





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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


__lowercase							:Optional[int]							=	logging.get_logger(__name__)



class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    ["pixel_values"]

							def __init__(   self					:  Union[str, Any]				,						a					:  bool = True				,						a					:  Dict[str, int] = None				,						a					:  bool = True				,						a					:  Dict[str, int] = None				,						a					:  PILImageResampling = PILImageResampling.BICUBIC				,						a					:  bool = True				,						a					:  Union[int, float] = 1 / 2_55				,						a					:  bool = True				,						a					:  Optional[Union[float, List[float]]] = None				,						a					:  Optional[Union[float, List[float]]] = None				,						**a					:  List[str]				,						)						->None:
													super().__init__(**a     )
													SCREAMING_SNAKE_CASE__  :					Dict =       crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
													SCREAMING_SNAKE_CASE__  :					Tuple =       get_size_dict(a				,						param_name="crop_size"     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       size if size is not None else {"height": 2_24, "width": 2_24}
													SCREAMING_SNAKE_CASE__  :					List[str] =       get_size_dict(a     )

													SCREAMING_SNAKE_CASE__  :					str =       do_center_crop
													SCREAMING_SNAKE_CASE__  :					List[Any] =       crop_size
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       do_resize
													SCREAMING_SNAKE_CASE__  :					str =       size
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       resample
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       do_rescale
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       rescale_factor
													SCREAMING_SNAKE_CASE__  :					List[Any] =       do_normalize
													SCREAMING_SNAKE_CASE__  :					Any =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def   A_	(   self					:  Dict				,						a					:  np.ndarray				,						a					:  Dict[str, int]				,						a					:  Optional[int] = None				,						a					:  Optional[Union[str, ChannelDimension]] = None				,						**a					:  Tuple				,						)						->np.ndarray:
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.size if size is None else size
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       get_size_dict(a     )
													SCREAMING_SNAKE_CASE__  :					str =       get_size_dict(a				,						param_name="crop_size"     )

													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[str] =       get_image_size(a     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       min(a				,						a     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       (size["height"] / crop_size["height"]) * min_dim
													SCREAMING_SNAKE_CASE__  :					List[Any] =       (size["width"] / crop_size["width"]) * min_dim
													return center_crop(a				,						size=(cropped_height, cropped_width)				,						data_format=a				,						**a     )

							def   A_	(   self					:  List[str]				,						a					:  np.ndarray				,						a					:  Dict[str, int]				,						a					:  PILImageResampling = PIL.Image.BICUBIC				,						a					:  Optional[Union[str, ChannelDimension]] = None				,						**a					:  str				,						)						->np.ndarray:
													SCREAMING_SNAKE_CASE__  :					str =       get_size_dict(a     )
													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(
													    a				,						size=(size["height"], size["width"])				,						resample=a				,						data_format=a				,						**a     )

							def   A_	(   self					:  Optional[int]				,						a					:  np.ndarray				,						a					:  Union[int, float]				,						a					:  Optional[Union[str, ChannelDimension]] = None				,						**a					:  Tuple				,						)						->int:
													return rescale(a				,						scale=a				,						data_format=a				,						**a     )

							def   A_	(   self					:  List[str]				,						a					:  np.ndarray				,						a					:  Union[float, List[float]]				,						a					:  Union[float, List[float]]				,						a					:  Optional[Union[str, ChannelDimension]] = None				,						**a					:  Tuple				,						)						->np.ndarray:
													return normalize(a				,						mean=a				,						std=a				,						data_format=a				,						**a     )






							def   A_	(   self					:  Optional[int]				,						a					:  ImageInput				,						a					:  Optional[bool] = None				,						a					:  Optional[Dict[str, int]] = None				,						a					:  Optional[bool] = None				,						a					:  Optional[Dict[str, int]] = None				,						a					:  PILImageResampling = None				,						a					:  Optional[bool] = None				,						a					:  Optional[float] = None				,						a					:  Optional[bool] = None				,						a					:  Optional[Union[float, List[float]]] = None				,						a					:  Optional[Union[float, List[float]]] = None				,						a					:  Optional[Union[str, TensorType]] = None				,						a					:  ChannelDimension = ChannelDimension.FIRST				,						**a					:  List[str]				,						)						->PIL.Image.Image:
													SCREAMING_SNAKE_CASE__  :					Any =       do_center_crop if do_center_crop is not None else self.do_center_crop
													SCREAMING_SNAKE_CASE__  :					Any =       crop_size if crop_size is not None else self.crop_size
													SCREAMING_SNAKE_CASE__  :					List[Any] =       get_size_dict(a				,						param_name="crop_size"     )
													SCREAMING_SNAKE_CASE__  :					List[str] =       do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE__  :					List[str] =       size if size is not None else self.size
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       get_size_dict(a     )
													SCREAMING_SNAKE_CASE__  :					str =       resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE__  :					Any =       image_std if image_std is not None else self.image_std

													SCREAMING_SNAKE_CASE__  :					str =       make_list_of_images(a     )

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

													if do_center_crop and crop_size is None:
																			raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."     )

													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."     )

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

													# All transformations expect numpy arrays.
													SCREAMING_SNAKE_CASE__  :					List[Any] =       [to_numpy_array(a     ) for image in images]

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

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

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

													if do_normalize:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       [self.normalize(image=a				,						mean=a				,						std=a     ) for image in images]

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       [to_channel_dimension_format(a				,						a     ) for image in images]

													SCREAMING_SNAKE_CASE__  :					str =       {"pixel_values": images}
													return BatchFeature(data=a				,						tensor_type=a     )






from typing import List, Optional, Tuple, Union

import torch

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



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  str				,						a					:  Optional[Any]     )						->Any:
													super().__init__()
													self.register_modules(unet=a				,						scheduler=a     )






							@torch.no_grad()
							def __call__(   self					:  int				,						a					:  int = 1				,						a					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						a					:  int = 10_00				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Union[ImagePipelineOutput, Tuple]:
													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(a				,						generator=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       randn_tensor(a				,						generator=a				,						device=self.device     )

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

													for t in self.progress_bar(self.scheduler.timesteps     ):
																			# 1. predict noise model_output
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.unet(a				,						a     ).sample

																			# 2. compute previous image: x_t -> x_t-1
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.scheduler.step(a				,						a				,						a				,						generator=a     ).prev_sample

													SCREAMING_SNAKE_CASE__  :					List[str] =       (image / 2 + 0.5).clamp(0				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       image.cpu().permute(0				,						2				,						3				,						1     ).numpy()
													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )