
'''simple docstring'''







from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_lowercase			:  List[Any]   							=	8
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			List[str]=BITS							):



						"""simple docstring"""





						lowercase_     :					Union[str, Any]													=	x.device

						lowercase_     :					Union[str, Any]													=	(x * 255).int().clamp(0				,				255							)

						lowercase_     :					str													=	2 ** torch.arange(bits - 1				,				-1				,				-1				,				device=__SCREAMING_SNAKE_CASE							)
						lowercase_     :					str													=	rearrange(__SCREAMING_SNAKE_CASE				,				'''d -> d 1 1'''							)
						lowercase_     :					Optional[int]													=	rearrange(__SCREAMING_SNAKE_CASE				,				'''b c h w -> b c 1 h w'''							)

						lowercase_     :					List[str]													=	((x & mask) != 0).float()
						lowercase_     :					str													=	rearrange(__SCREAMING_SNAKE_CASE				,				'''b c d h w -> b (c d) h w'''							)
						lowercase_     :					Any													=	bits * 2 - 1
						return bits
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			str=BITS							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	x.device

						lowercase_     :					Tuple													=	(x > 0).int()
						lowercase_     :					Optional[int]													=	2 ** torch.arange(bits - 1				,				-1				,				-1				,				device=__SCREAMING_SNAKE_CASE				,				dtype=torch.intaa							)

						lowercase_     :					Any													=	rearrange(__SCREAMING_SNAKE_CASE				,				'''d -> d 1 1'''							)
						lowercase_     :					Optional[Any]													=	rearrange(__SCREAMING_SNAKE_CASE				,				'''b (c d) h w -> b c d h w'''				,				d=8							)
						lowercase_     :					Any													=	reduce(x * mask				,				'''b c d h w -> b c h w'''				,				'''sum'''							)
						return (dec / 255).clamp(0.0				,				1.0							)
def  snake_case_		(    self :			List[str]				,				__SCREAMING_SNAKE_CASE :			torch.FloatTensor				,				__SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			torch.FloatTensor				,				__SCREAMING_SNAKE_CASE :			float = 0.0				,				__SCREAMING_SNAKE_CASE :			bool = True				,				__SCREAMING_SNAKE_CASE :			Any=None				,				__SCREAMING_SNAKE_CASE :			bool = True				,				):



						"""simple docstring"""





						if self.num_inference_steps is None:
												raise ValueError(
												    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''							)

						# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
						# Ideally, read DDIM paper in-detail understanding

						# Notation (<variable name> -> <name in paper>
						# - pred_noise_t -> e_theta(x_t, t)
						# - pred_original_sample -> f_theta(x_t, t) or x_0
						# - std_dev_t -> sigma_t
						# - eta -> η
						# - pred_sample_direction -> "direction pointing to x_t"
						# - pred_prev_sample -> "x_t-1"

						# 1. get previous step value (=t-1)
						lowercase_     :					int													=	timestep - self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						lowercase_     :					Optional[int]													=	self.alphas_cumprod[timestep]
						lowercase_     :					Optional[int]													=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

						lowercase_     :					Dict													=	1 - alpha_prod_t

						# 3. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						lowercase_     :					Optional[Any]													=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

						# 4. Clip "predicted x_0"
						lowercase_     :					List[Any]													=	self.bit_scale
						if self.config.clip_sample:
												lowercase_     :					Dict													=	torch.clamp(__SCREAMING_SNAKE_CASE				,				-scale				,				__SCREAMING_SNAKE_CASE							)

						# 5. compute variance: "sigma_t(η)" -> see formula (16)
						# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
						lowercase_     :					Optional[int]													=	self._get_variance(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Tuple													=	eta * variance ** 0.5

						if use_clipped_model_output:
												# the model_output is always re-derived from the clipped x_0 in Glide
												lowercase_     :					Tuple													=	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

						# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						lowercase_     :					List[Any]													=	(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

						# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						lowercase_     :					str													=	alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

						if eta > 0:
												# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
												lowercase_     :					Union[str, Any]													=	model_output.device if torch.is_tensor(__SCREAMING_SNAKE_CASE							) else '''cpu'''
												lowercase_     :					Dict													=	torch.randn(model_output.shape				,				dtype=model_output.dtype				,				generator=__SCREAMING_SNAKE_CASE							).to(__SCREAMING_SNAKE_CASE							)
												lowercase_     :					int													=	self._get_variance(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							) ** 0.5 * eta * noise

												lowercase_     :					Union[str, Any]													=	prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE				,				pred_original_sample=__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    self :			List[Any]				,				__SCREAMING_SNAKE_CASE :			torch.FloatTensor				,				__SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			torch.FloatTensor				,				__SCREAMING_SNAKE_CASE :			Optional[Any]="epsilon"				,				__SCREAMING_SNAKE_CASE :			Any=None				,				__SCREAMING_SNAKE_CASE :			bool = True				,				):



						"""simple docstring"""





						lowercase_     :					Tuple													=	timestep

						if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
												lowercase_     :					str													=	torch.split(__SCREAMING_SNAKE_CASE				,				sample.shape[1]				,				dim=1							)
						else:
												lowercase_     :					Union[str, Any]													=	None

						# 1. compute alphas, betas
						lowercase_     :					Optional[Any]													=	self.alphas_cumprod[t]
						lowercase_     :					str													=	self.alphas_cumprod[t - 1] if t > 0 else self.one
						lowercase_     :					Tuple													=	1 - alpha_prod_t
						lowercase_     :					List[str]													=	1 - alpha_prod_t_prev

						# 2. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
						if prediction_type == "epsilon":
												lowercase_     :					Optional[Any]													=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif prediction_type == "sample":
												lowercase_     :					List[str]													=	model_output
						else:
												raise ValueError(F'''Unsupported prediction_type {prediction_type}.'''							)

						# 3. Clip "predicted x_0"
						lowercase_     :					List[str]													=	self.bit_scale
						if self.config.clip_sample:
												lowercase_     :					List[str]													=	torch.clamp(__SCREAMING_SNAKE_CASE				,				-scale				,				__SCREAMING_SNAKE_CASE							)

						# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						lowercase_     :					int													=	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
						lowercase_     :					Any													=	self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

						# 5. Compute predicted previous sample µ_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						lowercase_     :					List[Any]													=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

						# 6. Add noise
						lowercase_     :					List[str]													=	0
						if t > 0:
												lowercase_     :					Union[str, Any]													=	torch.randn(
												    model_output.size()				,				dtype=model_output.dtype				,				layout=model_output.layout				,				generator=__SCREAMING_SNAKE_CASE							).to(model_output.device							)
												lowercase_     :					List[Any]													=	(self._get_variance(__SCREAMING_SNAKE_CASE				,				predicted_variance=__SCREAMING_SNAKE_CASE							) ** 0.5) * noise

						lowercase_     :					Dict													=	pred_prev_sample + variance

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE				,				pred_original_sample=__SCREAMING_SNAKE_CASE							)






class 			lowerCAmelCase__       (  lowerCamelCase_					):






					"""simple docstring"""




					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = 1.0    ,							):


											"""simple docstring"""





											super().__init__()
											lowercase_     :					Optional[int]													=	bit_scale
											lowercase_     :					Optional[int]													=	(
											    ddim_bit_scheduler_step if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else ddpm_bit_scheduler_step
											)

											self.register_modules(unet=__SCREAMING_SNAKE_CASE    ,							scheduler=__SCREAMING_SNAKE_CASE		)






					@torch.no_grad()
					def __call__(       self    ,							__SCREAMING_SNAKE_CASE = 2_56    ,							__SCREAMING_SNAKE_CASE = 2_56    ,							__SCREAMING_SNAKE_CASE = 50    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = 1    ,							__SCREAMING_SNAKE_CASE = "pil"    ,							__SCREAMING_SNAKE_CASE = True    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					List[str]													=	torch.randn(
											    (batch_size, self.unet.config.in_channels, height, width)    ,							generator=__SCREAMING_SNAKE_CASE    ,							)
											lowercase_     :					List[str]													=	decimal_to_bits(__SCREAMING_SNAKE_CASE		) * self.bit_scale
											lowercase_     :					Dict													=	latents.to(self.device		)

											self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)

											for t in self.progress_bar(self.scheduler.timesteps		):
																	# predict the noise residual
																	lowercase_     :					Union[str, Any]													=	self.unet(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		).sample

																	# compute the previous noisy sample x_t -> x_t-1
																	lowercase_     :					int													=	self.scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		).prev_sample

											lowercase_     :					Optional[int]													=	bits_to_decimal(__SCREAMING_SNAKE_CASE		)

											if output_type == "pil":
																	lowercase_     :					List[str]													=	self.numpy_to_pil(__SCREAMING_SNAKE_CASE		)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE		)





'''simple docstring'''







from __future__ import annotations






class 			lowerCAmelCase__       :






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE = 0		):


											"""simple docstring"""





											lowercase_     :					Any													=	key






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											assert isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) and isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	key or self.__key or 1

											# make sure key is an appropriate size
											key %= 2_55

											return [chr(ord(__SCREAMING_SNAKE_CASE		) ^ key		) for ch in content]






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											assert isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) and isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	key or self.__key or 1

											# make sure key is an appropriate size
											key %= 2_55

											return [chr(ord(__SCREAMING_SNAKE_CASE		) ^ key		) for ch in content]






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = 0		):


											"""simple docstring"""





											assert isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) and isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[Any]													=	key or self.__key or 1

											# make sure key can be any size
											while key > 2_55:
																	key -= 2_55

											# This will be returned
											lowercase_     :					str													=	''''''

											for ch in content:
																	ans += chr(ord(__SCREAMING_SNAKE_CASE		) ^ key		)

											return ans






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = 0		):


											"""simple docstring"""





											assert isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) and isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Any													=	key or self.__key or 1

											# make sure key can be any size
											while key > 2_55:
																	key -= 2_55

											# This will be returned
											lowercase_     :					Dict													=	''''''

											for ch in content:
																	ans += chr(ord(__SCREAMING_SNAKE_CASE		) ^ key		)

											return ans






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = 0		):


											"""simple docstring"""





											assert isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) and isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

											try:
																	with open(__SCREAMING_SNAKE_CASE		) as fin, open('''encrypt.out'''    ,							'''w+'''		) as fout:
																							# actual encrypt-process
																							for line in fin:
																													fout.write(self.encrypt_string(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)		)

											except OSError:
																	return False

											return True






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											assert isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) and isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

											try:
																	with open(__SCREAMING_SNAKE_CASE		) as fin, open('''decrypt.out'''    ,							'''w+'''		) as fout:
																							# actual encrypt-process
																							for line in fin:
																													fout.write(self.decrypt_string(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)		)

											except OSError:
																	return False

											return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")



