code: 
  
  
  """simple docstring"""
  import random
  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():
   import PIL
  
   from transformers import FlavaImageProcessor
   from transformers.image_utils import PILImageResampling
   from transformers.models.flava.image_processing_flava import (
       FLAVA_CODEBOOK_MEAN,
       FLAVA_CODEBOOK_STD,
       FLAVA_IMAGE_MEAN,
       FLAVA_IMAGE_STD,
   )
  else:
   UpperCAmelCase   =	UpperCAmelCase   =	UpperCAmelCase   =	UpperCAmelCase   =	None
  
  
  
  
  class   lowercase__							(				unittest.TestCase							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=7			,      SCREAMING_SNAKE_CASE=3			,      SCREAMING_SNAKE_CASE=30			,      SCREAMING_SNAKE_CASE=400			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=1 / 255			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=FLAVA_IMAGE_MEAN			,      SCREAMING_SNAKE_CASE=FLAVA_IMAGE_STD			,      SCREAMING_SNAKE_CASE=14			,      SCREAMING_SNAKE_CASE=75			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=16			,      SCREAMING_SNAKE_CASE=0.3			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=FLAVA_CODEBOOK_MEAN			,      SCREAMING_SNAKE_CASE=FLAVA_CODEBOOK_STD			,      )     ->     Optional[Any]:
        _lowerCamelCase  :				str      =		size if size is not None else {"""height""": 224, """width""": 224}
        _lowerCamelCase  :				Tuple      =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        _lowerCamelCase  :				List[Any]      =		codebook_size if codebook_size is not None else {"""height""": 112, """width""": 112}
        _lowerCamelCase  :				List[Any]      =		codebook_crop_size if codebook_crop_size is not None else {"""height""": 112, """width""": 112}
  
        _lowerCamelCase  :				int      =		parent
        _lowerCamelCase  :				int      =		batch_size
        _lowerCamelCase  :				Any      =		num_channels
        _lowerCamelCase  :				Dict      =		do_resize
        _lowerCamelCase  :				Optional[int]      =		do_rescale
        _lowerCamelCase  :				Optional[int]      =		rescale_factor
        _lowerCamelCase  :				List[Any]      =		min_resolution
        _lowerCamelCase  :				Optional[int]      =		max_resolution
        _lowerCamelCase  :				Union[str, Any]      =		size
        _lowerCamelCase  :				Union[str, Any]      =		resample if resample is not None else PILImageResampling.BICUBIC
        _lowerCamelCase  :				Optional[Any]      =		do_normalize
        _lowerCamelCase  :				Tuple      =		image_mean
        _lowerCamelCase  :				Tuple      =		image_std
        _lowerCamelCase  :				int      =		do_center_crop
        _lowerCamelCase  :				int      =		crop_size
  
        _lowerCamelCase  :				List[Any]      =		input_size_patches
        _lowerCamelCase  :				List[str]      =		total_mask_patches
        _lowerCamelCase  :				Optional[Any]      =		mask_group_max_patches
        _lowerCamelCase  :				Tuple      =		mask_group_min_patches
        _lowerCamelCase  :				Optional[Any]      =		mask_group_min_aspect_ratio
        _lowerCamelCase  :				List[Any]      =		mask_group_max_aspect_ratio
  
        _lowerCamelCase  :				Union[str, Any]      =		codebook_do_resize
        _lowerCamelCase  :				List[str]      =		codebook_size
        _lowerCamelCase  :				Union[str, Any]      =		codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
        _lowerCamelCase  :				str      =		codebook_do_center_crop
        _lowerCamelCase  :				Optional[int]      =		codebook_crop_size
        _lowerCamelCase  :				Optional[Any]      =		codebook_do_map_pixels
        _lowerCamelCase  :				Optional[Any]      =		codebook_do_normalize
        _lowerCamelCase  :				List[Any]      =		codebook_image_mean
        _lowerCamelCase  :				Dict      =		codebook_image_std
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "resample": self.resample,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "input_size_patches": self.input_size_patches,
            "total_mask_patches": self.total_mask_patches,
            "mask_group_max_patches": self.mask_group_max_patches,
            "mask_group_min_patches": self.mask_group_min_patches,
            "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
            "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
            "codebook_do_resize": self.codebook_do_resize,
            "codebook_size": self.codebook_size,
            "codebook_resample": self.codebook_resample,
            "codebook_do_center_crop": self.codebook_do_center_crop,
            "codebook_crop_size": self.codebook_crop_size,
            "codebook_do_map_pixels": self.codebook_do_map_pixels,
            "codebook_do_normalize": self.codebook_do_normalize,
            "codebook_image_mean": self.codebook_image_mean,
            "codebook_image_std": self.codebook_image_std,
        }
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        return (self.size["height"], self.size["width"])
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        return (
            (self.input_size_patches, self.input_size_patches)
            if not isinstance(self.input_size_patches			,      SCREAMING_SNAKE_CASE)
            else self.input_size_patches
        )
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     str:
        return (self.codebook_size["height"], self.codebook_size["width"])
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class   lowercase__							(				A_   ,unittest.TestCase							):
     __UpperCAmelCase       					=				FlavaImageProcessor if is_vision_available() else None
     __UpperCAmelCase       					=				None
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        _lowerCamelCase  :				Any      =		FlavaImageProcessingTester(self)
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     str:
        return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        _lowerCamelCase  :				Tuple      =		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			,      """resample"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """crop_size"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """do_center_crop"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """do_rescale"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """rescale_factor"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """masking_generator"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """codebook_do_resize"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """codebook_size"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """codebook_resample"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """codebook_do_center_crop"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """codebook_crop_size"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """codebook_do_map_pixels"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """codebook_do_normalize"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """codebook_image_mean"""))
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,      """codebook_image_std"""))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        _lowerCamelCase  :				Optional[Any]      =		self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size			,      {"""height""": 224, """width""": 224})
        self.assertEqual(image_processor.crop_size			,      {"""height""": 224, """width""": 224})
        self.assertEqual(image_processor.codebook_size			,      {"""height""": 112, """width""": 112})
        self.assertEqual(image_processor.codebook_crop_size			,      {"""height""": 112, """width""": 112})
  
        _lowerCamelCase  :				str      =		self.image_processing_class.from_dict(
            self.image_processor_dict			,      size=42			,      crop_size=84			,      codebook_size=33			,      codebook_crop_size=66)
        self.assertEqual(image_processor.size			,      {"""height""": 42, """width""": 42})
        self.assertEqual(image_processor.crop_size			,      {"""height""": 84, """width""": 84})
        self.assertEqual(image_processor.codebook_size			,      {"""height""": 33, """width""": 33})
        self.assertEqual(image_processor.codebook_crop_size			,      {"""height""": 66, """width""": 66})
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        pass
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        # Initialize image_processing
        _lowerCamelCase  :				Optional[int]      =		self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        _lowerCamelCase  :				Optional[Any]      =		prepare_image_inputs(self.image_processor_tester			,      equal_resolution=SCREAMING_SNAKE_CASE)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE			,      PIL.Image.Image)
  
        # Test not batched input
        _lowerCamelCase  :				List[str]      =		image_processing(image_inputs[0]			,      return_tensors="""pt""")
  
        # Test no bool masked pos
        self.assertFalse("""bool_masked_pos""" in encoded_images)
  
        _lowerCamelCase      ,						_lowerCamelCase  :				Any      =		self.image_processor_tester.get_expected_image_size()
  
        self.assertEqual(
            encoded_images.pixel_values.shape			,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,      )
  
        # Test batched
        _lowerCamelCase  :				int      =		image_processing(SCREAMING_SNAKE_CASE			,      return_tensors="""pt""")
        _lowerCamelCase      ,						_lowerCamelCase  :				int      =		self.image_processor_tester.get_expected_image_size()
  
        # Test no bool masked pos
        self.assertFalse("""bool_masked_pos""" in encoded_images)
  
        self.assertEqual(
            encoded_images.pixel_values.shape			,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,      )
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     str:
        # Initialize image_processing
        _lowerCamelCase  :				Optional[int]      =		self.image_processing_class(**self.image_processor_dict)
        # create random tensors
        _lowerCamelCase  :				Union[str, Any]      =		prepare_image_inputs(self.image_processor_tester			,      equal_resolution=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
        # Test not batched input
        _lowerCamelCase  :				Dict      =		image_processing(image_inputs[0]			,      return_tensors="""pt""")
  
        _lowerCamelCase      ,						_lowerCamelCase  :				Optional[Any]      =		self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape			,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,      )
  
        _lowerCamelCase  :				Any      =		image_processing(SCREAMING_SNAKE_CASE			,      return_image_mask=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""")
  
        _lowerCamelCase      ,						_lowerCamelCase  :				List[Any]      =		self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape			,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,      )
  
        _lowerCamelCase      ,						_lowerCamelCase  :				List[Any]      =		self.image_processor_tester.get_expected_mask_size()
        self.assertEqual(
            encoded_images.bool_masked_pos.shape			,      (
                self.image_processor_tester.batch_size,
                expected_height,
                expected_width,
            )			,      )
  
        # Test batched
        _lowerCamelCase  :				Tuple      =		image_processing(SCREAMING_SNAKE_CASE			,      return_tensors="""pt""").pixel_values
  
        _lowerCamelCase      ,						_lowerCamelCase  :				Optional[Any]      =		self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.shape			,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,      )
  
        # Test masking
        _lowerCamelCase  :				Any      =		image_processing(SCREAMING_SNAKE_CASE			,      return_image_mask=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""")
  
        _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape			,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,      )
  
        _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		self.image_processor_tester.get_expected_mask_size()
        self.assertEqual(
            encoded_images.bool_masked_pos.shape			,      (
                self.image_processor_tester.batch_size,
                expected_height,
                expected_width,
            )			,      )
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        self._test_call_framework(np.ndarray			,      prepare_kwargs={"""numpify""": True})
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        self._test_call_framework(torch.Tensor			,      prepare_kwargs={"""torchify""": True})
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        # Initialize image_processing
        random.seed(1234)
        _lowerCamelCase  :				List[Any]      =		self.image_processing_class(**self.image_processor_dict)
        _lowerCamelCase  :				Optional[int]      =		prepare_image_inputs(self.image_processor_tester			,      equal_resolution=SCREAMING_SNAKE_CASE			,      torchify=SCREAMING_SNAKE_CASE)
  
        # Test not batched input
        _lowerCamelCase  :				Optional[Any]      =		image_processing(image_inputs[0]			,      return_image_mask=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""")
        self.assertEqual(encoded_images.bool_masked_pos.sum().item()			,      75)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        # Initialize image_processing
        _lowerCamelCase  :				Tuple      =		self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        _lowerCamelCase  :				Optional[int]      =		prepare_image_inputs(self.image_processor_tester			,      equal_resolution=SCREAMING_SNAKE_CASE)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE			,      PIL.Image.Image)
  
        # Test not batched input
        _lowerCamelCase  :				List[str]      =		image_processing(image_inputs[0]			,      return_codebook_pixels=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""")
        _lowerCamelCase      ,						_lowerCamelCase  :				Union[str, Any]      =		self.image_processor_tester.get_expected_codebook_image_size()
        self.assertEqual(
            encoded_images.codebook_pixel_values.shape			,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,      )
  
        # Test batched
        _lowerCamelCase  :				Tuple      =		image_processing(SCREAMING_SNAKE_CASE			,      return_codebook_pixels=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""")
        _lowerCamelCase      ,						_lowerCamelCase  :				List[Any]      =		self.image_processor_tester.get_expected_codebook_image_size()
        self.assertEqual(
            encoded_images.codebook_pixel_values.shape			,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,      )
  
  
style_context: 
  
  
  """simple docstring"""
  from dataclasses import dataclass
  
  UpperCAmelCase   =	"""0.1"""
  UpperCAmelCase   =	"""Lucia Harcekova"""
  
  
  
  
  @dataclass
  class   lowercase__							:
     __UpperCAmelCase       					=				42
     __UpperCAmelCase       					=				42
     __UpperCAmelCase       					=				42
  
  
  
  
  
     def __repr__(       self)     ->     str:
        return F'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE = 13			,      SCREAMING_SNAKE_CASE = 6)     ->     None:
        _lowerCamelCase  :				Optional[Any]      =		window_size
        _lowerCamelCase  :				List[str]      =		lookahead_buffer_size
        _lowerCamelCase  :				List[Any]      =		self.window_size - self.lookahead_buffer_size
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     list[Token]:
        _lowerCamelCase  :				Union[str, Any]      =		[]
        _lowerCamelCase  :				Tuple      =		""""""
  
        # while there are still characters in text to compress
        while text:
           # find the next encoding phrase
           # - triplet with offset, length, indicator (the next encoding character)
           _lowerCamelCase  :				List[Any]      =		self._find_encoding_token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
           # update the search buffer:
           # - add new characters from text into it
           # - check if size exceed the max search buffer size, if so, drop the
           #   oldest elements
           search_buffer += text[: token.length + 1]
           if len(SCREAMING_SNAKE_CASE) > self.search_buffer_size:
              _lowerCamelCase  :				int      =		search_buffer[-self.search_buffer_size :]
  
           # update the text
           _lowerCamelCase  :				Tuple      =		text[token.length + 1 :]
  
           # append the token to output
           output.append(SCREAMING_SNAKE_CASE)
  
        return output
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     str:
        _lowerCamelCase  :				str      =		""""""
  
        for token in tokens:
           for _ in range(token.length):
              output += output[-token.offset]
           output += token.indicator
  
        return output
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Token:
  
        if not text:
           raise ValueError("""We need some text to work with.""")
  
        # Initialise result parameters to default values
        _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		0, 0
  
        if not search_buffer:
           return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])
  
        for i, character in enumerate(SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				Tuple      =		len(SCREAMING_SNAKE_CASE) - i
           if character == text[0]:
              _lowerCamelCase  :				Any      =		self._match_length_from_index(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      0			,      SCREAMING_SNAKE_CASE)
              # if the found length is bigger than the current or if it's equal,
              # which means it's offset is smaller: update offset and length
              if found_length >= length:
                 _lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		found_offset, found_length
  
        return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     int:
        if not text or text[text_index] != window[window_index]:
           return 0
        return 1 + self._match_length_from_index(
            SCREAMING_SNAKE_CASE			,      window + text[text_index]			,      text_index + 1			,      window_index + 1)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
   # Initialize compressor class
   UpperCAmelCase   =	LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
   # Example
   UpperCAmelCase   =	"""cabracadabrarrarrad"""
   UpperCAmelCase   =	lzaa_compressor.compress(TEXT)
   print(lzaa_compressor.compress("""ababcbababaa"""))
   UpperCAmelCase   =	lzaa_compressor.decompress(compressed_text)
   assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  