"""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:
				__lowerCamelCase									=	__lowerCamelCase									=	__lowerCamelCase									=	__lowerCamelCase									=	None
class      UpperCamelCase__(      unittest.TestCase			):





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase=7		,__UpperCAmelCase=3		,__UpperCAmelCase=30		,__UpperCAmelCase=4_00		,__UpperCAmelCase=True		,__UpperCAmelCase=None		,__UpperCAmelCase=True		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=True		,__UpperCAmelCase=1 / 2_55		,__UpperCAmelCase=True		,__UpperCAmelCase=FLAVA_IMAGE_MEAN		,__UpperCAmelCase=FLAVA_IMAGE_STD		,__UpperCAmelCase=14		,__UpperCAmelCase=75		,__UpperCAmelCase=None		,__UpperCAmelCase=16		,__UpperCAmelCase=0.3		,__UpperCAmelCase=None		,__UpperCAmelCase=True		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=True		,__UpperCAmelCase=None		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=FLAVA_CODEBOOK_MEAN		,__UpperCAmelCase=FLAVA_CODEBOOK_STD		,)       ->					Union[str, Any]:
													A__						  =				size if size is not None else {"height": 2_24, "width": 2_24}
													A__						  =				crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
													A__						  =				codebook_size if codebook_size is not None else {"height": 1_12, "width": 1_12}
													A__						  =				codebook_crop_size if codebook_crop_size is not None else {"height": 1_12, "width": 1_12}

													A__						  =				parent
													A__						  =				batch_size
													A__						  =				num_channels
													A__						  =				do_resize
													A__						  =				do_rescale
													A__						  =				rescale_factor
													A__						  =				min_resolution
													A__						  =				max_resolution
													A__						  =				size
													A__						  =				resample if resample is not None else PILImageResampling.BICUBIC
													A__						  =				do_normalize
													A__						  =				image_mean
													A__						  =				image_std
													A__						  =				do_center_crop
													A__						  =				crop_size

													A__						  =				input_size_patches
													A__						  =				total_mask_patches
													A__						  =				mask_group_max_patches
													A__						  =				mask_group_min_patches
													A__						  =				mask_group_min_aspect_ratio
													A__						  =				mask_group_max_aspect_ratio

													A__						  =				codebook_do_resize
													A__						  =				codebook_size
													A__						  =				codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
													A__						  =				codebook_do_center_crop
													A__						  =				codebook_crop_size
													A__						  =				codebook_do_map_pixels
													A__						  =				codebook_do_normalize
													A__						  =				codebook_image_mean
													A__						  =				codebook_image_std





						def 					snake_case__						(							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,
													    "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 					snake_case__						(							self  )       ->					List[Any]:
													return (self.size["height"], self.size["width"])





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													return (
													    (self.input_size_patches, self.input_size_patches)
													    if not isinstance(self.input_size_patches		,UpperCAmelCase_  )
													    else self.input_size_patches
													)





						def 					snake_case__						(							self  )       ->					Dict:
													return (self.codebook_size["height"], self.codebook_size["width"])






@require_torch
@require_vision
class      UpperCamelCase__(      lowercase_		,    unittest.TestCase			):
						lowerCAmelCase__	:    Any    				=		FlavaImageProcessor if is_vision_available() else None
						lowerCAmelCase__	:    List[Any]    				=		None





						def 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				FlavaImageProcessingTester(self  )





						@property
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													return self.image_processor_tester.prepare_image_processor_dict()





						def 					snake_case__						(							self  )       ->					Optional[Any]:
													A__						  =				self.image_processing_class(**self.image_processor_dict  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'image_mean'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'image_std'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'do_normalize'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'do_resize'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'resample'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'crop_size'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'do_center_crop'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'do_rescale'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'rescale_factor'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'masking_generator'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'codebook_do_resize'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'codebook_size'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'codebook_resample'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'codebook_do_center_crop'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'codebook_crop_size'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'codebook_do_map_pixels'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'codebook_do_normalize'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'codebook_image_mean'  )  )
													self.assertTrue(hasattr(UpperCAmelCase_		,'codebook_image_std'  )  )





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__						  =				self.image_processing_class.from_dict(self.image_processor_dict  )
													self.assertEqual(image_processor.size		,{'height': 2_24, 'width': 2_24}  )
													self.assertEqual(image_processor.crop_size		,{'height': 2_24, 'width': 2_24}  )
													self.assertEqual(image_processor.codebook_size		,{'height': 1_12, 'width': 1_12}  )
													self.assertEqual(image_processor.codebook_crop_size		,{'height': 1_12, 'width': 1_12}  )

													A__						  =				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 					snake_case__						(							self  )       ->					Tuple:
													pass





						def 					snake_case__						(							self  )       ->					int:
													A__						  =				self.image_processing_class(**self.image_processor_dict  )
													# create random PIL images
													A__						  =				prepare_image_inputs(self.image_processor_tester		,equal_resolution=UpperCAmelCase_  )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase_		,PIL.Image.Image  )

													# Test not batched input
													A__						  =				image_processing(image_inputs[0]		,return_tensors='pt'  )

													# Test no bool masked pos
													self.assertFalse('bool_masked_pos' in encoded_images  )

													A__						  =				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
													A__						  =				image_processing(UpperCAmelCase_		,return_tensors='pt'  )
													A__						  =				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 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					int:
													A__						  =				self.image_processing_class(**self.image_processor_dict  )
													# create random tensors
													A__						  =				prepare_image_inputs(self.image_processor_tester		,equal_resolution=UpperCAmelCase_		,**UpperCAmelCase_  )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase_		,UpperCAmelCase_  )

													# Test not batched input
													A__						  =				image_processing(image_inputs[0]		,return_tensors='pt'  )

													A__						  =				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)		,)

													A__						  =				image_processing(UpperCAmelCase_		,return_image_mask=UpperCAmelCase_		,return_tensors='pt'  )

													A__						  =				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,
													    )		,)

													A__						  =				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
													A__						  =				image_processing(UpperCAmelCase_		,return_tensors='pt'  ).pixel_values

													A__						  =				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
													A__						  =				image_processing(UpperCAmelCase_		,return_image_mask=UpperCAmelCase_		,return_tensors='pt'  )

													A__						  =				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,
													    )		,)

													A__						  =				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 					snake_case__						(							self  )       ->					Dict:
													self._test_call_framework(np.ndarray		,prepare_kwargs={'numpify': True}  )





						def 					snake_case__						(							self  )       ->					int:
													self._test_call_framework(torch.Tensor		,prepare_kwargs={'torchify': True}  )





						def 					snake_case__						(							self  )       ->					str:
													random.seed(12_34  )
													A__						  =				self.image_processing_class(**self.image_processor_dict  )
													A__						  =				prepare_image_inputs(self.image_processor_tester		,equal_resolution=UpperCAmelCase_		,torchify=UpperCAmelCase_  )

													# Test not batched input
													A__						  =				image_processing(image_inputs[0]		,return_image_mask=UpperCAmelCase_		,return_tensors='pt'  )
													self.assertEqual(encoded_images.bool_masked_pos.sum().item()		,75  )





						def 					snake_case__						(							self  )       ->					Optional[int]:
													A__						  =				self.image_processing_class(**self.image_processor_dict  )
													# create random PIL images
													A__						  =				prepare_image_inputs(self.image_processor_tester		,equal_resolution=UpperCAmelCase_  )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase_		,PIL.Image.Image  )

													# Test not batched input
													A__						  =				image_processing(image_inputs[0]		,return_codebook_pixels=UpperCAmelCase_		,return_tensors='pt'  )
													A__						  =				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
													A__						  =				image_processing(UpperCAmelCase_		,return_codebook_pixels=UpperCAmelCase_		,return_tensors='pt'  )
													A__						  =				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,
													    )		,)













"""simple docstring"""





from dataclasses import dataclass

UpperCamelCase					=     """0.1"""
UpperCamelCase					=     """Lucia Harcekova"""







@dataclass
class 						_a  :





   '''simple docstring'''






   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							42





   def __repr__(       self)			->   str:




          '''simple docstring'''




          return F"""({self.offset}, {self.length}, {self.indicator})"""







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_ = 13					, UpperCAmelCase_ = 6)			->   None:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     window_size
          lowercase__:							Any         =     lookahead_buffer_size
          lowercase__:							Optional[Any]         =     self.window_size - self.lookahead_buffer_size





   def __lowercase							(       self					, UpperCAmelCase_)			->   list[Token]:




          '''simple docstring'''




          lowercase__:							Any         =     []
          lowercase__:							Union[str, Any]         =     ""

          # 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)
                 lowercase__:							Optional[Any]         =     self._find_encoding_token(UpperCAmelCase_					, UpperCAmelCase_)

                 # 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(UpperCAmelCase_) > self.search_buffer_size:
                        lowercase__:							List[Any]         =     search_buffer[-self.search_buffer_size :]

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

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

          return output





   def __lowercase							(       self					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__:							int         =     ""

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

          return output





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   Token:




          '''simple docstring'''




          if not text:
                 raise ValueError("We need some text to work with.")

          # Initialise result parameters to default values
          lowercase__    ,      lowercase__:							Optional[int]         =     0, 0

          if not search_buffer:
                 return Token(UpperCAmelCase_					, UpperCAmelCase_					, text[length])

          for i, character in enumerate(UpperCAmelCase_):
                 lowercase__:							Union[str, Any]         =     len(UpperCAmelCase_) - i
                 if character == text[0]:
                        lowercase__:							Optional[Any]         =     self._match_length_from_index(UpperCAmelCase_					, UpperCAmelCase_					, 0					, UpperCAmelCase_)
                        # 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:
                               lowercase__    ,      lowercase__:							Any         =     found_offset, found_length

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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   int:




          '''simple docstring'''




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






