


# 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 A__     ( lowerCAmelCase__  ):



  """simple docstring"""



  _lowercase									=		4_2
  _lowercase									=		4_2




class A__     ( lowerCAmelCase__				,		lowerCAmelCase__  ):



  """simple docstring"""



  _lowercase									=		1

  @register_to_config
  def __init__( self			:  Union[str, Any]				,				lowerCamelCase__			:  int = 2_000				,				lowerCamelCase__			:  Optional[int] = 0.15				,				lowerCamelCase__			:  Optional[Any] = 0.01				,				lowerCamelCase__			:  int = 1_348.0				,				lowerCamelCase__			:  List[str] = 1E-5				,				lowerCamelCase__			:  Optional[int] = 1				,				):
        a__	:  Dict     =   sigma_max

        # setable values
        a__	:  Any     =   None

        self.set_sigmas(snake_case_				,				snake_case_				,				snake_case_				,				snake_case_       )

  def    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  int = None       ):
        return sample

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  Optional[Any] = None				,				lowerCamelCase__			:  Optional[int] = None       ):
        a__	:  Any     =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

        a__	:  str     =   torch.linspace(1				,				snake_case_				,				snake_case_				,				device=snake_case_       )

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  int = None				,				lowerCamelCase__			:  List[Any] = None				,				lowerCamelCase__			:  Tuple = None       ):
        a__	:  List[str]     =   sigma_min if sigma_min is not None else self.config.sigma_min
        a__	:  int     =   sigma_max if sigma_max is not None else self.config.sigma_max
        a__	:  int     =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
              self.set_timesteps(snake_case_				,				snake_case_       )

        a__	:  List[Any]     =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        a__	:  Tuple     =   torch.exp(torch.linspace(math.log(snake_case_       )				,				math.log(snake_case_       )				,				snake_case_       )       )
        a__	:  Optional[Any]     =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]       )

  def    _UpperCamelCase( self			:  List[str]				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  str       ):
        return torch.where(
            timesteps == 0				,				torch.zeros_like(t.to(timesteps.device       )       )				,				self.discrete_sigmas[timesteps - 1].to(timesteps.device       )				,				)

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  Dict				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  Tuple = None				,				lowerCamelCase__			:  Optional[Any] = True				,				):

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

        a__	:  Optional[Any]     =   timestep * torch.ones(
            sample.shape[0]				,				device=sample.device       )  # torch.repeat_interleave(timestep, sample.shape[0])
        a__	:  Union[str, Any]     =   (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
        a__	:  Union[str, Any]     =   timesteps.to(self.discrete_sigmas.device       )

        a__	:  int     =   self.discrete_sigmas[timesteps].to(sample.device       )
        a__	:  Optional[Any]     =   self.get_adjacent_sigma(snake_case_				,				snake_case_       ).to(sample.device       )
        a__	:  List[Any]     =   torch.zeros_like(snake_case_       )
        a__	:  Dict     =   (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
        a__	:  int     =   diffusion.flatten()
        while len(diffusion.shape       ) < len(sample.shape       ):
              a__	:  Optional[Any]     =   diffusion.unsqueeze(-1       )
        a__	:  Tuple     =   drift - diffusion**2 * model_output

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

  def    _UpperCamelCase( self			:  Any				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  Tuple = None				,				lowerCamelCase__			:  List[Any] = 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
        a__	:  int     =   randn_tensor(sample.shape				,				layout=sample.layout				,				generator=snake_case_       ).to(sample.device       )

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

        if not return_dict:
              return (prev_sample,)

        return SchedulerOutput(prev_sample=snake_case_       )

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  List[Any]				,				):
        a__	:  Union[str, Any]     =   timesteps.to(original_samples.device       )
        a__	:  str     =   self.discrete_sigmas.to(original_samples.device       )[timesteps]
        a__	:  List[str]     =   (
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(snake_case_       ) * sigmas[:, None, None, None]
        )
        a__	:  Tuple     =   noise + original_samples
        return noisy_samples

  def __len__( self			:  Dict       ):
        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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


a			:			List[str]												=					logging.get_logger(__name__)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["pixel_values"]






					def __init__(					self			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = PILImageResampling.BILINEAR			,							snake_case_ = True			,							snake_case_ = 1 / 2_5_5			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							**snake_case_			,							) ->      None:







								'''simple docstring'''

								super().__init__(**snake_case_    )
								__lowercase			       = size if size is not None else {'''shortest_edge''': 3_8_4}
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = do_resize
								__lowercase			       = size
								# Default value set here for backwards compatibility where the value in config is None
								__lowercase			       = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
								__lowercase			       = resample
								__lowercase			       = do_rescale
								__lowercase			       = rescale_factor
								__lowercase			       = do_normalize
								__lowercase			       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__lowercase			       = image_std if image_std is not None else IMAGENET_STANDARD_STD






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = PILImageResampling.BICUBIC			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )
								if "shortest_edge" not in size:
											raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'    )
								__lowercase			       = size['''shortest_edge''']

								if shortest_edge < 3_8_4:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											__lowercase			       = int(shortest_edge / crop_pct    )
											__lowercase			       = get_resize_output_image_size(snake_case_			,							size=snake_case_			,							default_to_square=snake_case_    )
											__lowercase			       = resize(image=snake_case_			,							size=snake_case_			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=snake_case_			,							size=(shortest_edge, shortest_edge)			,							data_format=snake_case_			,							**snake_case_    )
								else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    snake_case_			,							size=(shortest_edge, shortest_edge)			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

								return rescale(snake_case_			,							scale=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								return normalize(snake_case_			,							mean=snake_case_			,							std=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = ChannelDimension.FIRST			,							**snake_case_			,							) ->      PIL.Image.Image:







								'''simple docstring'''

								__lowercase			       = do_resize if do_resize is not None else self.do_resize
								__lowercase			       = crop_pct if crop_pct is not None else self.crop_pct
								__lowercase			       = resample if resample is not None else self.resample
								__lowercase			       = do_rescale if do_rescale is not None else self.do_rescale
								__lowercase			       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowercase			       = do_normalize if do_normalize is not None else self.do_normalize
								__lowercase			       = image_mean if image_mean is not None else self.image_mean
								__lowercase			       = image_std if image_std is not None else self.image_std

								__lowercase			       = size if size is not None else self.size
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = make_list_of_images(snake_case_    )

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

								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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
											raise ValueError('''crop_pct must be specified if size < 384.'''    )

								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.
								__lowercase			       = [to_numpy_array(snake_case_    ) for image in images]

								if do_resize:
											__lowercase			       = [self.resize(image=snake_case_			,							size=snake_case_			,							crop_pct=snake_case_			,							resample=snake_case_    ) for image in images]

								if do_rescale:
											__lowercase			       = [self.rescale(image=snake_case_			,							scale=snake_case_    ) for image in images]

								if do_normalize:
											__lowercase			       = [self.normalize(image=snake_case_			,							mean=snake_case_			,							std=snake_case_    ) for image in images]

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

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


