



'''simple docstring'''




import os
import sys
import time

from . import transposition_cipher as trans_cipher


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		"""Prehistoric Men.txt"""
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		"""Output.txt"""
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		int(input("""Enter key: """ ) )
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		input("""Encrypt/Decrypt [e/d]: """ )

		if not os.path.exists(_lowerCamelCase ):
				print(F"File {input_file} does not exist. Quitting..." )
				sys.exit()
		if os.path.exists(_lowerCamelCase ):
				print(F"Overwrite {output_file}? [y/n]" )
				__SCREAMING_SNAKE_CASE	:		Dict   	=		input("""> """ )
				if not response.lower().startswith("""y""" ):
						sys.exit()

		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		time.time()
		if mode.lower().startswith("""e""" ):
				with open(_lowerCamelCase ) as f:
						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		f.read()
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		trans_cipher.encrypt_message(_lowerCamelCase	,				_lowerCamelCase )
		elif mode.lower().startswith("""d""" ):
				with open(_lowerCamelCase ) as f:
						__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		f.read()
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		trans_cipher.decrypt_message(_lowerCamelCase	,				_lowerCamelCase )

		with open(_lowerCamelCase	,				"""w""" ) as output_obj:
				output_obj.write(_lowerCamelCase )

		__SCREAMING_SNAKE_CASE	:		Tuple   	=		round(time.time() - start_time	,				2 )
		print(("""Done (""", total_time, """seconds )""") )


if __name__ == "__main__":
						main()




'''simple docstring'''




import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		(VQDiffusionScheduler,)

			def UpperCamelCase__ (							self					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		{
					    """num_vec_classes""": 4_0_9_7,
					    """num_train_timesteps""": 1_0_0,
					}

					config.update(**lowerCAmelCase__						)
					return config

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		4
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		8
					__SCREAMING_SNAKE_CASE	:		int   	=		8

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.randint(0							,					lowerCAmelCase__							,					(batch_size, height * width)						)

					return sample

			@property
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					assert False

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					def model(lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					*lowerCAmelCase__					:			Optional[int]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sample.shape
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
							__SCREAMING_SNAKE_CASE	:		str   	=		F.log_softmax(logits.double()							,					dim=1						).float()
							return return_value

					return model

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
							self.check_over_configs(num_vec_classes=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					for t in [0, 5_0, 9_9]:
							self.check_over_forward(time_step=lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass