from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__		:					Dict						=					logging.get_logger(__name__)

class       snake_case					(      lowerCAmelCase__						):
  lowercase_			=     ["pixel_values"]




  def __init__(      self     :	int     , a_     :	Any = True     , a_     :	Dict = 32     , a_     :	str=PILImageResampling.BILINEAR     , a_     :	Optional[Any] = True     , **a_     :	int     , )->       None:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		do_resize
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		do_rescale
       SCREAMING_SNAKE_CASE__				:				Tuple			=		size_divisor
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		resample
       super().__init__(**_SCREAMING_SNAKE_CASE				)




  def 	__lowercase(      self     :	Optional[int]     , a_     :	Optional[int]     , a_     :	List[str]     , a_     :	List[Any]     , a_     :	str = None     , **a_     :	int				)->       np.ndarray:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		get_image_size(_SCREAMING_SNAKE_CASE				)
       # Rounds the height and width down to the closest multiple of size_divisor
       SCREAMING_SNAKE_CASE__				:				Any			=		height // size_divisor * size_divisor
       SCREAMING_SNAKE_CASE__				:				str			=		width // size_divisor * size_divisor
       SCREAMING_SNAKE_CASE__				:				str			=		resize(_SCREAMING_SNAKE_CASE     , (new_h, new_w)     , resample=_SCREAMING_SNAKE_CASE     , data_format=_SCREAMING_SNAKE_CASE     , **_SCREAMING_SNAKE_CASE				)
       return image




  def 	__lowercase(      self     :	Dict     , a_     :	Optional[Any]     , a_     :	List[str]     , a_     :	str = None     , **a_     :	int				)->       np.ndarray:




       """simple docstring"""
       return rescale(image=_SCREAMING_SNAKE_CASE     , scale=_SCREAMING_SNAKE_CASE     , data_format=_SCREAMING_SNAKE_CASE     , **_SCREAMING_SNAKE_CASE				)




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	List[str]     , a_     :	str = None     , a_     :	Optional[Any] = None     , a_     :	Any=None     , a_     :	List[Any] = None     , a_     :	Dict = None     , a_     :	Union[str, Any] = ChannelDimension.FIRST     , **a_     :	Tuple     , )->       BatchFeature:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		size_divisor if size_divisor is not None else self.size_divisor
       SCREAMING_SNAKE_CASE__				:				List[str]			=		resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
            raise ValueError('size_divisor is required for resizing'				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		make_list_of_images(_SCREAMING_SNAKE_CASE				)

       if not valid_images(_SCREAMING_SNAKE_CASE				):
            raise ValueError('Invalid image(s)'				)

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__				:				int			=		[to_numpy_array(_SCREAMING_SNAKE_CASE				) for img in images]

       if do_resize:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[self.resize(_SCREAMING_SNAKE_CASE     , size_divisor=_SCREAMING_SNAKE_CASE     , resample=_SCREAMING_SNAKE_CASE				) for image in images]

       if do_rescale:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[self.rescale(_SCREAMING_SNAKE_CASE     , scale=1 / 255				) for image in images]

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[to_channel_dimension_format(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE				) for image in images]

       SCREAMING_SNAKE_CASE__				:				List[str]			=		{'pixel_values': images}
       return BatchFeature(data=_SCREAMING_SNAKE_CASE     , tensor_type=_SCREAMING_SNAKE_CASE				)




import math
import sys







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		''
     try:
          with open(lowercase__     ,  'rb' ) as binary_file:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		binary_file.read()
          for dat in data:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[str]			=		{'0': '0', '1': '1'}
     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				int			=		'', ''
     SCREAMING_SNAKE_CASE__				:				Tuple			=		len(lowercase__ )

     for i in range(len(lowercase__ ) ):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          SCREAMING_SNAKE_CASE__				:				int			=		lexicon[curr_string]
          result += last_match_id
          SCREAMING_SNAKE_CASE__				:				str			=		last_match_id + '0'

          if math.loga(lowercase__ ).is_integer():
               SCREAMING_SNAKE_CASE__				:				List[str]			=		{}
               for curr_key in list(lowercase__ ):
                    SCREAMING_SNAKE_CASE__				:				Optional[int]			=		lexicon.pop(lowercase__ )
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		new_lex

          SCREAMING_SNAKE_CASE__				:				Any			=		last_match_id + '1'
          index += 1
          SCREAMING_SNAKE_CASE__				:				Tuple			=		''
     return result







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		8
     try:
          with open(lowercase__     ,  'wb' ) as opened_file:
               SCREAMING_SNAKE_CASE__				:				Dict			=		[
                   to_write[i : i + byte_length]
                   for i in range(0     ,  len(lowercase__ )     ,  lowercase__ )
               ]

               if len(result_byte_array[-1] ) % byte_length == 0:
                    result_byte_array.append('10000000' )
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1] ) - 1
                    )

               for elem in result_byte_array[:-1]:
                    opened_file.write(int(lowercase__     ,  2 ).to_bytes(1     ,  byteorder='big' ) )
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		0
     for letter in data_bits:
          if letter == "1":
               break
          counter += 1

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		data_bits[counter:]
     SCREAMING_SNAKE_CASE__				:				int			=		data_bits[counter + 1 :]
     return data_bits







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		read_file_binary(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Dict			=		remove_prefix(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Tuple			=		decompress_data(lowercase__ )
     write_file_binary(lowercase__     ,  lowercase__ )


if __name__ == "__main__":
      compress(sys.argv[1], sys.argv[2])



