



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])



"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class      A(  unittest.TestCase		):

		"""simple docstring"""





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	UNetaDModel(
					    block_out_channels=(32, 64)					,				layers_per_block=2					,				sample_size=32					,				in_channels=3					,				out_channels=3					,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,				)
					return model





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.dummy_uncond_unet
					_UpperCamelCase					:List[Any]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[Any]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:List[Any]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:Dict					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''					,				return_dict=SCREAMING_SNAKE_CASE__   )[0]

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCamelCase					:List[Any]					     =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2






@slow
@require_torch
class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/ncsnpp-celebahq-256'''
					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[int]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=20					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_56, 2_56, 3)
					_UpperCamelCase					:Any					     =	np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2

