

import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
 import numpy as np
 import torch

if is_vision_available():
 from PIL import Image







def 		_lowercase			(					lowercase__       ,     lowercase__=False       ,     lowercase__=False       ,     lowercase__=False						):
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      __lowerCAmelCase :						List[str] = []
      for i in range(image_processor_tester.batch_size						):
            if equal_resolution:
                  __lowerCAmelCase :						Any = image_processor_tester.max_resolution
            else:
                  # To avoid getting image width/height 0
                  __lowerCAmelCase :						List[Any] = image_processor_tester.min_resolution
                  if getattr(lowercase__       ,     '''size_divisor'''       ,     lowercase__						):
                        # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                        __lowerCAmelCase :						List[Any] = max(image_processor_tester.size_divisor       ,     lowercase__						)
                  __lowerCAmelCase,							__lowerCAmelCase :						List[Any] = np.random.choice(np.arange(lowercase__       ,     image_processor_tester.max_resolution						)       ,     2						)
            image_inputs.append(
                np.random.randint(2_5_5       ,     size=(image_processor_tester.num_channels, width, height)       ,     dtype=np.uinta						)						)

      if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            __lowerCAmelCase :						int = [Image.fromarray(np.moveaxis(lowercase__       ,     0       ,     -1						)						) for image in image_inputs]

      if torchify:
            __lowerCAmelCase :						Any = [torch.from_numpy(lowercase__						) for image in image_inputs]

      return image_inputs







def 		_lowercase			(					lowercase__       ,     lowercase__=1_0       ,     lowercase__=1_0       ,     lowercase__=False       ,     lowercase__=False						):
      __lowerCAmelCase :						Tuple = []
      for i in range(image_processor_tester.num_frames						):
            video.append(np.random.randint(2_5_5       ,     size=(image_processor_tester.num_channels, width, height)       ,     dtype=np.uinta						)						)

      if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            __lowerCAmelCase :						Union[str, Any] = [Image.fromarray(np.moveaxis(lowercase__       ,     0       ,     -1						)						) for frame in video]

      if torchify:
            __lowerCAmelCase :						Tuple = [torch.from_numpy(lowercase__						) for frame in video]

      return video







def 		_lowercase			(					lowercase__       ,     lowercase__=False       ,     lowercase__=False       ,     lowercase__=False						):
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      __lowerCAmelCase :						Dict = []
      for i in range(image_processor_tester.batch_size						):
            if equal_resolution:
                  __lowerCAmelCase :						Dict = image_processor_tester.max_resolution
            else:
                  __lowerCAmelCase,							__lowerCAmelCase :						List[Any] = np.random.choice(
                      np.arange(image_processor_tester.min_resolution       ,     image_processor_tester.max_resolution						)       ,     2						)
                  __lowerCAmelCase :						Any = prepare_video(
                      image_processor_tester=lowercase__       ,     width=lowercase__       ,     height=lowercase__       ,     numpify=lowercase__       ,     torchify=lowercase__       ,     )
            video_inputs.append(lowercase__						)

      return video_inputs







class   __lowercase				:
 _UpperCamelCase        =  None
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = self.image_processing_class(**self.image_processor_dict )
       __lowerCAmelCase :						Tuple = json.loads(image_processor.to_json_string() )
       for key, value in self.image_processor_dict.items():
             self.assertEqual(obj[key]				,							A_ )
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						str = self.image_processing_class(**self.image_processor_dict )

       with tempfile.TemporaryDirectory() as tmpdirname:
             __lowerCAmelCase :						Any = os.path.join(A_				,							'''image_processor.json''' )
             image_processor_first.to_json_file(A_ )
             __lowerCAmelCase :						int = self.image_processing_class.from_json_file(A_ )

       self.assertEqual(image_processor_second.to_dict()				,							image_processor_first.to_dict() )
 def      UpperCamelCase__  (							self ) ->Dict:



       '''simple docstring'''





       __lowerCAmelCase :						str = self.image_processing_class(**self.image_processor_dict )

       with tempfile.TemporaryDirectory() as tmpdirname:
             __lowerCAmelCase :						Tuple = image_processor_first.save_pretrained(A_ )[0]
             check_json_file_has_correct_format(A_ )
             __lowerCAmelCase :						List[Any] = self.image_processing_class.from_pretrained(A_ )

       self.assertEqual(image_processor_second.to_dict()				,							image_processor_first.to_dict() )
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = self.image_processing_class()
       self.assertIsNotNone(A_ )





 @require_torch
 @require_vision
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       if self.test_cast_dtype is not None:
             # Initialize image_processor
             __lowerCAmelCase :						Any = self.image_processing_class(**self.image_processor_dict )

             # create random PyTorch tensors
             __lowerCAmelCase :						Optional[int] = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=A_				,							torchify=A_ )

             __lowerCAmelCase :						str = image_processor(A_				,							return_tensors='''pt''' )
             # for layoutLM compatiblity
             self.assertEqual(encoding.pixel_values.device				,							torch.device('''cpu''' ) )
             self.assertEqual(encoding.pixel_values.dtype				,							torch.floataa )

             __lowerCAmelCase :						Union[str, Any] = image_processor(A_				,							return_tensors='''pt''' ).to(torch.floataa )
             self.assertEqual(encoding.pixel_values.device				,							torch.device('''cpu''' ) )
             self.assertEqual(encoding.pixel_values.dtype				,							torch.floataa )

             __lowerCAmelCase :						Tuple = image_processor(A_				,							return_tensors='''pt''' ).to('''cpu'''				,							torch.bfloataa )
             self.assertEqual(encoding.pixel_values.device				,							torch.device('''cpu''' ) )
             self.assertEqual(encoding.pixel_values.dtype				,							torch.bfloataa )

             with self.assertRaises(A_ ):
                   __lowerCAmelCase :						Any = image_processor(A_				,							return_tensors='''pt''' ).to(torch.bfloataa				,							'''cpu''' )

             # Try with text + image feature
             __lowerCAmelCase :						List[Any] = image_processor(A_				,							return_tensors='''pt''' )
             encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]] )} )
             __lowerCAmelCase :						Optional[Any] = encoding.to(torch.floataa )

             self.assertEqual(encoding.pixel_values.device				,							torch.device('''cpu''' ) )
             self.assertEqual(encoding.pixel_values.dtype				,							torch.floataa )
             self.assertEqual(encoding.input_ids.dtype				,							torch.long )





from dataclasses import dataclass

_UpperCamelCase			      =		"0.1"
_UpperCamelCase			      =		"Lucia Harcekova"







@dataclass
class   __lowercase				:
 _UpperCamelCase        =  42
 _UpperCamelCase        =  42
 _UpperCamelCase        =  42
 def __repr__(							self ) ->str:



       '''simple docstring'''





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







class   __lowercase				:
 def __init__(							self				,							A_ = 13				,							A_ = 6 ) ->None:



       '''simple docstring'''





       __lowerCAmelCase :						List[str] = window_size
       __lowerCAmelCase :						str = lookahead_buffer_size
       __lowerCAmelCase :						Dict = self.window_size - self.lookahead_buffer_size
 def      UpperCamelCase__  (							self				,							A_ ) ->list[Token]:



       '''simple docstring'''





       __lowerCAmelCase :						int = []
       __lowerCAmelCase :						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)
             __lowerCAmelCase :						Optional[int] = self._find_encoding_token(A_				,							A_ )

             # 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(A_ ) > self.search_buffer_size:
                   __lowerCAmelCase :						Dict = search_buffer[-self.search_buffer_size :]

             # update the text
             __lowerCAmelCase :						List[Any] = text[token.length + 1 :]

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

       return output
 def      UpperCamelCase__  (							self				,							A_ ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = ''''''

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

       return output
 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Token:



       '''simple docstring'''





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

       # Initialise result parameters to default values
       __lowerCAmelCase,							__lowerCAmelCase :						Union[str, Any] = 0, 0

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

       for i, character in enumerate(A_ ):
             __lowerCAmelCase :						Dict = len(A_ ) - i
             if character == text[0]:
                   __lowerCAmelCase :						Dict = self._match_length_from_index(A_				,							A_				,							0				,							A_ )
                   # 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 :						int = found_offset, found_length

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





 def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_ ) ->int:



       '''simple docstring'''





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


