

"""simple docstring"""




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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





@dataclass
class    lowerCamelCase						(__snake_case			):
			lowerCamelCase__   :		Optional[int]							=							4_2
			lowerCamelCase__   :		Dict							=							4_2





class    lowerCamelCase						(__snake_case			,__snake_case			):
			lowerCamelCase__   :		Any							=							1

			@register_to_config
			def __init__(					self				:						str    ,				__UpperCAmelCase				:						Tuple = 2_0_0_0    ,				__UpperCAmelCase				:						str = 0.15    ,				__UpperCAmelCase				:						Any = 0.01    ,				__UpperCAmelCase				:						int = 1_348.0    ,				__UpperCAmelCase				:						str = 1e-5    ,				__UpperCAmelCase				:						Dict = 1    ,				)   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      sigma_max

						# setable values
						SCREAMING_SNAKE_CASE__								=      None

						self.set_sigmas(__UpperCamelCase    ,				__UpperCamelCase    ,				__UpperCamelCase    ,				__UpperCamelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						int = None       )   ->    Optional[Any]:
						return sample

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Union[str, Any] = None    ,				__UpperCAmelCase				:						Tuple = None       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      sampling_eps if sampling_eps is not None else self.config.sampling_eps

						SCREAMING_SNAKE_CASE__								=      torch.linspace(1    ,				__UpperCamelCase    ,				__UpperCamelCase    ,				device=__UpperCamelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Optional[int] = None    ,				__UpperCAmelCase				:						Optional[int] = None    ,				__UpperCAmelCase				:						Optional[int] = None       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      sigma_min if sigma_min is not None else self.config.sigma_min
						SCREAMING_SNAKE_CASE__								=      sigma_max if sigma_max is not None else self.config.sigma_max
						SCREAMING_SNAKE_CASE__								=      sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if self.timesteps is None:
									self.set_timesteps(__UpperCamelCase    ,				__UpperCamelCase       )

						SCREAMING_SNAKE_CASE__								=      sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
						SCREAMING_SNAKE_CASE__								=      torch.exp(torch.linspace(math.log(__UpperCamelCase       )    ,				math.log(__UpperCamelCase       )    ,				__UpperCamelCase       )       )
						SCREAMING_SNAKE_CASE__								=      torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						int       )   ->    Optional[int]:
						return torch.where(
						    timesteps == 0    ,				torch.zeros_like(t.to(timesteps.device       )       )    ,				self.discrete_sigmas[timesteps - 1].to(timesteps.device       )    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						List[str] = None    ,				__UpperCAmelCase				:						List[str] = True    ,				)   ->    List[Any]:


						if self.timesteps is None:
									raise ValueError(
									    """`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""       )

						SCREAMING_SNAKE_CASE__								=      timestep * torch.ones(
						    sample.shape[0]    ,				device=sample.device       )  # torch.repeat_interleave(timestep, sample.shape[0])
						SCREAMING_SNAKE_CASE__								=      (timestep * (len(self.timesteps       ) - 1)).long()

						# mps requires indices to be in the same device, so we use cpu as is the default with cuda
						SCREAMING_SNAKE_CASE__								=      timesteps.to(self.discrete_sigmas.device       )

						SCREAMING_SNAKE_CASE__								=      self.discrete_sigmas[timesteps].to(sample.device       )
						SCREAMING_SNAKE_CASE__								=      self.get_adjacent_sigma(__UpperCamelCase    ,				__UpperCamelCase       ).to(sample.device       )
						SCREAMING_SNAKE_CASE__								=      torch.zeros_like(__UpperCamelCase       )
						SCREAMING_SNAKE_CASE__								=      (sigma**2 - adjacent_sigma**2) ** 0.5

						# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
						# also equation 47 shows the analog from SDE models to ancestral sampling methods
						SCREAMING_SNAKE_CASE__								=      diffusion.flatten()
						while len(diffusion.shape       ) < len(sample.shape       ):
									SCREAMING_SNAKE_CASE__								=      diffusion.unsqueeze(-1       )
						SCREAMING_SNAKE_CASE__								=      drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						SCREAMING_SNAKE_CASE__								=      randn_tensor(
						    sample.shape    ,				layout=sample.layout    ,				generator=__UpperCamelCase    ,				device=sample.device    ,				dtype=sample.dtype       )
						SCREAMING_SNAKE_CASE__								=      sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						SCREAMING_SNAKE_CASE__								=      prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
									return (prev_sample, prev_sample_mean)

						return SdeVeOutput(prev_sample=__UpperCamelCase    ,				prev_sample_mean=__UpperCamelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						str = None    ,				__UpperCAmelCase				:						Optional[Any] = True    ,				)   ->    Dict:


						if self.timesteps is None:
									raise ValueError(
									    """`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""       )

						# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
						# sample noise for correction
						SCREAMING_SNAKE_CASE__								=      randn_tensor(sample.shape    ,				layout=sample.layout    ,				generator=__UpperCamelCase       ).to(sample.device       )

						# compute step size from the model_output, the noise, and the snr
						SCREAMING_SNAKE_CASE__								=      torch.norm(model_output.reshape(model_output.shape[0]    ,				-1       )    ,				dim=-1       ).mean()
						SCREAMING_SNAKE_CASE__								=      torch.norm(noise.reshape(noise.shape[0]    ,				-1       )    ,				dim=-1       ).mean()
						SCREAMING_SNAKE_CASE__								=      (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						SCREAMING_SNAKE_CASE__								=      step_size * torch.ones(sample.shape[0]       ).to(sample.device       )
						# self.repeat_scalar(step_size, sample.shape[0])

						# compute corrected sample: model_output term and noise term
						SCREAMING_SNAKE_CASE__								=      step_size.flatten()
						while len(step_size.shape       ) < len(sample.shape       ):
									SCREAMING_SNAKE_CASE__								=      step_size.unsqueeze(-1       )
						SCREAMING_SNAKE_CASE__								=      sample + step_size * model_output
						SCREAMING_SNAKE_CASE__								=      prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
									return (prev_sample,)

						return SchedulerOutput(prev_sample=__UpperCamelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Tuple    ,				)   ->    str:
						SCREAMING_SNAKE_CASE__								=      timesteps.to(original_samples.device       )
						SCREAMING_SNAKE_CASE__								=      self.discrete_sigmas.to(original_samples.device       )[timesteps]
						SCREAMING_SNAKE_CASE__								=      (
						    noise * sigmas[:, None, None, None]
						    if noise is not None
						    else torch.randn_like(__UpperCamelCase       ) * sigmas[:, None, None, None]
						)
						SCREAMING_SNAKE_CASE__								=      noise + original_samples
						return noisy_samples





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





"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      UNetaDModel(
						    block_out_channels=(3_2, 6_4)    ,				layers_per_block=2    ,				sample_size=3_2    ,				in_channels=3    ,				out_channels=3    ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,				)
						return model





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.dummy_uncond_unet
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""    ,				return_dict=__UpperCAmelCase       )[0]

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						SCREAMING_SNAKE_CASE__								=      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2





@slow
@require_torch
class    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      """google/ncsnpp-celebahq-256"""
						SCREAMING_SNAKE_CASE__								=      UNetaDModel.from_pretrained(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2_0    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


