





'''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 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				torch.FloatTensor
	a    :				torch.FloatTensor






class 							__lowercase       (      lowerCAmelCase__ ,      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Any											=       1


	@register_to_config
	def __init__(self     ,_lowerCamelCase = 2000     ,_lowerCamelCase = 0.1_5     ,_lowerCamelCase = 0.0_1     ,_lowerCamelCase = 1_3_4_8.0     ,_lowerCamelCase = 1E-5     ,_lowerCamelCase = 1     ,)							->							List[str]:







				'''simple docstring'''





				__lowercase										=							sigma_max

				# setable values
				__lowercase										=							None

				self.set_sigmas(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None )							->							torch.FloatTensor:







				'''simple docstring'''





				return sample


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = None )							->							List[str]:







				'''simple docstring'''





				__lowercase										=							sampling_eps if sampling_eps is not None else self.config.sampling_eps

				__lowercase										=							torch.linspace(1     ,_lowerCamelCase     ,_lowerCamelCase     ,device=_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							sigma_min if sigma_min is not None else self.config.sigma_min
				__lowercase										=							sigma_max if sigma_max is not None else self.config.sigma_max
				__lowercase										=							sampling_eps if sampling_eps is not None else self.config.sampling_eps
				if self.timesteps is None:
							self.set_timesteps(_lowerCamelCase     ,_lowerCamelCase )

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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[str]:







				'''simple docstring'''





				return torch.where(
				    timesteps == 0     ,torch.zeros_like(t.to(timesteps.device ) )     ,self.discrete_sigmas[timesteps - 1].to(timesteps.device )     ,)


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = True     ,)							->							Union[SdeVeOutput, Tuple]:







				'''simple docstring'''





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

				__lowercase										=							timestep * torch.ones(
				    sample.shape[0]     ,device=sample.device )  # torch.repeat_interleave(timestep, sample.shape[0])
				__lowercase										=							(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
				__lowercase										=							timesteps.to(self.discrete_sigmas.device )

				__lowercase										=							self.discrete_sigmas[timesteps].to(sample.device )
				__lowercase										=							self.get_adjacent_sigma(_lowerCamelCase     ,_lowerCamelCase ).to(sample.device )
				__lowercase										=							torch.zeros_like(_lowerCamelCase )
				__lowercase										=							(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
				__lowercase										=							diffusion.flatten()
				while len(diffusion.shape ) < len(sample.shape ):
							__lowercase										=							diffusion.unsqueeze(-1 )
				__lowercase										=							drift - diffusion**2 * model_output

				#  equation 6: sample noise for the diffusion term of
				__lowercase										=							randn_tensor(
				    sample.shape     ,layout=sample.layout     ,generator=_lowerCamelCase     ,device=sample.device     ,dtype=sample.dtype )
				__lowercase										=							sample - drift  # subtract because `dt` is a small negative timestep
				# TODO is the variable diffusion the correct scaling term for the noise?
				__lowercase										=							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=_lowerCamelCase     ,prev_sample_mean=_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = True     ,)							->							Union[SchedulerOutput, Tuple]:







				'''simple docstring'''





				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
				__lowercase										=							randn_tensor(sample.shape     ,layout=sample.layout     ,generator=_lowerCamelCase ).to(sample.device )

				# compute step size from the model_output, the noise, and the snr
				__lowercase										=							torch.norm(model_output.reshape(model_output.shape[0]     ,-1 )     ,dim=-1 ).mean()
				__lowercase										=							torch.norm(noise.reshape(noise.shape[0]     ,-1 )     ,dim=-1 ).mean()
				__lowercase										=							(self.config.snr * noise_norm / grad_norm) ** 2 * 2
				__lowercase										=							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
				__lowercase										=							step_size.flatten()
				while len(step_size.shape ) < len(sample.shape ):
							__lowercase										=							step_size.unsqueeze(-1 )
				__lowercase										=							sample + step_size * model_output
				__lowercase										=							prev_sample_mean + ((step_size * 2) ** 0.5) * noise

				if not return_dict:
							return (prev_sample,)

				return SchedulerOutput(prev_sample=_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							torch.FloatTensor:







				'''simple docstring'''





				__lowercase										=							timesteps.to(original_samples.device )
				__lowercase										=							self.discrete_sigmas.to(original_samples.device )[timesteps]
				__lowercase										=							(
				    noise * sigmas[:, None, None, None]
				    if noise is not None
				    else torch.randn_like(_lowerCamelCase ) * sigmas[:, None, None, None]
				)
				__lowercase										=							noise + original_samples
				return noisy_samples


	def __len__(self )							->							Union[str, Any]:







				'''simple docstring'''





				return self.config.num_train_timesteps












'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				int											=       ["pixel_values"]


	def __init__(self     ,_lowerCamelCase = True     ,_lowerCamelCase = 32     ,_lowerCamelCase=PILImageResampling.BILINEAR     ,_lowerCamelCase = True     ,**_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				__lowercase										=							do_resize
				__lowercase										=							do_rescale
				__lowercase										=							size_divisor
				__lowercase										=							resample
				super().__init__(**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							get_image_size(_lowerCamelCase )
				# Rounds the height and width down to the closest multiple of size_divisor
				__lowercase										=							height // size_divisor * size_divisor
				__lowercase										=							width // size_divisor * size_divisor
				__lowercase										=							resize(_lowerCamelCase     ,(new_h, new_w)     ,resample=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )
				return image


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





				return rescale(image=_lowerCamelCase     ,scale=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase=None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = ChannelDimension.FIRST     ,**_lowerCamelCase     ,)							->							BatchFeature:







				'''simple docstring'''





				__lowercase										=							do_resize if do_resize is not None else self.do_resize
				__lowercase										=							do_rescale if do_rescale is not None else self.do_rescale
				__lowercase										=							size_divisor if size_divisor is not None else self.size_divisor
				__lowercase										=							resample if resample is not None else self.resample

				if do_resize and size_divisor is None:
							raise ValueError('''size_divisor is required for resizing''' )

				__lowercase										=							make_list_of_images(_lowerCamelCase )

				if not valid_images(_lowerCamelCase ):
							raise ValueError('''Invalid image(s)''' )

				# All transformations expect numpy arrays.
				__lowercase										=							[to_numpy_array(_lowerCamelCase ) for img in images]

				if do_resize:
							__lowercase										=							[self.resize(_lowerCamelCase     ,size_divisor=_lowerCamelCase     ,resample=_lowerCamelCase ) for image in images]

				if do_rescale:
							__lowercase										=							[self.rescale(_lowerCamelCase     ,scale=1 / 255 ) for image in images]

				__lowercase										=							[to_channel_dimension_format(_lowerCamelCase     ,_lowerCamelCase ) for image in images]

				__lowercase										=							{'''pixel_values''': images}
				return BatchFeature(data=_lowerCamelCase     ,tensor_type=_lowerCamelCase )





