import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			List[Any]      			=						(LMSDiscreteScheduler,)
			__a  :			int      			=						10





			def 	_SCREAMING_SNAKE_CASE      (       self				,       **lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							{
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**lowerCAmelCase__						)
							return config





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Tuple:

							'''simple docstring'''


							for timesteps in [10, 50, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]				,       [0.0002, 0.002, 0.02]						):
											self.check_over_configs(beta_start=lowerCAmelCase__				,       beta_end=lowerCAmelCase__						)





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

							'''simple docstring'''


							for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=lowerCAmelCase__						)





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

							'''simple docstring'''


							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							for t in [0, 5_00, 8_00]:
											self.check_over_forward(time_step=lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							self.scheduler_classes[0]
							__lowercase			      =							self.get_scheduler_config()
							__lowercase			      =							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase			      =							self.dummy_model()
							__lowercase			      =							self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase			      =							scheduler.scale_model_input(lowerCAmelCase__				,       lowerCAmelCase__						)

											__lowercase			      =							model(lowerCAmelCase__				,       lowerCAmelCase__						)

											__lowercase			      =							scheduler.step(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)
											__lowercase			      =							output.prev_sample

							__lowercase			      =							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase			      =							torch.mean(torch.abs(lowerCAmelCase__						)						)

							assert abs(result_sum.item() - 1006.388						) < 1E-2
							assert abs(result_mean.item() - 1.31						) < 1E-3





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							self.scheduler_classes[0]
							__lowercase			      =							self.get_scheduler_config(prediction_type='''v_prediction'''						)
							__lowercase			      =							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase			      =							self.dummy_model()
							__lowercase			      =							self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase			      =							scheduler.scale_model_input(lowerCAmelCase__				,       lowerCAmelCase__						)

											__lowercase			      =							model(lowerCAmelCase__				,       lowerCAmelCase__						)

											__lowercase			      =							scheduler.step(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)
											__lowercase			      =							output.prev_sample

							__lowercase			      =							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase			      =							torch.mean(torch.abs(lowerCAmelCase__						)						)

							assert abs(result_sum.item() - 0.0017						) < 1E-2
							assert abs(result_mean.item() - 2.2676E-06						) < 1E-3





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							self.scheduler_classes[0]
							__lowercase			      =							self.get_scheduler_config()
							__lowercase			      =							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps				,       device=lowerCAmelCase__						)

							__lowercase			      =							self.dummy_model()
							__lowercase			      =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							__lowercase			      =							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase			      =							scheduler.scale_model_input(lowerCAmelCase__				,       lowerCAmelCase__						)

											__lowercase			      =							model(lowerCAmelCase__				,       lowerCAmelCase__						)

											__lowercase			      =							scheduler.step(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)
											__lowercase			      =							output.prev_sample

							__lowercase			      =							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase			      =							torch.mean(torch.abs(lowerCAmelCase__						)						)

							assert abs(result_sum.item() - 1006.388						) < 1E-2
							assert abs(result_mean.item() - 1.31						) < 1E-3





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							self.scheduler_classes[0]
							__lowercase			      =							self.get_scheduler_config()
							__lowercase			      =							scheduler_class(**lowerCAmelCase__				,       use_karras_sigmas=lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps				,       device=lowerCAmelCase__						)

							__lowercase			      =							self.dummy_model()
							__lowercase			      =							self.dummy_sample_deter.to(lowerCAmelCase__						) * scheduler.init_noise_sigma
							__lowercase			      =							sample.to(lowerCAmelCase__						)

							for t in scheduler.timesteps:
											__lowercase			      =							scheduler.scale_model_input(lowerCAmelCase__				,       lowerCAmelCase__						)

											__lowercase			      =							model(lowerCAmelCase__				,       lowerCAmelCase__						)

											__lowercase			      =							scheduler.step(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)
											__lowercase			      =							output.prev_sample

							__lowercase			      =							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase			      =							torch.mean(torch.abs(lowerCAmelCase__						)						)

							assert abs(result_sum.item() - 3812.9927						) < 2E-2
							assert abs(result_mean.item() - 4.9648						) < 1E-3
import os
import sys

from . import rsa_key_generator as rkg

__a  :					Union[str, Any]          =  1_2_8
__a  :					List[str]          =  2_5_6





def     UpperCAmelCase						(   lowercase							,      lowercase = DEFAULT_BLOCK_SIZE							):







				"""simple docstring"""



				__lowercase			      =							message.encode('''ascii'''							)

				__lowercase			      =							[]
				for block_start in range(0							,      len(lowercase							)							,      lowercase							):
								__lowercase			      =							0
								for i in range(lowercase							,      min(block_start + block_size							,      len(lowercase							)							)							):
												block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
								block_ints.append(lowercase							)
				return block_ints





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase = DEFAULT_BLOCK_SIZE							):







				"""simple docstring"""



				__lowercase			      =							[]
				for block_int in block_ints:
								__lowercase			      =							[]
								for i in range(block_size - 1							,      -1							,      -1							):
												if len(lowercase							) + i < message_length:
																__lowercase			      =							block_int // (BYTE_SIZE**i)
																__lowercase			      =							block_int % (BYTE_SIZE**i)
																block_message.insert(0							,      chr(lowercase							)							)
								message.extend(lowercase							)
				return "".join(lowercase							)





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase = DEFAULT_BLOCK_SIZE							):







				"""simple docstring"""



				__lowercase			      =							[]
				__lowercase      ,					__lowercase			      =							key

				for block in get_blocks_from_text(lowercase							,      lowercase							):
								encrypted_blocks.append(pow(lowercase							,      lowercase							,      lowercase							)							)
				return encrypted_blocks





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							,      lowercase = DEFAULT_BLOCK_SIZE							,      ):







				"""simple docstring"""



				__lowercase			      =							[]
				__lowercase      ,					__lowercase			      =							key
				for block in encrypted_blocks:
								decrypted_blocks.append(pow(lowercase							,      lowercase							,      lowercase							)							)
				return get_text_from_blocks(lowercase							,      lowercase							,      lowercase							)





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				with open(lowercase							) as fo:
								__lowercase			      =							fo.read()
				__lowercase      ,					__lowercase      ,					__lowercase			      =							content.split(''','''							)
				return (int(lowercase							), int(lowercase							), int(lowercase							))





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							,      lowercase = DEFAULT_BLOCK_SIZE							,      ):







				"""simple docstring"""



				__lowercase      ,					__lowercase      ,					__lowercase			      =							read_key_file(lowercase							)
				if key_size < block_size * 8:
								sys.exit(
								    '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
								    '''requires the block size to be equal to or greater than the key size. '''
								    '''Either decrease the block size or use different keys.'''.format(
								        block_size * 8							,      lowercase							)							)

				__lowercase			      =							[str(lowercase							) for i in encrypt_message(lowercase							,      (n, e)							,      lowercase							)]

				__lowercase			      =							''','''.join(lowercase							)
				__lowercase			      =							F"{len(lowercase							)}_{block_size}_{encrypted_content}"
				with open(lowercase							,      '''w'''							) as fo:
								fo.write(lowercase							)
				return encrypted_content





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				__lowercase      ,					__lowercase      ,					__lowercase			      =							read_key_file(lowercase							)
				with open(lowercase							) as fo:
								__lowercase			      =							fo.read()
				__lowercase      ,					__lowercase      ,					__lowercase			      =							content.split('''_'''							)
				__lowercase			      =							int(lowercase							)
				__lowercase			      =							int(lowercase							)

				if key_size < block_size * 8:
								sys.exit(
								    '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
								    '''requires the block size to be equal to or greater than the key size. '''
								    '''Did you specify the correct key file and encrypted file?'''.format(
								        block_size * 8							,      lowercase							)							)

				__lowercase			      =							[]
				for block in encrypted_message.split(''','''							):
								encrypted_blocks.append(int(lowercase							)							)

				return decrypt_message(lowercase							,      lowercase							,      (n, d)							,      lowercase							)





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''encrypted_file.txt'''
				__lowercase			      =							input(r'''Encrypt\Decrypt [e\d]: '''							)

				if response.lower().startswith('''e'''							):
								__lowercase			      =							'''encrypt'''
				elif response.lower().startswith('''d'''							):
								__lowercase			      =							'''decrypt'''

				if mode == "encrypt":
								if not os.path.exists('''rsa_pubkey.txt'''							):
												rkg.make_key_files('''rsa'''							,      1024							)

								__lowercase			      =							input('''\nEnter message: '''							)
								__lowercase			      =							'''rsa_pubkey.txt'''
								print(F"Encrypting and writing to {filename}..."							)
								__lowercase			      =							encrypt_and_write_to_file(lowercase							,      lowercase							,      lowercase							)

								print('''\nEncrypted text:'''							)
								print(lowercase							)

				elif mode == "decrypt":
								__lowercase			      =							'''rsa_privkey.txt'''
								print(F"Reading from {filename} and decrypting..."							)
								__lowercase			      =							read_from_file_and_decrypt(lowercase							,      lowercase							)
								print('''writing decryption to rsa_decryption.txt...'''							)
								with open('''rsa_decryption.txt'''							,      '''w'''							) as dec:
												dec.write(lowercase							)

								print('''\nDecryption:'''							)
								print(lowercase							)


if __name__ == "__main__":
		main()