from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
      import PIL

if is_torch_available():
      import torch


lowercase_  : List[str]								=					logging.get_logger(__name__)






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   ["pixel_values"]
    def __init__(	self			,			lowerCAmelCase = True			,			lowerCAmelCase = None			,			lowerCAmelCase = PILImageResampling.BILINEAR			,			lowerCAmelCase = True			,			lowerCAmelCase = 1 / 255			,			lowerCAmelCase = True			,			lowerCAmelCase = None			,			lowerCAmelCase = True			,			**lowerCAmelCase			,			)							->       None:
     super().__init__(**lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Tuple=   size if size is not None else {'''shortest_edge''': 224}
     SCREAMING_SNAKE_CASE__: Any=   get_size_dict(lowerCAmelCase			,			default_to_square=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[int]=   crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
     SCREAMING_SNAKE_CASE__: Tuple=   get_size_dict(lowerCAmelCase			,			param_name='''crop_size''' )

     SCREAMING_SNAKE_CASE__: List[str]=   do_resize
     SCREAMING_SNAKE_CASE__: List[Any]=   size
     SCREAMING_SNAKE_CASE__: Optional[int]=   resample
     SCREAMING_SNAKE_CASE__: Any=   do_rescale
     SCREAMING_SNAKE_CASE__: str=   rescale_factor
     SCREAMING_SNAKE_CASE__: str=   do_center_crop
     SCREAMING_SNAKE_CASE__: int=   crop_size
     SCREAMING_SNAKE_CASE__: List[str]=   do_flip_channel_order
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = PIL.Image.BILINEAR			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       np.ndarray:
     SCREAMING_SNAKE_CASE__: Any=   get_size_dict(lowerCAmelCase			,			default_to_square=lowerCAmelCase )
     if "shortest_edge" not in size:
      raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}' )
     SCREAMING_SNAKE_CASE__: Dict=   get_resize_output_image_size(lowerCAmelCase			,			size=size['''shortest_edge''']			,			default_to_square=lowerCAmelCase )
     return resize(lowerCAmelCase			,			size=lowerCAmelCase			,			resample=lowerCAmelCase			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       np.ndarray:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   get_size_dict(lowerCAmelCase )
     if "height" not in size or "width" not in size:
      raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
     return center_crop(lowerCAmelCase			,			size=(size['''height'''], size['''width'''])			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       List[Any]:
     return rescale(lowerCAmelCase			,			scale=lowerCAmelCase			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None )							->       np.ndarray:
     return flip_channel_order(lowerCAmelCase			,			data_format=lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = ChannelDimension.FIRST			,			**lowerCAmelCase			,			)							->       PIL.Image.Image:
     SCREAMING_SNAKE_CASE__: int=   do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__: List[str]=   resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__: List[str]=   do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__: Optional[int]=   rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__: str=   do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__: Optional[Any]=   (
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

     SCREAMING_SNAKE_CASE__: List[Any]=   size if size is not None else self.size
     SCREAMING_SNAKE_CASE__: int=   get_size_dict(lowerCAmelCase			,			default_to_square=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[int]=   crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__: Optional[Any]=   get_size_dict(lowerCAmelCase			,			param_name='''crop_size''' )

     SCREAMING_SNAKE_CASE__: Tuple=   make_list_of_images(lowerCAmelCase )

     if not valid_images(lowerCAmelCase ):
      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:
      raise ValueError('''Size must be specified if do_resize is True.''' )

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

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

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__: Dict=   [to_numpy_array(lowerCAmelCase ) for image in images]

     if do_resize:
      SCREAMING_SNAKE_CASE__: Any=   [self.resize(image=lowerCAmelCase			,			size=lowerCAmelCase			,			resample=lowerCAmelCase ) for image in images]

     if do_center_crop:
      SCREAMING_SNAKE_CASE__: Dict=   [self.center_crop(image=lowerCAmelCase			,			size=lowerCAmelCase ) for image in images]

     if do_rescale:
      SCREAMING_SNAKE_CASE__: Dict=   [self.rescale(image=lowerCAmelCase			,			scale=lowerCAmelCase ) for image in images]

     # the pretrained checkpoints assume images are BGR, not RGB
     if do_flip_channel_order:
      SCREAMING_SNAKE_CASE__: Any=   [self.flip_channel_order(image=lowerCAmelCase ) for image in images]

     SCREAMING_SNAKE_CASE__: int=   [to_channel_dimension_format(lowerCAmelCase			,			lowerCAmelCase ) for image in images]

     SCREAMING_SNAKE_CASE__: Dict=   {'''pixel_values''': images}
     return BatchFeature(data=lowerCAmelCase			,			tensor_type=lowerCAmelCase )



    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
      if len(lowerCAmelCase ) != len(lowerCAmelCase ):
       raise ValueError(
           '''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )

      if is_torch_tensor(lowerCAmelCase ):
       SCREAMING_SNAKE_CASE__: Any=   target_sizes.numpy()

      SCREAMING_SNAKE_CASE__: Any=   []

      for idx in range(len(lowerCAmelCase ) ):
       SCREAMING_SNAKE_CASE__: str=   torch.nn.functional.interpolate(
           logits[idx].unsqueeze(dim=0 )			,			size=target_sizes[idx]			,			mode='''bilinear'''			,			align_corners=lowerCAmelCase )
       SCREAMING_SNAKE_CASE__: List[Any]=   resized_logits[0].argmax(dim=0 )
       semantic_segmentation.append(lowerCAmelCase )
     else:
      SCREAMING_SNAKE_CASE__: List[Any]=   logits.argmax(dim=1 )
      SCREAMING_SNAKE_CASE__: List[Any]=   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

     return semantic_segmentation






import math
import sys






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    ''
							try:
														with open(lowerCAmelCase__			,	'rb'				) as binary_file:
																					A      						=    binary_file.read()
														for dat in data:
																					A      						=    F'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print('File not accessible'				)
														sys.exit()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    {'0': '0', '1': '1'}
							A						,   A      						=    '', ''
							A      						=    len(lowerCAmelCase__				)

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

														A      						=    lexicon[curr_string]
														result += last_match_id
														A      						=    last_match_id + '0'

														if math.loga(lowerCAmelCase__				).is_integer():
																					A      						=    {}
																					for curr_key in list(lowerCAmelCase__				):
																												A      						=    lexicon.pop(lowerCAmelCase__				)
																					A      						=    new_lex

														A      						=    last_match_id + '1'
														index += 1
														A      						=    ''
							return result






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str				)       ->							None:

							'''simple docstring'''






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

																					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(lowerCAmelCase__			,	2				).to_bytes(1			,	byteorder='big'				)				)
							except OSError:
														print('File not accessible'				)
														sys.exit()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






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

							A      						=    data_bits[counter:]
							A      						=    data_bits[counter + 1 :]
							return data_bits






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str				)       ->							None:

							'''simple docstring'''






							A      						=    read_file_binary(lowerCAmelCase__				)
							A      						=    remove_prefix(lowerCAmelCase__				)
							A      						=    decompress_data(lowerCAmelCase__				)
							write_file_binary(lowerCAmelCase__			,	lowerCAmelCase__				)


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