



# 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      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    torch.FloatTensor
					lowerCAmelCase_				:    torch.FloatTensor





class 	lowerCamelCase      (						lowercase__,     lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Optional[int]									=  1



					@register_to_config
					def __init__(			self    ,	lowerCAmelCase = 2000    ,	lowerCAmelCase = 0.15    ,	lowerCAmelCase = 0.01    ,	lowerCAmelCase = 1348.0    ,	lowerCAmelCase = 1e-5    ,	lowerCAmelCase = 1    ,	):
							# standard deviation of the initial noise distribution
							UpperCAmelCase_						=				sigma_max

							# setable values
							UpperCAmelCase_						=				None

							self.set_sigmas(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							return sample



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

							UpperCAmelCase_						=				torch.linspace(1    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	device=lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				sigma_min if sigma_min is not None else self.config.sigma_min
							UpperCAmelCase_						=				sigma_max if sigma_max is not None else self.config.sigma_max
							UpperCAmelCase_						=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if self.timesteps is None:
									self.set_timesteps(lowerCAmelCase    ,	lowerCAmelCase     )

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



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



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

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

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

							#  equation 6: sample noise for the diffusion term of
							UpperCAmelCase_						=				randn_tensor(
							    sample.shape    ,	layout=sample.layout    ,	generator=lowerCAmelCase    ,	device=sample.device    ,	dtype=sample.dtype     )
							UpperCAmelCase_						=				sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							UpperCAmelCase_						=				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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = True    ,	):
							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
							UpperCAmelCase_						=				randn_tensor(sample.shape    ,	layout=sample.layout    ,	generator=lowerCAmelCase     ).to(sample.device     )

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

							if not return_dict:
									return (prev_sample,)

							return SchedulerOutput(prev_sample=lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	):
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							UpperCAmelCase_						=				timesteps.to(original_samples.device     )
							UpperCAmelCase_						=				self.discrete_sigmas.to(original_samples.device     )[timesteps]
							UpperCAmelCase_						=				(
							    noise * sigmas[:, None, None, None]
							    if noise is not None
							    else torch.randn_like(lowerCAmelCase     ) * sigmas[:, None, None, None]
							)
							UpperCAmelCase_						=				noise + original_samples
							return noisy_samples



					def __len__(			self     ):
							return self.config.num_train_timesteps






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							List[List[ImageInput]]:
		if isinstance(__SCREAMING_SNAKE_CASE			,     (list, tuple) ) and isinstance(videos[0]			,     (list, tuple) ) and is_valid_image(videos[0][0] ):
				return videos

		elif isinstance(__SCREAMING_SNAKE_CASE			,     (list, tuple) ) and is_valid_image(videos[0] ):
				return [videos]

		elif is_valid_image(__SCREAMING_SNAKE_CASE ):
				return [[videos]]

		raise ValueError(f'''Could not make batched video from {videos}''' )



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[Any]									=  ['pixel_values']



					def __init__(			self    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = PILImageResampling.BILINEAR    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = True    ,	lowerCAmelCase = 1 / 255    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							super().__init__(**lowerCAmelCase     )
							UpperCAmelCase_						=				size if size is not None else {"shortest_edge": 224}
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	default_to_square=lowerCAmelCase     )
							UpperCAmelCase_						=				crop_size if crop_size is not None else {"height": 224, "width": 224}
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	param_name="crop_size"     )

							UpperCAmelCase_						=				do_resize
							UpperCAmelCase_						=				size
							UpperCAmelCase_						=				do_center_crop
							UpperCAmelCase_						=				crop_size
							UpperCAmelCase_						=				resample
							UpperCAmelCase_						=				do_rescale
							UpperCAmelCase_						=				rescale_factor
							UpperCAmelCase_						=				do_normalize
							UpperCAmelCase_						=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCAmelCase_						=				image_std if image_std is not None else IMAGENET_STANDARD_STD



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = PILImageResampling.BILINEAR    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	default_to_square=lowerCAmelCase     )
							if "shortest_edge" in size:
									UpperCAmelCase_						=				get_resize_output_image_size(lowerCAmelCase    ,	size["shortest_edge"]    ,	default_to_square=lowerCAmelCase     )
							elif "height" in size and "width" in size:
									UpperCAmelCase_						=				(size["height"], size["width"])
							else:
									raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''     )
							return resize(lowerCAmelCase    ,	size=lowerCAmelCase    ,	resample=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase     )
							if "height" not in size or "width" not in size:
									raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''     )
							return center_crop(lowerCAmelCase    ,	size=(size["height"], size["width"])    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							return rescale(lowerCAmelCase    ,	scale=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							return normalize(lowerCAmelCase    ,	mean=lowerCAmelCase    ,	std=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = ChannelDimension.FIRST    ,	):
							if do_resize and size is None or resample is None:
									raise ValueError("Size and resample must be specified if do_resize is True."     )

							if do_center_crop and crop_size is None:
									raise ValueError("Crop size must be specified if do_center_crop is True."     )

							if do_rescale and rescale_factor is None:
									raise ValueError("Rescale factor must be specified if do_rescale is True."     )

							if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("Image mean and std must be specified if do_normalize is True."     )

							# All transformations expect numpy arrays.
							UpperCAmelCase_						=				to_numpy_array(lowerCAmelCase     )

							if do_resize:
									UpperCAmelCase_						=				self.resize(image=lowerCAmelCase    ,	size=lowerCAmelCase    ,	resample=lowerCAmelCase     )

							if do_center_crop:
									UpperCAmelCase_						=				self.center_crop(lowerCAmelCase    ,	size=lowerCAmelCase     )

							if do_rescale:
									UpperCAmelCase_						=				self.rescale(image=lowerCAmelCase    ,	scale=lowerCAmelCase     )

							if do_normalize:
									UpperCAmelCase_						=				self.normalize(image=lowerCAmelCase    ,	mean=lowerCAmelCase    ,	std=lowerCAmelCase     )

							UpperCAmelCase_						=				to_channel_dimension_format(lowerCAmelCase    ,	lowerCAmelCase     )
							return image



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = ChannelDimension.FIRST    ,	**lowerCAmelCase    ,	):
							UpperCAmelCase_						=				do_resize if do_resize is not None else self.do_resize
							UpperCAmelCase_						=				resample if resample is not None else self.resample
							UpperCAmelCase_						=				do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCAmelCase_						=				do_rescale if do_rescale is not None else self.do_rescale
							UpperCAmelCase_						=				rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCAmelCase_						=				do_normalize if do_normalize is not None else self.do_normalize
							UpperCAmelCase_						=				image_mean if image_mean is not None else self.image_mean
							UpperCAmelCase_						=				image_std if image_std is not None else self.image_std

							UpperCAmelCase_						=				size if size is not None else self.size
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	default_to_square=lowerCAmelCase     )
							UpperCAmelCase_						=				crop_size if crop_size is not None else self.crop_size
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	param_name="crop_size"     )

							if not valid_images(lowerCAmelCase     ):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray."     )

							UpperCAmelCase_						=				make_batched(lowerCAmelCase     )

							UpperCAmelCase_						=				[
							    [
							        self._preprocess_image(
							            image=lowerCAmelCase    ,	do_resize=lowerCAmelCase    ,	size=lowerCAmelCase    ,	resample=lowerCAmelCase    ,	do_center_crop=lowerCAmelCase    ,	crop_size=lowerCAmelCase    ,	do_rescale=lowerCAmelCase    ,	rescale_factor=lowerCAmelCase    ,	do_normalize=lowerCAmelCase    ,	image_mean=lowerCAmelCase    ,	image_std=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	)
							        for img in video
							    ]
							    for video in videos
							]

							UpperCAmelCase_						=				{"pixel_values": videos}
							return BatchFeature(data=lowerCAmelCase    ,	tensor_type=lowerCAmelCase     )

