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

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class    _snake_case  :
							# setable values
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None






							@classmethod
							def 							__lowercase  (					cls						:			Optional[int]    ):






												"""simple docstring"""



												return cls()






@dataclass
class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						42
							SCREAMING_SNAKE_CASE		     =						42
							SCREAMING_SNAKE_CASE		     =						None






class    _snake_case  (						_lowercase ,				_lowercase       ):






							@property
							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												return True






							@register_to_config
							def __init__(					self						:			Tuple						, snake_case__						:			int = 2_0_0_0						, snake_case__						:			float = 0.15						, snake_case__						:			float = 0.01						, snake_case__						:			float = 1_348.0						, snake_case__						:			float = 1e-5						, snake_case__						:			int = 1						, ):






												"""simple docstring"""



												pass






							def 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												__a:						List[Any]		    =					ScoreSdeVeSchedulerState.create()
												return self.set_sigmas(
												    snake_case__						, self.config.num_train_timesteps						, self.config.sigma_min						, self.config.sigma_max						, self.config.sampling_eps						, )






							def 							__lowercase  (					self						:			List[Any]						, snake_case__						:			ScoreSdeVeSchedulerState						, snake_case__						:			int						, snake_case__						:			Tuple = ()						, snake_case__						:			float = None    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

												__a:						List[Any]		    =					jnp.linspace(1						, snake_case__						, snake_case__    )
												return state.replace(timesteps=snake_case__    )






							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			ScoreSdeVeSchedulerState						, snake_case__						:			int						, snake_case__						:			float = None						, snake_case__						:			float = None						, snake_case__						:			float = None						, ):






												"""simple docstring"""



												__a:						Tuple		    =					sigma_min if sigma_min is not None else self.config.sigma_min
												__a:						List[Any]		    =					sigma_max if sigma_max is not None else self.config.sigma_max
												__a:						List[str]		    =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if state.timesteps is None:
																	__a:						Optional[Any]		    =					self.set_timesteps(snake_case__						, snake_case__						, snake_case__    )

												__a:						Union[str, Any]		    =					jnp.exp(jnp.linspace(jnp.log(snake_case__    )						, jnp.log(snake_case__    )						, snake_case__    )    )
												__a:						str		    =					jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]    )

												return state.replace(discrete_sigmas=snake_case__						, sigmas=snake_case__    )






							def 							__lowercase  (					self						:			str						, snake_case__						:			Optional[Any]						, snake_case__						:			List[Any]						, snake_case__						:			List[str]    ):






												"""simple docstring"""



												return jnp.where(timesteps == 0						, jnp.zeros_like(snake_case__    )						, state.discrete_sigmas[timesteps - 1]    )






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			ScoreSdeVeSchedulerState						, snake_case__						:			jnp.ndarray						, snake_case__						:			int						, snake_case__						:			jnp.ndarray						, snake_case__						:			random.KeyArray						, snake_case__						:			bool = True						, ):






												"""simple docstring"""



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

												__a:						Tuple		    =					timestep * jnp.ones(
												    sample.shape[0]						, )
												__a:						Any		    =					(timestep * (len(state.timesteps    ) - 1)).long()

												__a:						Union[str, Any]		    =					state.discrete_sigmas[timesteps]
												__a:						List[str]		    =					self.get_adjacent_sigma(snake_case__						, snake_case__						, snake_case__    )
												__a:						int		    =					jnp.zeros_like(snake_case__    )
												__a:						Tuple		    =					(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:						Tuple		    =					diffusion.flatten()
												__a:						Tuple		    =					broadcast_to_shape_from_left(snake_case__						, sample.shape    )
												__a:						List[str]		    =					drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												__a:						Optional[Any]		    =					random.split(snake_case__						, num=1    )
												__a:						Dict		    =					random.normal(key=snake_case__						, shape=sample.shape    )
												__a:						List[str]		    =					sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												__a:						Tuple		    =					prev_sample_mean + diffusion * noise  # add impact of diffusion field g

												if not return_dict:
																	return (prev_sample, prev_sample_mean, state)

												return FlaxSdeVeOutput(prev_sample=snake_case__						, prev_sample_mean=snake_case__						, state=snake_case__    )






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			ScoreSdeVeSchedulerState						, snake_case__						:			jnp.ndarray						, snake_case__						:			jnp.ndarray						, snake_case__						:			random.KeyArray						, snake_case__						:			bool = True						, ):






												"""simple docstring"""



												if state.timesteps is None:
																	raise ValueError(
																	    """`state.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:						Tuple		    =					random.split(snake_case__						, num=1    )
												__a:						Tuple		    =					random.normal(key=snake_case__						, shape=sample.shape    )

												# compute step size from the model_output, the noise, and the snr
												__a:						List[Any]		    =					jnp.linalg.norm(snake_case__    )
												__a:						List[str]		    =					jnp.linalg.norm(snake_case__    )
												__a:						Union[str, Any]		    =					(self.config.snr * noise_norm / grad_norm) ** 2 * 2
												__a:						str		    =					step_size * jnp.ones(sample.shape[0]    )

												# compute corrected sample: model_output term and noise term
												__a:						Dict		    =					step_size.flatten()
												__a:						Optional[int]		    =					broadcast_to_shape_from_left(snake_case__						, sample.shape    )
												__a:						Tuple		    =					sample + step_size * model_output
												__a:						Dict		    =					prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																	return (prev_sample, state)

												return FlaxSdeVeOutput(prev_sample=snake_case__						, state=snake_case__    )






							def __len__(					self						:			Tuple    ):






												"""simple docstring"""



												return self.config.num_train_timesteps


"""simple docstring"""
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 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, logging


lowercase		:						Tuple =					logging.get_logger(__name__)




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						['''pixel_values''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			bool = True						, snake_case__						:			Optional[Dict[str, int]] = None						, snake_case__						:			PILImageResampling = PILImageResampling.BILINEAR						, snake_case__						:			bool = True						, snake_case__						:			Union[int, float] = 1 / 2_5_5						, snake_case__						:			bool = True						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, **snake_case__						:			Optional[int]						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												__a:						Dict		    =					size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
												__a:						Optional[Any]		    =					get_size_dict(snake_case__    )
												__a:						Union[str, Any]		    =					do_resize
												__a:						str		    =					do_rescale
												__a:						Tuple		    =					do_normalize
												__a:						Dict		    =					size
												__a:						str		    =					resample
												__a:						Any		    =					rescale_factor
												__a:						Optional[int]		    =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												__a:						int		    =					image_std if image_std is not None else IMAGENET_STANDARD_STD






							def 							__lowercase  (					self						:			Optional[Any]						, snake_case__						:			np.ndarray						, snake_case__						:			Dict[str, int]						, snake_case__						:			PILImageResampling = PILImageResampling.BILINEAR						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			List[str]						, ):






												"""simple docstring"""



												__a:						int		    =					get_size_dict(snake_case__    )
												if "height" not in size or "width" not in size:
																	raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""    )
												return resize(
												    snake_case__						, size=(size["""height"""], size["""width"""])						, resample=snake_case__						, data_format=snake_case__						, **snake_case__    )






							def 							__lowercase  (					self						:			List[str]						, snake_case__						:			np.ndarray						, snake_case__						:			float						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			Union[str, Any]    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			np.ndarray						, snake_case__						:			Union[float, List[float]]						, snake_case__						:			Union[float, List[float]]						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			Union[str, Any]						, ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			ImageInput						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Dict[str, int] = None						, snake_case__						:			PILImageResampling = None						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Optional[float] = None						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[str, TensorType]] = None						, snake_case__						:			Union[str, ChannelDimension] = ChannelDimension.FIRST						, **snake_case__						:			List[str]						, ):






												"""simple docstring"""



												__a:						Any		    =					do_resize if do_resize is not None else self.do_resize
												__a:						Any		    =					do_rescale if do_rescale is not None else self.do_rescale
												__a:						int		    =					do_normalize if do_normalize is not None else self.do_normalize
												__a:						Any		    =					resample if resample is not None else self.resample
												__a:						Any		    =					rescale_factor if rescale_factor is not None else self.rescale_factor
												__a:						Dict		    =					image_mean if image_mean is not None else self.image_mean
												__a:						List[str]		    =					image_std if image_std is not None else self.image_std

												__a:						int		    =					size if size is not None else self.size
												__a:						Any		    =					get_size_dict(snake_case__    )

												__a:						List[str]		    =					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:
																	raise ValueError("""Size must be specified if do_resize is True."""    )

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

												# All transformations expect numpy arrays.
												__a:						str		    =					[to_numpy_array(snake_case__    ) for image in images]

												if do_resize:
																	__a:						Dict		    =					[self.resize(image=snake_case__						, size=snake_case__						, resample=snake_case__    ) for image in images]

												if do_rescale:
																	__a:						str		    =					[self.rescale(image=snake_case__						, scale=snake_case__    ) for image in images]

												if do_normalize:
																	__a:						Dict		    =					[self.normalize(image=snake_case__						, mean=snake_case__						, std=snake_case__    ) for image in images]

												__a:						int		    =					[to_channel_dimension_format(snake_case__						, snake_case__    ) for image in images]

												__a:						str		    =					{"""pixel_values""": images}
												return BatchFeature(data=snake_case__						, tensor_type=snake_case__    )
