
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, logging


lowercase__							:							Union[str, Any]									=			logging.get_logger(__name__)






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  ['pixel_values']



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = 1 / 255     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   None:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			size if size is not None else {'''shortest_edge''': 256}
													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_     ,	default_to_square=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			do_resize
													__UpperCamelCase       =			size
													__UpperCamelCase       =			resample
													__UpperCamelCase       =			do_center_crop
													__UpperCamelCase       =			crop_size
													__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     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   np.ndarray:





													'''simple docstring'''






													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_     ,	default_to_square=SCREAMING_SNAKE_CASE_			)
													if "shortest_edge" not in size:
																			raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"			)
													__UpperCamelCase       =			get_resize_output_image_size(SCREAMING_SNAKE_CASE_     ,	size=size['''shortest_edge''']     ,	default_to_square=SCREAMING_SNAKE_CASE_			)
													return resize(SCREAMING_SNAKE_CASE_     ,	size=SCREAMING_SNAKE_CASE_     ,	resample=SCREAMING_SNAKE_CASE_     ,	data_format=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   np.ndarray:





													'''simple docstring'''






													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
													return center_crop(SCREAMING_SNAKE_CASE_     ,	size=(size['''height'''], size['''width'''])     ,	data_format=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_			)->   np.ndarray:





													'''simple docstring'''






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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   np.ndarray:





													'''simple docstring'''






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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			do_resize if do_resize is not None else self.do_resize
													__UpperCamelCase       =			size if size is not None else self.size
													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_     ,	default_to_square=SCREAMING_SNAKE_CASE_			)
													__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       =			crop_size if crop_size is not None else self.crop_size
													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
													__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       =			make_list_of_images(SCREAMING_SNAKE_CASE_			)

													if not valid_images(SCREAMING_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_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(SCREAMING_SNAKE_CASE_			) for image in images]

													if do_resize:
																			__UpperCamelCase       =			[self.resize(image=SCREAMING_SNAKE_CASE_     ,	size=SCREAMING_SNAKE_CASE_     ,	resample=SCREAMING_SNAKE_CASE_			) for image in images]

													if do_center_crop:
																			__UpperCamelCase       =			[self.center_crop(image=SCREAMING_SNAKE_CASE_     ,	size=SCREAMING_SNAKE_CASE_			) for image in images]

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

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

													__UpperCamelCase       =			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) for image in images]

													__UpperCamelCase       =			{'''pixel_values''': images}
													return BatchFeature(data=SCREAMING_SNAKE_CASE_     ,	tensor_type=SCREAMING_SNAKE_CASE_			)






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													super().__init__()

													# make sure scheduler can always be converted to DDIM
													__UpperCamelCase       =			DDIMScheduler.from_config(scheduler.config			)

													self.register_modules(unet=SCREAMING_SNAKE_CASE_     ,	scheduler=SCREAMING_SNAKE_CASE_			)



							@torch.no_grad()
							def __call__(  self     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = 50     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "pil"     ,	SCREAMING_SNAKE_CASE_ = True     ,	)->   Union[ImagePipelineOutput, Tuple]:





													'''simple docstring'''






													if isinstance(self.unet.config.sample_size     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			(
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			__UpperCamelCase       =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) and len(SCREAMING_SNAKE_CASE_			) != batch_size:
																			raise ValueError(
																			    F"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_			)}, but requested an effective batch"
																			    F" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

													__UpperCamelCase       =			randn_tensor(SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	device=self.device     ,	dtype=self.unet.dtype			)

													# set step values
													self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)

													for t in self.progress_bar(self.scheduler.timesteps			):
																			# 1. predict noise model_output
																			__UpperCamelCase       =			self.unet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			).sample

																			# 2. predict previous mean of image x_t-1 and add variance depending on eta
																			# eta corresponds to η in paper and should be between [0, 1]
																			# do x_t -> x_t-1
																			__UpperCamelCase       =			self.scheduler.step(
																			    SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	eta=SCREAMING_SNAKE_CASE_     ,	use_clipped_model_output=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_			).prev_sample

													__UpperCamelCase       =			(image / 2 + 0.5).clamp(0     ,	1			)
													__UpperCamelCase       =			image.cpu().permute(0     ,	2     ,	3     ,	1			).numpy()
													if output_type == "pil":
																			__UpperCamelCase       =			self.numpy_to_pil(SCREAMING_SNAKE_CASE_			)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)




