





"""simple docstring"""






import math
import sys






def      lowercase__ (					lowerCAmelCase__   :			str			)    ->			str:



			'''simple docstring'''
			a__  :	Dict		 =			""
			try:
						with open(lowerCAmelCase__							,			"rb"			) as binary_file:
									a__  :	List[str]		 =			binary_file.read()
						for dat in data:
									a__  :	Optional[int]		 =			F"{dat:08b}"
									result += curr_byte
						return result
			except OSError:
						print("File not accessible"			)
						sys.exit()






def      lowercase__ (					lowerCAmelCase__   :			str			)    ->			str:



			'''simple docstring'''
			a__  :	int		 =			{"0": "0", "1": "1"}
			a__ ,	a__  :	Any		 =			"", ""
			a__  :	Optional[int]		 =			len(lowerCAmelCase__			)

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

						a__  :	Optional[int]		 =			lexicon[curr_string]
						result += last_match_id
						a__  :	Optional[Any]		 =			last_match_id + "0"

						if math.loga(lowerCAmelCase__			).is_integer():
									a__  :	List[str]		 =			{}
									for curr_key in list(lowerCAmelCase__			):
												a__  :	str		 =			lexicon.pop(lowerCAmelCase__			)
									a__  :	str		 =			new_lex

						a__  :	List[Any]		 =			last_match_id + "1"
						index += 1
						a__  :	str		 =			""
			return result






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



			'''simple docstring'''
			a__  :	List[str]		 =			8
			try:
						with open(lowerCAmelCase__							,			"wb"			) as opened_file:
									a__  :	List[Any]		 =			[
									    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      lowercase__ (					lowerCAmelCase__   :			str			)    ->			str:



			'''simple docstring'''
			a__  :	List[Any]		 =			0
			for letter in data_bits:
						if letter == "1":
									break
						counter += 1

			a__  :	Dict		 =			data_bits[counter:]
			a__  :	List[Any]		 =			data_bits[counter + 1 :]
			return data_bits






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



			'''simple docstring'''
			a__  :	Tuple		 =			read_file_binary(lowerCAmelCase__			)
			a__  :	Tuple		 =			remove_prefix(lowerCAmelCase__			)
			a__  :	Any		 =			decompress_data(lowerCAmelCase__			)
			write_file_binary(lowerCAmelCase__							,			lowerCAmelCase__			)


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






"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__UpperCAmelCase						=			False



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]=32    )	->	Union[str, Any]:



							'''simple docstring'''



							set_seed(0    )
							a__  :	Any		 =			UNetaDModel(sample_size=a_	, in_channels=3	, out_channels=3    )
							a__  :	Union[str, Any]		 =			torch.optim.SGD(model.parameters()	, lr=0.0001    )
							return model, optimizer






				@slow
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							a__  :	Dict		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0    )
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).clip(-1	, 1    ).to(a_    ) for _ in range(4    )]
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).to(a_    ) for _ in range(4    )]
							a__  :	Optional[Any]		 =			[torch.randint(0	, 10_00	, (4,)    ).long().to(a_    ) for _ in range(4    )]

							# train with a DDPM scheduler
							a__ ,	a__  :	Union[str, Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	Optional[int]		 =			ddpm_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	List[str]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Optional[int]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							a__ ,	a__  :	Optional[Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	List[str]		 =			ddim_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	Optional[int]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Union[str, Any]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )