
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey







def 		A_(     A						:		Tuple):
							# getting number of pixels in the image
							UpperCamelCase			,	UpperCamelCase   			=     img.shape[0], img.shape[1]

							# converting each pixel's color to its negative
							for i in range(A):
														for j in range(A):
																					UpperCamelCase   			=     [255, 255, 255] - img[i][j]

							return img


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

						# convert to its negative
						lowerCAmelCase					:    List[Any]           = convert_to_negative(img)

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





"""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


__lowerCAmelCase       :			Tuple   	=8


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:int=BITS						)					->       Optional[Any]:
      '''simple docstring'''


      lowercase								=      x.device

      lowercase								=      (x * 2_5_5).int().clamp(0   ,						2_5_5						)

      lowercase								=      2 ** torch.arange(bits - 1   ,						-1   ,						-1   ,						device=lowerCAmelCase__						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""d -> d 1 1"""						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b c h w -> b c 1 h w"""						)

      lowercase								=      ((x & mask) != 0).float()
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b c d h w -> b (c d) h w"""						)
      lowercase								=      bits * 2 - 1
      return bits


def      UpperCAmelCase__			(							lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:int=BITS						)					->       List[Any]:
      '''simple docstring'''


      lowercase								=      x.device

      lowercase								=      (x > 0).int()
      lowercase								=      2 ** torch.arange(bits - 1   ,						-1   ,						-1   ,						device=lowerCAmelCase__   ,						dtype=torch.intaa						)

      lowercase								=      rearrange(lowerCAmelCase__   ,						"""d -> d 1 1"""						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b (c d) h w -> b c d h w"""   ,						d=8						)
      lowercase								=      reduce(x * mask   ,						"""b c d h w -> b c h w"""   ,						"""sum"""						)
      return (dec / 2_5_5).clamp(0.0   ,						1.0						)


def      UpperCAmelCase__			(							self	:Union[str, Any]   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:float = 0.0   ,						lowerCAmelCase__	:bool = True   ,						lowerCAmelCase__	:Optional[Any]=None   ,						lowerCAmelCase__	:bool = True   ,						)					->       Union[DDIMSchedulerOutput, Tuple]:
      '''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								=      timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      lowercase								=      self.alphas_cumprod[timestep]
      lowercase								=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

      lowercase								=      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								=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

      # 4. Clip "predicted x_0"
      lowercase								=      self.bit_scale
      if self.config.clip_sample:
            lowercase								=      torch.clamp(lowerCAmelCase__   ,						-scale   ,						lowerCAmelCase__						)

      # 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								=      self._get_variance(lowerCAmelCase__   ,						lowerCAmelCase__						)
      lowercase								=      eta * variance ** 0.5

      if use_clipped_model_output:
            # the model_output is always re-derived from the clipped x_0 in Glide
            lowercase								=      (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								=      (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								=      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								=      model_output.device if torch.is_tensor(lowerCAmelCase__						) else """cpu"""
            lowercase								=      torch.randn(model_output.shape   ,						dtype=model_output.dtype   ,						generator=lowerCAmelCase__						).to(lowerCAmelCase__						)
            lowercase								=      self._get_variance(lowerCAmelCase__   ,						lowerCAmelCase__						) ** 0.5 * eta * noise

            lowercase								=      prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=lowerCAmelCase__   ,						pred_original_sample=lowerCAmelCase__						)


def      UpperCAmelCase__			(							self	:Any   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:List[str]="epsilon"   ,						lowerCAmelCase__	:Optional[int]=None   ,						lowerCAmelCase__	:bool = True   ,						)					->       Union[DDPMSchedulerOutput, Tuple]:
      '''simple docstring'''


      lowercase								=      timestep

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

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

      # 3. Clip "predicted x_0"
      lowercase								=      self.bit_scale
      if self.config.clip_sample:
            lowercase								=      torch.clamp(lowerCAmelCase__   ,						-scale   ,						lowerCAmelCase__						)

      # 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								=      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      lowercase								=      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								=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

      lowercase								=      pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=lowerCAmelCase__   ,						pred_original_sample=lowerCAmelCase__						)






class 					_A		(			lowerCAmelCase      ):

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







            """simple docstring"""



            super().__init__()
            lowercase								=      bit_scale
            lowercase								=      (
                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 = 256						,			__lowerCAmelCase = 256						,			__lowerCAmelCase = 50						,			__lowerCAmelCase = None						,			__lowerCAmelCase = 1						,			__lowerCAmelCase = "pil"						,			__lowerCAmelCase = True						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      torch.randn(
                (batch_size, self.unet.config.in_channels, height, width)						,			generator=__lowerCAmelCase						,			)
            lowercase								=      decimal_to_bits(__lowerCAmelCase		) * self.bit_scale
            lowercase								=      latents.to(self.device		)

            self.scheduler.set_timesteps(__lowerCAmelCase		)

            for t in self.progress_bar(self.scheduler.timesteps		):
                  # predict the noise residual
                  lowercase								=      self.unet(__lowerCAmelCase						,			__lowerCAmelCase		).sample

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

            lowercase								=      bits_to_decimal(__lowerCAmelCase		)

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

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__lowerCAmelCase		)



