


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 							lowercase__(						unittest.TestCase			):






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=7   ,			__SCREAMING_SNAKE_CASE=3   ,			__SCREAMING_SNAKE_CASE=18   ,			__SCREAMING_SNAKE_CASE=30   ,			__SCREAMING_SNAKE_CASE=4_00   ,			__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]   ,			)					->						Dict:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=size if size is not None else {'height': 18, 'width': 18}
         UpperCamelCase__ :			Union[str, Any] 						=parent
         UpperCamelCase__ :			Optional[Any] 						=batch_size
         UpperCamelCase__ :			List[Any] 						=num_channels
         UpperCamelCase__ :			Any 						=image_size
         UpperCamelCase__ :			List[Any] 						=min_resolution
         UpperCamelCase__ :			List[Any] 						=max_resolution
         UpperCamelCase__ :			List[str] 						=do_resize
         UpperCamelCase__ :			Dict 						=size
         UpperCamelCase__ :			int 						=do_normalize
         UpperCamelCase__ :			Dict 						=image_mean
         UpperCamelCase__ :			Tuple 						=image_std




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         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__(						snake_case__			,							unittest.TestCase			):






      '''simple docstring'''







      snake_case__       =					ViTImageProcessor if is_vision_available() else None




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=ViTImageProcessingTester(self)




      @property
      def    UpperCAmelCase							(		self)					->						List[Any]:







         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







         UpperCamelCase__ :			int 						=self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(_snake_case   ,			"image_mean"))
         self.assertTrue(hasattr(_snake_case   ,			"image_std"))
         self.assertTrue(hasattr(_snake_case   ,			"do_normalize"))
         self.assertTrue(hasattr(_snake_case   ,			"do_resize"))
         self.assertTrue(hasattr(_snake_case   ,			"size"))




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size   ,			{"height": 18, "width": 18})

         UpperCamelCase__ :			Union[str, Any] 						=self.image_processing_class.from_dict(self.image_processor_dict   ,			size=42)
         self.assertEqual(image_processor.size   ,			{"height": 42, "width": 42})




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         pass




      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         UpperCamelCase__ :			Optional[int] 						=prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=_snake_case)
         for image in image_inputs:
            self.assertIsInstance(_snake_case   ,			Image.Image)

         # Test not batched input
         UpperCamelCase__ :			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
         UpperCamelCase__ :			List[str] 						=image_processing(_snake_case   ,			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    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         UpperCamelCase__ :			Union[str, Any] 						=prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=_snake_case   ,			numpify=_snake_case)
         for image in image_inputs:
            self.assertIsInstance(_snake_case   ,			np.ndarray)

         # Test not batched input
         UpperCamelCase__ :			str 						=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
         UpperCamelCase__ :			Dict 						=image_processing(_snake_case   ,			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    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         UpperCamelCase__ :			Tuple 						=prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=_snake_case   ,			torchify=_snake_case)
         for image in image_inputs:
            self.assertIsInstance(_snake_case   ,			torch.Tensor)

         # Test not batched input
         UpperCamelCase__ :			List[str] 						=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
         UpperCamelCase__ :			Union[str, Any] 						=image_processing(_snake_case   ,			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"],
             )   ,			)









from dataclasses import dataclass

UpperCamelCase											=      '''0.1'''
UpperCamelCase											=      '''Lucia Harcekova'''




@dataclass
class 	lowerCamelCase__				:
      lowerCamelCase_							:   int
      lowerCamelCase_							:   int
      lowerCamelCase_							:   str






      def __repr__(self					:    Tuple		)       ->   str:


       """simple docstring"""






       return f'({self.offset}, {self.length}, {self.indicator})'




class 	lowerCamelCase__				:






      def __init__(self					:    Optional[int]		,		_snake_case					:    int = 13		,		_snake_case					:    int = 6		)       ->   None:


       """simple docstring"""






       lowerCamelCase_							:    Any							=     window_size
       lowerCamelCase_							:    List[str]							=     lookahead_buffer_size
       lowerCamelCase_							:    Optional[int]							=     self.window_size - self.lookahead_buffer_size






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    str		)       ->   list[Token]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     []
       lowerCamelCase_							:    List[str]							=     ''

       # 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(_snake_case		,		_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(_snake_case		) > self.search_buffer_size:
         lowerCamelCase_							:    int							=     search_buffer[-self.search_buffer_size :]

        # update the text
        lowerCamelCase_							:    str							=     text[token.length + 1 :]

        # append the token to output
        output.append(_snake_case		)

       return output






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    list[Token]		)       ->   str:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     ''

       for token in tokens:
        for _ in range(token.length		):
         output += output[-token.offset]
        output += token.indicator

       return output






      def      UpperCAmelCase_     (self					:    Dict		,		_snake_case					:    str		,		_snake_case					:    str		)       ->   Token:


       """simple docstring"""






       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(_snake_case		,		_snake_case		,		text[length]		)

       for i, character in enumerate(_snake_case		):
        lowerCamelCase_							:    Union[str, Any]							=     len(_snake_case		) - i
        if character == text[0]:
         lowerCamelCase_							:    str							=     self._match_length_from_index(_snake_case		,		_snake_case		,		0		,		_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_							:    str							=     found_offset, found_length

       return Token(_snake_case		,		_snake_case		,		text[length]		)






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    str		,		_snake_case					:    str		,		_snake_case					:    int		,		_snake_case					:    int		)       ->   int:


       """simple docstring"""






       if not text or text[text_index] != window[window_index]:
        return 0
       return 1 + self._match_length_from_index(
           _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=1_3, 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."
