






from cva import destroyAllWindows, imread, imshow, waitKey

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__     ,				__magic_name__		:List[str]        =      img.shape[0], img.shape[1]

				# converting each pixel's color to its negative
				for i in range(snake_case ):
								for j in range(snake_case ):
												__magic_name__		:Optional[Any]        =      [2_5_5, 2_5_5, 2_5_5] - img[i][j]

				return img


if __name__ == "__main__":
					# read original image
					SCREAMING_SNAKE_CASE__      :			str							=		imread("""image_data/lena.jpg""", 1)

					# convert to its negative
					SCREAMING_SNAKE_CASE__      :			List[str]							=		convert_to_negative(img)

					# show result image
					imshow("""negative of original image""", img)
					waitKey(0)
					destroyAllWindows()














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


SCREAMING_SNAKE_CASE__      :			Any							=		8

def      __lowercase					( snake_case,  snake_case=BITS ):







				"""simple docstring"""






				__magic_name__		:int        =      x.device

				__magic_name__		:Optional[Any]        =      (x * 2_5_5).int().clamp(0,  2_5_5 )

				__magic_name__		:Dict        =      2 ** torch.arange(bits - 1,  -1,  -1,  device=snake_case )
				__magic_name__		:Any        =      rearrange(snake_case,  '''d -> d 1 1''' )
				__magic_name__		:Dict        =      rearrange(snake_case,  '''b c h w -> b c 1 h w''' )

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

def      __lowercase					( snake_case,  snake_case=BITS ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      x.device

				__magic_name__		:Dict        =      (x > 0).int()
				__magic_name__		:int        =      2 ** torch.arange(bits - 1,  -1,  -1,  device=snake_case,  dtype=torch.intaa )

				__magic_name__		:Optional[Any]        =      rearrange(snake_case,  '''d -> d 1 1''' )
				__magic_name__		:Union[str, Any]        =      rearrange(snake_case,  '''b (c d) h w -> b c d h w''',  d=8 )
				__magic_name__		:Optional[Any]        =      reduce(x * mask,  '''b c d h w -> b c h w''',  '''sum''' )
				return (dec / 2_5_5).clamp(0.0,  1.0 )

def      __lowercase					( self,  snake_case,  snake_case,  snake_case,  snake_case = 0.0,  snake_case = True,  snake_case=None,  snake_case = 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)
				__magic_name__		:Union[str, Any]        =      timestep - self.config.num_train_timesteps // self.num_inference_steps

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

				__magic_name__		: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
				__magic_name__		:List[Any]        =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				__magic_name__		:Optional[int]        =      self.bit_scale
				if self.config.clip_sample:
								__magic_name__		:Dict        =      torch.clamp(snake_case,  -scale,  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)
				__magic_name__		:Optional[Any]        =      self._get_variance(snake_case,  snake_case )
				__magic_name__		:Tuple        =      eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								__magic_name__		:int        =      (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
				__magic_name__		:Tuple        =      (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
				__magic_name__		: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
								__magic_name__		:List[str]        =      model_output.device if torch.is_tensor(snake_case ) else '''cpu'''
								__magic_name__		:Tuple        =      torch.randn(model_output.shape,  dtype=model_output.dtype,  generator=snake_case ).to(snake_case )
								__magic_name__		:Optional[Any]        =      self._get_variance(snake_case,  snake_case ) ** 0.5 * eta * noise

								__magic_name__		:List[Any]        =      prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=snake_case,  pred_original_sample=snake_case )

def      __lowercase					( self,  snake_case,  snake_case,  snake_case,  snake_case="epsilon",  snake_case=None,  snake_case = True,  ):







				"""simple docstring"""






				__magic_name__		:int        =      timestep

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

				# 1. compute alphas, betas
				__magic_name__		:List[str]        =      self.alphas_cumprod[t]
				__magic_name__		:List[str]        =      self.alphas_cumprod[t - 1] if t > 0 else self.one
				__magic_name__		:Any        =      1 - alpha_prod_t
				__magic_name__		:Tuple        =      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":
								__magic_name__		:str        =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								__magic_name__		:List[Any]        =      model_output
				else:
								raise ValueError(f'''Unsupported prediction_type {prediction_type}.''' )

				# 3. Clip "predicted x_0"
				__magic_name__		:List[str]        =      self.bit_scale
				if self.config.clip_sample:
								__magic_name__		:int        =      torch.clamp(snake_case,  -scale,  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
				__magic_name__		:Any        =      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				__magic_name__		:str        =      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
				__magic_name__		:List[Any]        =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				__magic_name__		:int        =      0
				if t > 0:
								__magic_name__		:Any        =      torch.randn(
								    model_output.size(),  dtype=model_output.dtype,  layout=model_output.layout,  generator=snake_case ).to(model_output.device )
								__magic_name__		:int        =      (self._get_variance(snake_case,  predicted_variance=snake_case ) ** 0.5) * noise

				__magic_name__		:int        =      pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=snake_case,  pred_original_sample=snake_case )

class 						lowerCamelCase_				(      lowerCamelCase ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = 1.0       ,					):
								"""simple docstring"""
								super().__init__()
								__magic_name__		:Any        =      bit_scale
								__magic_name__		:List[Any]        =      (
								    ddim_bit_scheduler_step if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) else ddpm_bit_scheduler_step
								)

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







				@torch.no_grad()
				def __call__(						self       ,					__lowerCAmelCase = 2_5_6       ,					__lowerCAmelCase = 2_5_6       ,					__lowerCAmelCase = 5_0       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = 1       ,					__lowerCAmelCase = "pil"       ,					__lowerCAmelCase = True       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:List[str]        =      torch.randn(
								    (batch_size, self.unet.config.in_channels, height, width)       ,					generator=__lowerCAmelCase       ,					)
								__magic_name__		:List[str]        =      decimal_to_bits(__lowerCAmelCase     ) * self.bit_scale
								__magic_name__		:int        =      latents.to(self.device     )

								self.scheduler.set_timesteps(__lowerCAmelCase     )

								for t in self.progress_bar(self.scheduler.timesteps     ):
												# predict the noise residual
												__magic_name__		:List[Any]        =      self.unet(__lowerCAmelCase       ,					__lowerCAmelCase     ).sample

												# compute the previous noisy sample x_t -> x_t-1
												__magic_name__		:Any        =      self.scheduler.step(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ).prev_sample

								__magic_name__		:str        =      bits_to_decimal(__lowerCAmelCase     )

								if output_type == "pil":
												__magic_name__		:Any        =      self.numpy_to_pil(__lowerCAmelCase     )

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=__lowerCAmelCase     )






