




import os
from collections import deque

import torch
from torch.utils.data import Dataset







class 		lowercase							(     UpperCamelCase__	):

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

           _A							:  Any    =		[]
           _A							:  Any    =		os.listdir(_a       )
           for story_filename in story_filenames_list:
                 if "summary" in story_filename:
                       continue
                 _A							:  Dict    =		os.path.join(_a	,  _a       )
                 if not os.path.isfile(_a       ):
                       continue
                 self.documents.append(_a       )

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



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







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  List[str]    =		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
      _A							:  Dict    =		[_add_missing_period(_snake_case  ) for line in nonempty_lines]

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

      return story_lines, summary_lines







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







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):






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







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[Any]    =		torch.ones_like(_snake_case  )
      _A							:  Any    =		sequence == pad_token_id
      _A							:  Any    =		0
      return mask







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  Tuple    =		[tokenizer.encode(_snake_case  ) for line in story_lines]
      _A							:  List[str]    =		[token for sentence in story_lines_token_ids for token in sentence]
      _A							:  Any    =		[tokenizer.encode(_snake_case  ) for line in summary_lines]
      _A							:  Union[str, Any]    =		[token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  str    =		[]
      for sequence in batch:
            _A							:  List[Any]    =		-1
            _A							:  Optional[int]    =		[]
            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  )







import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor







class 		lowercase							(     unittest.TestCase	):

     def __init__(						self	,  _a	,  _a=7	,  _a=3	,  _a=18	,  _a=30	,  _a=400	,  _a=True	,  _a=None	,  _a=True	,  _a=[0.5, 0.5, 0.5]	,  _a=[0.5, 0.5, 0.5]	,  )		->							Tuple:
           _A							:  Any    =		size if size is not None else {"""height""": 18, """width""": 18}
           _A							:  Optional[Any]    =		parent
           _A							:  Union[str, Any]    =		batch_size
           _A							:  List[Any]    =		num_channels
           _A							:  List[str]    =		image_size
           _A							:  Optional[Any]    =		min_resolution
           _A							:  List[Any]    =		max_resolution
           _A							:  Optional[Any]    =		do_resize
           _A							:  str    =		size
           _A							:  List[str]    =		do_normalize
           _A							:  Dict    =		image_mean
           _A							:  int    =		image_std



     def       a__			(						self       )		->							Any:
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
           }







@require_torch
@require_vision
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		DPTImageProcessor if is_vision_available() else None

     def       a__			(						self       )		->							Optional[int]:
           _A							:  Optional[Any]    =		DPTImageProcessingTester(self       )

     @property
     def       a__			(						self       )		->							Tuple:
           return self.image_processor_tester.prepare_image_processor_dict()

     def       a__			(						self       )		->							Any:
           _A							:  List[str]    =		self.image_processing_class(**self.image_processor_dict       )
           self.assertTrue(hasattr(_a	,  """image_mean"""       )       )
           self.assertTrue(hasattr(_a	,  """image_std"""       )       )
           self.assertTrue(hasattr(_a	,  """do_normalize"""       )       )
           self.assertTrue(hasattr(_a	,  """do_resize"""       )       )
           self.assertTrue(hasattr(_a	,  """size"""       )       )

     def       a__			(						self       )		->							Any:
           _A							:  List[str]    =		self.image_processing_class.from_dict(self.image_processor_dict       )
           self.assertEqual(image_processor.size	,  {"""height""": 18, """width""": 18}       )

           _A							:  str    =		self.image_processing_class.from_dict(self.image_processor_dict	,  size=42       )
           self.assertEqual(image_processor.size	,  {"""height""": 42, """width""": 42}       )

     def       a__			(						self       )		->							List[Any]:
           # Initialize image_processing
           _A							:  Optional[int]    =		self.image_processing_class(**self.image_processor_dict       )
           # create random PIL images
           _A							:  Any    =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=_a       )
           for image in image_inputs:
                 self.assertIsInstance(_a	,  Image.Image       )

           # Test not batched input
           _A							:  Union[str, Any]    =		image_processing(image_inputs[0]	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

           # Test batched
           _A							:  int    =		image_processing(_a	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

     def       a__			(						self       )		->							Union[str, Any]:
           # Initialize image_processing
           _A							:  List[Any]    =		self.image_processing_class(**self.image_processor_dict       )
           # create random numpy tensors
           _A							:  Optional[Any]    =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=_a	,  numpify=_a       )
           for image in image_inputs:
                 self.assertIsInstance(_a	,  np.ndarray       )

           # Test not batched input
           _A							:  Dict    =		image_processing(image_inputs[0]	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

           # Test batched
           _A							:  Any    =		image_processing(_a	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )



     def       a__			(						self       )		->							List[str]:
           # Initialize image_processing
           _A							:  Dict    =		self.image_processing_class(**self.image_processor_dict       )
           # create random PyTorch tensors
           _A							:  str    =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=_a	,  torchify=_a       )
           for image in image_inputs:
                 self.assertIsInstance(_a	,  torch.Tensor       )

           # Test not batched input
           _A							:  Optional[Any]    =		image_processing(image_inputs[0]	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

           # Test batched
           _A							:  int    =		image_processing(_a	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

