

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


lowerCamelCase									= logging.get_logger(__name__)







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     str    =       ['pixel_values']



   def __init__(    self,   lowercase_ = True,   lowercase_ = None,   lowercase_ = PIL.Image.NEAREST,   lowercase_ = False,   lowercase_ = None,   lowercase_ = 1 / 255,   lowercase_ = False,   lowercase_ = True,   lowercase_ = True,   lowercase_ = None,   lowercase_ = None,   lowercase_ = True,   **lowercase_,   )							->   None:






     """simple docstring"""






     super().__init__(**lowercase_	)
     a__  =size if size is not None else {'''height''': 346, '''width''': 346}
     a__  =get_size_dict(lowercase_	)
     a__  =crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
     a__  =get_size_dict(lowercase_,   param_name='''crop_size'''	)

     a__  =do_resize
     a__  =size
     a__  =resample
     a__  =do_center_crop
     a__  =crop_size
     a__  =do_rescale
     a__  =rescale_factor
     a__  =rescale_offset
     a__  =do_normalize
     a__  =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     a__  =image_std if image_std is not None else IMAGENET_STANDARD_STD
     a__  =include_top



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_ = PIL.Image.NEAREST,   lowercase_ = None,   **lowercase_,   )							->   np.ndarray:






     """simple docstring"""






     a__  =get_size_dict(lowercase_	)
     if "height" not in size or "width" not in size:
       raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""	)
     return resize(
         lowercase_,   size=(size['''height'''], size['''width''']),   resample=lowercase_,   data_format=lowercase_,   **lowercase_	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_ = None,   **lowercase_,   )							->   np.ndarray:






     """simple docstring"""






     a__  =get_size_dict(lowercase_	)
     if "height" not in size or "width" not in size:
       raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""	)
     return center_crop(lowercase_,   size=(size['''height'''], size['''width''']),   data_format=lowercase_,   **lowercase_	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_ = True,   lowercase_ = None,   **lowercase_,   )							->   str:






     """simple docstring"""






     if offset:
       a__  =(image - 127.5) * scale
       if data_format is not None:
         a__  =to_channel_dimension_format(lowercase_,   lowercase_	)
       a__  =rescaled_image.astype(np.floataa	)
     else:
       a__  =rescale(lowercase_,   scale=lowercase_,   data_format=lowercase_,   **lowercase_	)
     return rescaled_image



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = None,   **lowercase_,   )							->   np.ndarray:






     """simple docstring"""






     return normalize(lowercase_,   mean=lowercase_,   std=lowercase_,   data_format=lowercase_,   **lowercase_	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = None,   lowercase_ = None,   lowercase_=None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = ChannelDimension.FIRST,   **lowercase_,   )							->   PIL.Image.Image:






     """simple docstring"""






     a__  =do_resize if do_resize is not None else self.do_resize
     a__  =resample if resample is not None else self.resample
     a__  =do_center_crop if do_center_crop is not None else self.do_center_crop
     a__  =do_rescale if do_rescale is not None else self.do_rescale
     a__  =rescale_factor if rescale_factor is not None else self.rescale_factor
     a__  =rescale_offset if rescale_offset is not None else self.rescale_offset
     a__  =do_normalize if do_normalize is not None else self.do_normalize
     a__  =image_mean if image_mean is not None else self.image_mean
     a__  =image_std if image_std is not None else self.image_std
     a__  =include_top if include_top is not None else self.include_top

     a__  =size if size is not None else self.size
     a__  =get_size_dict(lowercase_	)
     a__  =crop_size if crop_size is not None else self.crop_size
     a__  =get_size_dict(lowercase_,   param_name='''crop_size'''	)

     a__  =make_list_of_images(lowercase_	)

     if not valid_images(lowercase_	):
       raise ValueError(
           '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
           '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

     if do_resize and size is None or resample is None:
       raise ValueError('''Size and resample must be specified if do_resize is True.'''	)

     if do_center_crop and crop_size is None:
       raise ValueError('''Crop size must be specified if do_center_crop is True.'''	)

     if do_rescale and rescale_factor is None:
       raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('''Image mean and std must be specified if do_normalize is True.'''	)

     # All transformations expect numpy arrays.
     a__  =[to_numpy_array(lowercase_	) for image in images]

     if do_resize:
       a__  =[self.resize(image=lowercase_,   size=lowercase_,   resample=lowercase_	) for image in images]

     if do_center_crop:
       a__  =[self.center_crop(image=lowercase_,   size=lowercase_	) for image in images]

     if do_rescale:
       a__  =[self.rescale(image=lowercase_,   scale=lowercase_,   offset=lowercase_	) for image in images]

     if do_normalize:
       a__  =[self.normalize(image=lowercase_,   mean=lowercase_,   std=lowercase_	) for image in images]

     if include_top:
       a__  =[self.normalize(image=lowercase_,   mean=[0, 0, 0],   std=lowercase_	) for image in images]

     a__  =[to_channel_dimension_format(lowercase_,   lowercase_	) for image in images]

     a__  ={'''pixel_values''': images}
     return BatchFeature(data=lowercase_,   tensor_type=lowercase_	)



from dataclasses import dataclass

lowerCamelCase									= '''0.1'''
lowerCamelCase									= '''Lucia Harcekova'''







@dataclass
class 						__magic_name__      :

   '''simple docstring'''




   lowerCamelCase__			:     int
   lowerCamelCase__			:     int
   lowerCamelCase__			:     str



   def __repr__(    self	)							->   str:






     """simple docstring"""






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







class 						__magic_name__      :

   '''simple docstring'''




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






     """simple docstring"""






     a__  =window_size
     a__  =lookahead_buffer_size
     a__  =self.window_size - self.lookahead_buffer_size



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   list[Token]:






     """simple docstring"""






     a__  =[]
     a__  =''''''

     # 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)
       a__  =self._find_encoding_token(lowercase_,   lowercase_	)

       # 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(lowercase_	) > self.search_buffer_size:
         a__  =search_buffer[-self.search_buffer_size :]

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

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

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =''''''

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

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Token:






     """simple docstring"""






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

     # Initialise result parameters to default values
     a__,				a__  =0, 0

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

     for i, character in enumerate(lowercase_	):
       a__  =len(lowercase_	) - i
       if character == text[0]:
         a__  =self._match_length_from_index(lowercase_,   lowercase_,   0,   lowercase_	)
         # 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:
           a__,				a__  =found_offset, found_length

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   int:






     """simple docstring"""






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


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       # Initialize compressor class
       lowerCamelCase									= LZaaCompressor(window_size=13, lookahead_buffer_size=6)

       # Example
       lowerCamelCase									= '''cabracadabrarrarrad'''
       lowerCamelCase									= lzaa_compressor.compress(TEXT)
       print(lzaa_compressor.compress('''ababcbababaa'''))
       lowerCamelCase									= lzaa_compressor.decompress(compressed_text)
       assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
