


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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase       		=    logging.get_logger(__name__)


if is_vision_available():
				import PIL




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Any										=						['''pixel_values''']


	def __init__(    self				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = PILImageResampling.BICUBIC				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = True				,     lowercase__ = 1 / 2_5_5				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = True				,     **lowercase__				,     ):
							super().__init__(**lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	size if size is not None else {'''shortest_edge''': 2_2_4}
							__UpperCAmelCase			:							List[Any]	    =	get_size_dict(lowercase__				,     default_to_square=lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
							__UpperCAmelCase			:							Optional[int]	    =	get_size_dict(lowercase__				,     default_to_square=lowercase__				,     param_name='''crop_size''')

							__UpperCAmelCase			:							Optional[int]	    =	do_resize
							__UpperCAmelCase			:							Optional[int]	    =	size
							__UpperCAmelCase			:							Optional[Any]	    =	resample
							__UpperCAmelCase			:							List[Any]	    =	do_center_crop
							__UpperCAmelCase			:							Any	    =	crop_size
							__UpperCAmelCase			:							Any	    =	do_rescale
							__UpperCAmelCase			:							Optional[int]	    =	rescale_factor
							__UpperCAmelCase			:							Dict	    =	do_normalize
							__UpperCAmelCase			:							Dict	    =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							__UpperCAmelCase			:							Optional[int]	    =	image_std if image_std is not None else OPENAI_CLIP_STD
							__UpperCAmelCase			:							Union[str, Any]	    =	do_convert_rgb


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = PILImageResampling.BICUBIC				,     lowercase__ = None				,     **lowercase__				,     ):
							__UpperCAmelCase			:							str	    =	get_size_dict(lowercase__				,     default_to_square=lowercase__)
							if "shortest_edge" not in size:
													raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
							__UpperCAmelCase			:							Any	    =	get_resize_output_image_size(lowercase__				,     size=size['''shortest_edge''']				,     default_to_square=lowercase__)
							return resize(lowercase__				,     size=lowercase__				,     resample=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):
							__UpperCAmelCase			:							str	    =	get_size_dict(lowercase__)
							if "height" not in size or "width" not in size:
													raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}")
							return center_crop(lowercase__				,     size=(size['''height'''], size['''width'''])				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):
							return rescale(lowercase__				,     scale=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):
							return normalize(lowercase__				,     mean=lowercase__				,     std=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = ChannelDimension.FIRST				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Tuple	    =	do_resize if do_resize is not None else self.do_resize
							__UpperCAmelCase			:							int	    =	size if size is not None else self.size
							__UpperCAmelCase			:							Tuple	    =	get_size_dict(lowercase__				,     param_name='''size'''				,     default_to_square=lowercase__)
							__UpperCAmelCase			:							Dict	    =	resample if resample is not None else self.resample
							__UpperCAmelCase			:							Tuple	    =	do_center_crop if do_center_crop is not None else self.do_center_crop
							__UpperCAmelCase			:							Optional[Any]	    =	crop_size if crop_size is not None else self.crop_size
							__UpperCAmelCase			:							Dict	    =	get_size_dict(lowercase__				,     param_name='''crop_size'''				,     default_to_square=lowercase__)
							__UpperCAmelCase			:							Tuple	    =	do_rescale if do_rescale is not None else self.do_rescale
							__UpperCAmelCase			:							Dict	    =	rescale_factor if rescale_factor is not None else self.rescale_factor
							__UpperCAmelCase			:							Union[str, Any]	    =	do_normalize if do_normalize is not None else self.do_normalize
							__UpperCAmelCase			:							Tuple	    =	image_mean if image_mean is not None else self.image_mean
							__UpperCAmelCase			:							Union[str, Any]	    =	image_std if image_std is not None else self.image_std
							__UpperCAmelCase			:							Union[str, Any]	    =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							__UpperCAmelCase			:							List[Any]	    =	make_list_of_images(lowercase__)

							if not valid_images(lowercase__):
													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_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.''')

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
													__UpperCAmelCase			:							Union[str, Any]	    =	[convert_to_rgb(lowercase__) for image in images]

							# All transformations expect numpy arrays.
							__UpperCAmelCase			:							Tuple	    =	[to_numpy_array(lowercase__) for image in images]

							if do_resize:
													__UpperCAmelCase			:							Optional[Any]	    =	[self.resize(image=lowercase__				,     size=lowercase__				,     resample=lowercase__) for image in images]

							if do_center_crop:
													__UpperCAmelCase			:							int	    =	[self.center_crop(image=lowercase__				,     size=lowercase__) for image in images]

							if do_rescale:
													__UpperCAmelCase			:							int	    =	[self.rescale(image=lowercase__				,     scale=lowercase__) for image in images]

							if do_normalize:
													__UpperCAmelCase			:							List[Any]	    =	[self.normalize(image=lowercase__				,     mean=lowercase__				,     std=lowercase__) for image in images]

							__UpperCAmelCase			:							Any	    =	[to_channel_dimension_format(lowercase__				,     lowercase__) for image in images]

							__UpperCAmelCase			:							Tuple	    =	{'''pixel_values''': images}
							return BatchFeature(data=lowercase__				,     tensor_type=lowercase__)





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		UNetaDModel
	_lowerCAmelCase     :		ScoreSdeVeScheduler


	def __init__(    self				,     lowercase__				,     lowercase__):
							super().__init__()
							self.register_modules(unet=lowercase__				,     scheduler=lowercase__)


	@torch.no_grad()
	def __call__(    self				,     lowercase__ = 1				,     lowercase__ = 2_0_0_0				,     lowercase__ = None				,     lowercase__ = "pil"				,     lowercase__ = True				,     **lowercase__				,     ):
							__UpperCAmelCase			:							int	    =	self.unet.config.sample_size
							__UpperCAmelCase			:							Optional[int]	    =	(batch_size, 3, img_size, img_size)

							__UpperCAmelCase			:							int	    =	self.unet

							__UpperCAmelCase			:							Tuple	    =	randn_tensor(lowercase__				,     generator=lowercase__) * self.scheduler.init_noise_sigma
							__UpperCAmelCase			:							List[str]	    =	sample.to(self.device)

							self.scheduler.set_timesteps(lowercase__)
							self.scheduler.set_sigmas(lowercase__)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
													__UpperCAmelCase			:							List[str]	    =	self.scheduler.sigmas[i] * torch.ones(shape[0]				,     device=self.device)

													# correction step
													for _ in range(self.scheduler.config.correct_steps):
																			__UpperCAmelCase			:							int	    =	self.unet(lowercase__				,     lowercase__).sample
																			__UpperCAmelCase			:							str	    =	self.scheduler.step_correct(lowercase__				,     lowercase__				,     generator=lowercase__).prev_sample

													# prediction step
													__UpperCAmelCase			:							Tuple	    =	model(lowercase__				,     lowercase__).sample
													__UpperCAmelCase			:							Dict	    =	self.scheduler.step_pred(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__)

													__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	output.prev_sample, output.prev_sample_mean

							__UpperCAmelCase			:							List[str]	    =	sample_mean.clamp(0				,     1)
							__UpperCAmelCase			:							Union[str, Any]	    =	sample.cpu().permute(0				,     2				,     3				,     1).numpy()
							if output_type == "pil":
													__UpperCAmelCase			:							List[Any]	    =	self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (sample,)

							return ImagePipelineOutput(images=lowercase__)

