union[str, Any]							      =					torch.tensor(
          [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]      )
      #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
      #  xlmr.eval()
      #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
      with torch.no_grad():
          lowerCAmelCase__ :  Union[str, Any]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )


