


import math
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 KarrasDiffusionSchedulers, SchedulerMixin


_snake_case   = logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class    A_   ( a_     ):







	"""simple docstring"""





	_UpperCamelCase				: torch.FloatTensor
	_UpperCamelCase				: Optional[torch.FloatTensor]      =					None







def   A   (		A__		,		A__=0.999		,		A__="cosine"		,		):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(A__ ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(A__ ):
															return math.exp(t * -12.0 )

					else:
										raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}' )

					UpperCAmelCase						:				Any		  =			[]
					for i in range(A__ ):
										UpperCAmelCase						:				Optional[int]		  =			i / num_diffusion_timesteps
										UpperCAmelCase						:				Union[str, Any]		  =			(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(A__ ) / alpha_bar_fn(A__ )		,		A__ ) )
					return torch.tensor(A__		,		dtype=torch.floataa )




class    A_   ( a_      ,						a_     ):







	"""simple docstring"""





	_UpperCamelCase				: Dict      =					[e.name for e in KarrasDiffusionSchedulers]
	_UpperCamelCase				: List[str]      =					1
	@register_to_config
	def __init__(			self					: List[str]       ,    _a					: int = 1_0_0_0       ,    _a					: float = 0.0_0_0_1       ,    _a					: float = 0.0_2       ,    _a					: str = "linear"       ,    _a					: Optional[Union[np.ndarray, List[float]]] = None       ,    _a					: str = "epsilon"       ,    _a					: str = "linspace"       ,    _a					: int = 0       ,    )    ->			Optional[int]:
						if trained_betas is not None:
											UpperCAmelCase						:				List[str]		  =			torch.tensor(_a       ,    dtype=torch.floataa				)
						elif beta_schedule == "linear":
											UpperCAmelCase						:				Dict		  =			torch.linspace(_a       ,    _a       ,    _a       ,    dtype=torch.floataa				)
						elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											UpperCAmelCase						:				Any		  =			(
											    torch.linspace(beta_start**0.5       ,    beta_end**0.5       ,    _a       ,    dtype=torch.floataa				) ** 2
											)
						elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											UpperCAmelCase						:				str		  =			betas_for_alpha_bar(_a				)
						else:
											raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'				)

						UpperCAmelCase						:				Dict		  =			1.0 - self.betas
						UpperCAmelCase						:				Optional[Any]		  =			torch.cumprod(self.alphas       ,    dim=0				)

						UpperCAmelCase						:				Dict		  =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
						UpperCAmelCase						:				Union[str, Any]		  =			np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
						UpperCAmelCase						:				Optional[Any]		  =			torch.from_numpy(_a				)

						# setable values
						UpperCAmelCase						:				Optional[int]		  =			None
						UpperCAmelCase						:				Tuple		  =			np.linspace(0       ,    num_train_timesteps - 1       ,    _a       ,    dtype=_a				)[::-1].copy()
						UpperCAmelCase						:				str		  =			torch.from_numpy(_a				)
						UpperCAmelCase						:				Any		  =			False
	@property
	def   _UpperCamelCase    (			self					: Any				)    ->			Optional[Any]:
						# standard deviation of the initial noise distribution
						if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5
	def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: torch.FloatTensor       ,    _a					: Union[float, torch.FloatTensor]				)    ->			torch.FloatTensor:
						if isinstance(_a       ,    torch.Tensor				):
											UpperCAmelCase						:				Dict		  =			timestep.to(self.timesteps.device				)
						UpperCAmelCase						:				int		  =			(self.timesteps == timestep).nonzero().item()
						UpperCAmelCase						:				Dict		  =			self.sigmas[step_index]
						UpperCAmelCase						:				int		  =			sample / ((sigma**2 + 1) ** 0.5)
						UpperCAmelCase						:				List[str]		  =			True
						return sample
	def   _UpperCamelCase    (			self					: List[str]       ,    _a					: int       ,    _a					: Union[str, torch.device] = None				)    ->			Union[str, Any]:
						UpperCAmelCase						:				Optional[Any]		  =			num_inference_steps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
											UpperCAmelCase						:				List[Any]		  =			np.linspace(0       ,    self.config.num_train_timesteps - 1       ,    _a       ,    dtype=_a				)[
											    ::-1
											].copy()
						elif self.config.timestep_spacing == "leading":
											UpperCAmelCase						:				Dict		  =			self.config.num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											UpperCAmelCase						:				Tuple		  =			(np.arange(0       ,    _a				) * step_ratio).round()[::-1].copy().astype(_a				)
											timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
											UpperCAmelCase						:				Optional[Any]		  =			self.config.num_train_timesteps / self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											UpperCAmelCase						:				Any		  =			(np.arange(self.config.num_train_timesteps       ,    0       ,    -step_ratio				)).round().copy().astype(_a				)
											timesteps -= 1
						else:
											raise ValueError(
											    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'				)

						UpperCAmelCase						:				Dict		  =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
						UpperCAmelCase						:				List[Any]		  =			np.interp(_a       ,    np.arange(0       ,    len(_a				)				)       ,    _a				)
						UpperCAmelCase						:				Optional[Any]		  =			np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
						UpperCAmelCase						:				Optional[Any]		  =			torch.from_numpy(_a				).to(device=_a				)
						if str(_a				).startswith("mps"				):
											# mps does not support float64
											UpperCAmelCase						:				str		  =			torch.from_numpy(_a				).to(_a       ,    dtype=torch.floataa				)
						else:
											UpperCAmelCase						:				Union[str, Any]		  =			torch.from_numpy(_a				).to(device=_a				)
	def   _UpperCamelCase    (			self					: Any       ,    _a					: torch.FloatTensor       ,    _a					: Union[float, torch.FloatTensor]       ,    _a					: torch.FloatTensor       ,    _a					: Optional[torch.Generator] = None       ,    _a					: bool = True       ,    )    ->			Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

						if (
						    isinstance(_a       ,    _a				)
						    or isinstance(_a       ,    torch.IntTensor				)
						    or isinstance(_a       ,    torch.LongTensor				)
						):
											raise ValueError(
											    (
											        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
											        " `EulerDiscreteScheduler.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(_a       ,    torch.Tensor				):
											UpperCAmelCase						:				List[Any]		  =			timestep.to(self.timesteps.device				)

						UpperCAmelCase						:				Optional[Any]		  =			(self.timesteps == timestep).nonzero().item()
						UpperCAmelCase						:				Any		  =			self.sigmas[step_index]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
											UpperCAmelCase						:				Optional[int]		  =			sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											UpperCAmelCase						:				str		  =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						elif self.config.prediction_type == "sample":
											raise NotImplementedError("prediction_type not implemented yet: sample"				)
						else:
											raise ValueError(
											    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'				)

						UpperCAmelCase						:				Optional[Any]		  =			self.sigmas[step_index]
						UpperCAmelCase						:				Optional[int]		  =			self.sigmas[step_index + 1]
						UpperCAmelCase						:				List[str]		  =			(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
						UpperCAmelCase						:				Dict		  =			(sigma_to**2 - sigma_up**2) ** 0.5

						# 2. Convert to an ODE derivative
						UpperCAmelCase						:				Any		  =			(sample - pred_original_sample) / sigma

						UpperCAmelCase						:				str		  =			sigma_down - sigma

						UpperCAmelCase						:				List[Any]		  =			sample + derivative * dt

						UpperCAmelCase						:				Any		  =			model_output.device
						UpperCAmelCase						:				int		  =			randn_tensor(model_output.shape       ,    dtype=model_output.dtype       ,    device=_a       ,    generator=_a				)

						UpperCAmelCase						:				List[Any]		  =			prev_sample + noise * sigma_up

						if not return_dict:
											return (prev_sample,)

						return EulerAncestralDiscreteSchedulerOutput(
						    prev_sample=_a       ,    pred_original_sample=_a				)
	def   _UpperCamelCase    (			self					: Any       ,    _a					: torch.FloatTensor       ,    _a					: torch.FloatTensor       ,    _a					: torch.FloatTensor       ,    )    ->			torch.FloatTensor:
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						UpperCAmelCase						:				Dict		  =			self.sigmas.to(device=original_samples.device       ,    dtype=original_samples.dtype				)
						if original_samples.device.type == "mps" and torch.is_floating_point(_a				):
											# mps does not support float64
											UpperCAmelCase						:				List[str]		  =			self.timesteps.to(original_samples.device       ,    dtype=torch.floataa				)
											UpperCAmelCase						:				Optional[Any]		  =			timesteps.to(original_samples.device       ,    dtype=torch.floataa				)
						else:
											UpperCAmelCase						:				Optional[int]		  =			self.timesteps.to(original_samples.device				)
											UpperCAmelCase						:				int		  =			timesteps.to(original_samples.device				)

						UpperCAmelCase						:				Optional[Any]		  =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

						UpperCAmelCase						:				Dict		  =			sigmas[step_indices].flatten()
						while len(sigma.shape				) < len(original_samples.shape				):
											UpperCAmelCase						:				List[Any]		  =			sigma.unsqueeze(-1				)

						UpperCAmelCase						:				Union[str, Any]		  =			original_samples + noise * sigma
						return noisy_samples

	def __len__(			self					: Any				)    ->			Optional[int]:
						return self.config.num_train_timesteps







from cva import destroyAllWindows, imread, imshow, waitKey







def   A   (		A__		,		A__ ):
     UpperCAmelCase  , UpperCAmelCase						:				Optional[Any]		  =			img.shape[0], img.shape[1]

     def to_grayscale(A__		,		A__		,		A__ ):
          return 0.2126 * red + 0.587 * green + 0.114 * blue

     def normalize(A__ ):
          return min(A__		,		2_5_5 )

     for i in range(A__ ):
          for j in range(A__ ):
               UpperCAmelCase						:				Optional[Any]		  =			int(to_grayscale(*img[i][j] ) )
               UpperCAmelCase						:				List[str]		  =			[
                   normalize(A__ ),
                   normalize(greyscale + factor ),
                   normalize(greyscale + 2 * factor ),
               ]

     return img


if __name__ == "__main__":
      # read original image
      _snake_case   = {
          percentage: imread("image_data/lena.jpg", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
      }

      for percentage, img in images.items():
            make_sepia(img, percentage)

      for percentage, img in images.items():
            imshow(f'''Original image with sepia (factor: {percentage})''', img)

      waitKey(0)
      destroyAllWindows()



