import os
import sys

from . import rsa_key_generator as rkg

lowercase__    :       int						=			1_2_8
lowercase__    :       List[Any]						=			2_5_6

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__ = DEFAULT_BLOCK_SIZE							)  ->			list[int]:
							lowerCAmelCase				=	message.encode('''ascii'''							)

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

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__ = DEFAULT_BLOCK_SIZE							)  ->			str:
							lowerCAmelCase				=	[]
							for block_int in block_ints:
														lowerCAmelCase				=	[]
														for i in range(block_size - 1     ,						-1     ,						-1							):
																					if len(snake_case__							) + i < message_length:
																												lowerCAmelCase				=	block_int // (BYTE_SIZE**i)
																												lowerCAmelCase				=	block_int % (BYTE_SIZE**i)
																												block_message.insert(0     ,						chr(snake_case__							)							)
														message.extend(snake_case__							)
							return "".join(snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__ = DEFAULT_BLOCK_SIZE							)  ->			list[int]:
							lowerCAmelCase				=	[]
							lowerCAmelCase	,   lowerCAmelCase				=	key

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

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__ = DEFAULT_BLOCK_SIZE     ,						)  ->			str:
							lowerCAmelCase				=	[]
							lowerCAmelCase	,   lowerCAmelCase				=	key
							for block in encrypted_blocks:
														decrypted_blocks.append(pow(snake_case__     ,						snake_case__     ,						snake_case__							)							)
							return get_text_from_blocks(snake_case__     ,						snake_case__     ,						snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			tuple[int, int, int]:
							with open(snake_case__							) as fo:
														lowerCAmelCase				=	fo.read()
							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	content.split(''','''							)
							return (int(snake_case__							), int(snake_case__							), int(snake_case__							))

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__ = DEFAULT_BLOCK_SIZE     ,						)  ->			str:
							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	read_key_file(snake_case__							)
							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     ,						snake_case__							)							)

							lowerCAmelCase				=	[str(snake_case__							) for i in encrypt_message(snake_case__     ,						(n, e)     ,						snake_case__							)]

							lowerCAmelCase				=	''','''.join(snake_case__							)
							lowerCAmelCase				=	f"{len(snake_case__							)}_{block_size}_{encrypted_content}"
							with open(snake_case__     ,						'''w'''							) as fo:
														fo.write(snake_case__							)
							return encrypted_content

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			str:
							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	read_key_file(snake_case__							)
							with open(snake_case__							) as fo:
														lowerCAmelCase				=	fo.read()
							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	content.split('''_'''							)
							lowerCAmelCase				=	int(snake_case__							)
							lowerCAmelCase				=	int(snake_case__							)

							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     ,						snake_case__							)							)

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

							return decrypt_message(snake_case__     ,						snake_case__     ,						(n, d)     ,						snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		)  ->			None:
							lowerCAmelCase				=	'''encrypted_file.txt'''
							lowerCAmelCase				=	input(R'''Encrypt\Decrypt [e\d]: '''							)

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

							if mode == "encrypt":
														if not os.path.exists('''rsa_pubkey.txt'''							):
																					rkg.make_key_files('''rsa'''     ,						1_0_2_4							)

														lowerCAmelCase				=	input('''\nEnter message: '''							)
														lowerCAmelCase				=	'''rsa_pubkey.txt'''
														print(f"Encrypting and writing to {filename}..."							)
														lowerCAmelCase				=	encrypt_and_write_to_file(snake_case__     ,						snake_case__     ,						snake_case__							)

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

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

														print('''\nDecryption:'''							)
														print(snake_case__							)


if __name__ == "__main__":
	main()




from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


lowercase__    :       List[Any]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      torch.FloatTensor




class      lowercase_						(						UpperCamelCase_   ,  UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      Tuple     =       1


	@register_to_config
	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 40	,    __SCREAMING_SNAKE_CASE = 0.0_0_2	,    __SCREAMING_SNAKE_CASE = 8_0.0	,    __SCREAMING_SNAKE_CASE = 0.5	,    __SCREAMING_SNAKE_CASE = 1.0	,    __SCREAMING_SNAKE_CASE = 7.0	,    __SCREAMING_SNAKE_CASE = True	,    )		->Tuple:
								# standard deviation of the initial noise distribution
								lowerCAmelCase				=	sigma_max

								lowerCAmelCase				=	np.linspace(0	,    1	,    __SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigma_to_t(__SCREAMING_SNAKE_CASE       )

								# setable values
								lowerCAmelCase				=	None
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	False
								lowerCAmelCase				=	False


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
								if schedule_timesteps is None:
															lowerCAmelCase				=	self.timesteps

								lowerCAmelCase				=	(schedule_timesteps == timestep).nonzero()
								return indices.item()


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->torch.FloatTensor:
								# Get sigma corresponding to timestep
								if isinstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
															lowerCAmelCase				=	timestep.to(self.timesteps.device       )
								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigmas[step_idx]

								lowerCAmelCase				=	sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

								lowerCAmelCase				=	True
								return sample


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								if not isinstance(__SCREAMING_SNAKE_CASE	,    np.ndarray       ):
															lowerCAmelCase				=	np.array(__SCREAMING_SNAKE_CASE	,    dtype=np.floataa       )

								lowerCAmelCase				=	1000 * 0.2_5 * np.log(sigmas + 1e-44       )

								return timesteps


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    )		->str:
								if num_inference_steps is None and timesteps is None:
															raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''       )

								if num_inference_steps is not None and timesteps is not None:
															raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''       )

								# Follow DDPMScheduler custom timesteps logic
								if timesteps is not None:
															for i in range(1	,    len(__SCREAMING_SNAKE_CASE       )       ):
																						if timesteps[i] >= timesteps[i - 1]:
																													raise ValueError('''`timesteps` must be in descending order.'''       )

															if timesteps[0] >= self.config.num_train_timesteps:
																						raise ValueError(
																						    F"`timesteps` must start before `self.config.train_timesteps`:"
																						    F" {self.config.num_train_timesteps}."       )

															lowerCAmelCase				=	np.array(__SCREAMING_SNAKE_CASE	,    dtype=np.intaa       )
															lowerCAmelCase				=	True
								else:
															if num_inference_steps > self.config.num_train_timesteps:
																						raise ValueError(
																						    F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
																						    F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
																						    F" maximal {self.config.num_train_timesteps} timesteps."       )

															lowerCAmelCase				=	num_inference_steps

															lowerCAmelCase				=	self.config.num_train_timesteps // self.num_inference_steps
															lowerCAmelCase				=	(np.arange(0	,    __SCREAMING_SNAKE_CASE       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
															lowerCAmelCase				=	False

								# Map timesteps to Karras sigmas directly for multistep sampling
								# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
								lowerCAmelCase				=	self.config.num_train_timesteps
								lowerCAmelCase				=	timesteps[::-1].copy()
								lowerCAmelCase				=	ramp / (num_train_timesteps - 1)
								lowerCAmelCase				=	self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigma_to_t(__SCREAMING_SNAKE_CASE       )

								lowerCAmelCase				=	np.concatenate([sigmas, [self.sigma_min]]       ).astype(np.floataa       )
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )

								if str(__SCREAMING_SNAKE_CASE       ).startswith('''mps'''       ):
															# mps does not support float64
															lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE	,    dtype=torch.floataa       )
								else:
															lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_max

								lowerCAmelCase				=	self.config.rho
								lowerCAmelCase				=	sigma_min ** (1 / rho)
								lowerCAmelCase				=	sigma_max ** (1 / rho)
								lowerCAmelCase				=	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	self.config.sigma_data

								lowerCAmelCase				=	sigma_data**2 / (sigma**2 + sigma_data**2)
								lowerCAmelCase				=	sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->List[str]:
								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_data

								lowerCAmelCase				=	sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
								lowerCAmelCase				=	(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = True	,    )		->Union[CMStochasticIterativeSchedulerOutput, Tuple]:

								if (
								    isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
								    or isinstance(__SCREAMING_SNAKE_CASE	,    torch.IntTensor       )
								    or isinstance(__SCREAMING_SNAKE_CASE	,    torch.LongTensor       )
								):
															raise ValueError(
															    (
															        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
															        F" `{self.__class__}.step()` is not supported. Make sure to pass"
															        ''' one of the `scheduler.timesteps` as a timestep.'''
															    )	,    )

								if not self.is_scale_input_called:
															logger.warning(
															    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
															    '''See `StableDiffusionPipeline` for a usage example.'''       )

								if isinstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
															lowerCAmelCase				=	timestep.to(self.timesteps.device       )

								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_max

								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )

								# sigma_next corresponds to next_t in original implementation
								lowerCAmelCase				=	self.sigmas[step_index]
								if step_index + 1 < self.config.num_train_timesteps:
															lowerCAmelCase				=	self.sigmas[step_index + 1]
								else:
															# Set sigma_next to sigma_min
															lowerCAmelCase				=	self.sigmas[-1]

								# Get scalings for boundary conditions
								lowerCAmelCase	,   lowerCAmelCase				=	self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE       )

								# 1. Denoise model output using boundary conditions
								lowerCAmelCase				=	c_out * model_output + c_skip * sample
								if self.config.clip_denoised:
															lowerCAmelCase				=	denoised.clamp(-1	,    1       )

								# 2. Sample z ~ N(0, s_noise^2 * I)
								# Noise is not used for onestep sampling.
								if len(self.timesteps       ) > 1:
															lowerCAmelCase				=	randn_tensor(
															    model_output.shape	,    dtype=model_output.dtype	,    device=model_output.device	,    generator=__SCREAMING_SNAKE_CASE       )
								else:
															lowerCAmelCase				=	torch.zeros_like(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	noise * self.config.s_noise

								lowerCAmelCase				=	sigma_next.clamp(min=__SCREAMING_SNAKE_CASE	,    max=__SCREAMING_SNAKE_CASE       )

								# 3. Return noisy sample
								# tau = sigma_hat, eps = sigma_min
								lowerCAmelCase				=	denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

								if not return_dict:
															return (prev_sample,)

								return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    )		->torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowerCAmelCase				=	self.sigmas.to(device=original_samples.device	,    dtype=original_samples.dtype       )
								if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE       ):
															# mps does not support float64
															lowerCAmelCase				=	self.timesteps.to(original_samples.device	,    dtype=torch.floataa       )
															lowerCAmelCase				=	timesteps.to(original_samples.device	,    dtype=torch.floataa       )
								else:
															lowerCAmelCase				=	self.timesteps.to(original_samples.device       )
															lowerCAmelCase				=	timesteps.to(original_samples.device       )

								lowerCAmelCase				=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

								lowerCAmelCase				=	sigmas[step_indices].flatten()
								while len(sigma.shape       ) < len(original_samples.shape       ):
															lowerCAmelCase				=	sigma.unsqueeze(-1       )

								lowerCAmelCase				=	original_samples + noise * sigma
								return noisy_samples




	def __len__(							self       )		->int:
								return self.config.num_train_timesteps



