



import os
from collections import deque

import torch
from torch.utils.data import Dataset



class     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_="",      SCREAMING_SNAKE_CASE_="train"	)					->		Tuple:
          assert os.path.isdir(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     []
          UpperCamelCase   :  str						     =     os.listdir(SCREAMING_SNAKE_CASE_	)
          for story_filename in story_filenames_list:
             if "summary" in story_filename:
                continue
             UpperCamelCase   :  List[Any]						     =     os.path.join(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             if not os.path.isfile(SCREAMING_SNAKE_CASE_	):
                continue
             self.documents.append(SCREAMING_SNAKE_CASE_	)
       def __len__(      self	)					->		List[Any]:
          return len(self.documents	)






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






def 		UpperCamelCase     (       snake_case__	:   int     )     ->					Optional[Any]:
   UpperCamelCase   :  Union[str, Any]						     =     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
   UpperCamelCase   :  Any						     =     [_add_missing_period(snake_case__     ) for line in nonempty_lines]

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

   return story_lines, summary_lines
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					Optional[Any]:
   UpperCamelCase   :  Dict						     =     ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
   if line.startswith('@highlight'     ):
      return line
   if line[-1] in END_TOKENS:
      return line
   return line + "."
def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   List[Any]	,					snake_case__	:   List[Any]     )     ->					Dict:
   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__	:   Optional[Any]	,					snake_case__	:   Tuple     )     ->					Union[str, Any]:
   UpperCamelCase   :  Optional[Any]						     =     torch.ones_like(snake_case__     )
   UpperCamelCase   :  List[Any]						     =     sequence == pad_token_id
   UpperCamelCase   :  List[str]						     =     0
   return mask
def 		UpperCamelCase     (       snake_case__	:   Optional[int]	,					snake_case__	:   str	,					snake_case__	:   Optional[Any]     )     ->					List[Any]:
   UpperCamelCase   :  Dict						     =     [tokenizer.encode(snake_case__     ) for line in story_lines]
   UpperCamelCase   :  Any						     =     [token for sentence in story_lines_token_ids for token in sentence]
   UpperCamelCase   :  Optional[int]						     =     [tokenizer.encode(snake_case__     ) for line in summary_lines]
   UpperCamelCase   :  Any						     =     [token for sentence in summary_lines_token_ids for token in sentence]

   return story_token_ids, summary_token_ids


def 		UpperCamelCase     (       snake_case__	:   Any	,					snake_case__	:   Dict     )     ->					str:
   UpperCamelCase   :  Tuple						     =     []
   for sequence in batch:
      UpperCamelCase   :  Optional[int]						     =     -1
      UpperCamelCase   :  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.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

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 ViTImageProcessor



class     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=224,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=400,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      )					->		List[str]:
          UpperCamelCase   :  Optional[int]						     =     size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase   :  List[Any]						     =     parent
          UpperCamelCase   :  List[Any]						     =     batch_size
          UpperCamelCase   :  int						     =     num_channels
          UpperCamelCase   :  int						     =     image_size
          UpperCamelCase   :  List[Any]						     =     min_resolution
          UpperCamelCase   :  int						     =     max_resolution
          UpperCamelCase   :  Any						     =     do_resize
          UpperCamelCase   :  Optional[int]						     =     size
          UpperCamelCase   :  List[str]						     =     do_normalize
          UpperCamelCase   :  Optional[Any]						     =     image_mean
          UpperCamelCase   :  Tuple						     =     image_std






       def  snake_case_	(      self	)					->		List[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     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Optional[Any]       					=  ViTImageProcessor if is_vision_available() else None
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Dict						     =     EfficientFormerImageProcessorTester(self	)
       @property
       def  snake_case_	(      self	)					->		List[Any]:
          return self.image_proc_tester.prepare_image_processor_dict()
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Optional[int]						     =     self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_mean'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_std'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_normalize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_resize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'size'	)	)
       def  snake_case_	(      self	)					->		Any:
          pass
       def  snake_case_	(      self	)					->		int:
          # Initialize image_processor
          UpperCamelCase   :  Union[str, Any]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          UpperCamelCase   :  List[str]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      Image.Image	)

          # Test not batched input
          UpperCamelCase   :  str						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Optional[Any]						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )
       def  snake_case_	(      self	)					->		str:
          # Initialize image_processor
          UpperCamelCase   :  List[str]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          UpperCamelCase   :  Union[str, Any]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      numpify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)

          # Test not batched input
          UpperCamelCase   :  Dict						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Dict						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )






       def  snake_case_	(      self	)					->		Tuple:
          # Initialize image_processor
          UpperCamelCase   :  Any						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          UpperCamelCase   :  int						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      torchify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor	)

          # Test not batched input
          UpperCamelCase   :  Optional[int]						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  int						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )



