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


UpperCAmelCase      		=					logging.get_logger(__name__)


if is_vision_available():
 import PIL




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						str					     =			['pixel_values']

 def __init__(      self							:		Any							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		Dict[str, int] = None							,      lowerCamelCase							:		PILImageResampling = PILImageResampling.BICUBIC							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		Dict[str, int] = None							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		Union[int, float] = 1 / 2_5_5							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		Optional[Union[float, List[float]]] = None							,      lowerCamelCase							:		Optional[Union[float, List[float]]] = None							,      lowerCamelCase							:		bool = True							,      **lowerCamelCase							:		Any							,      )			->				None:
        super().__init__(**lowerCamelCase	)
        _UpperCamelCase  :							Optional[int]	    =	size if size is not None else {'shortest_edge': 2_2_4}
        _UpperCamelCase  :							Any	    =	get_size_dict(lowerCamelCase							,      default_to_square=lowerCamelCase	)
        _UpperCamelCase  :							Optional[int]	    =	crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        _UpperCamelCase  :							int	    =	get_size_dict(lowerCamelCase	)

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

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		np.ndarray							,      lowerCamelCase							:		Dict[str, int]							,      lowerCamelCase							:		PILImageResampling = PILImageResampling.BICUBIC							,      lowerCamelCase							:		Optional[Union[str, ChannelDimension]] = None							,      **lowerCamelCase							:		List[str]							,      )			->				np.ndarray:
        _UpperCamelCase  :							Dict	    =	get_size_dict(lowerCamelCase							,      default_to_square=lowerCamelCase	)
        _UpperCamelCase  :							Union[str, Any]	    =	get_resize_output_image_size(
            lowerCamelCase							,      size=(size['height'], size['width'])							,      default_to_square=lowerCamelCase	)
        return resize(lowerCamelCase							,      size=lowerCamelCase							,      resample=lowerCamelCase							,      data_format=lowerCamelCase							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		np.ndarray							,      lowerCamelCase							:		Dict[str, int]							,      lowerCamelCase							:		Optional[Union[str, ChannelDimension]] = None							,      **lowerCamelCase							:		int							,      )			->				np.ndarray:
        _UpperCamelCase  :							Any	    =	get_size_dict(lowerCamelCase	)
        return center_crop(lowerCamelCase							,      size=(size['height'], size['width'])							,      data_format=lowerCamelCase							,      **lowerCamelCase	)

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

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

 def        __snake_case       (      self							:		str							,      lowerCamelCase							:		ImageInput							,      lowerCamelCase							:		bool = None							,      lowerCamelCase							:		Dict[str, int] = None							,      lowerCamelCase							:		PILImageResampling = None							,      lowerCamelCase							:		bool = None							,      lowerCamelCase							:		int = None							,      lowerCamelCase							:		bool = None							,      lowerCamelCase							:		float = None							,      lowerCamelCase							:		bool = None							,      lowerCamelCase							:		Optional[Union[float, List[float]]] = None							,      lowerCamelCase							:		Optional[Union[float, List[float]]] = None							,      lowerCamelCase							:		bool = None							,      lowerCamelCase							:		Optional[Union[str, TensorType]] = None							,      lowerCamelCase							:		Optional[ChannelDimension] = ChannelDimension.FIRST							,      **lowerCamelCase							:		int							,      )			->				PIL.Image.Image:
        _UpperCamelCase  :							List[Any]	    =	do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase  :							str	    =	size if size is not None else self.size
        _UpperCamelCase  :							str	    =	get_size_dict(lowerCamelCase							,      default_to_square=lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	resample if resample is not None else self.resample
        _UpperCamelCase  :							str	    =	do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCamelCase  :							str	    =	crop_size if crop_size is not None else self.crop_size
        _UpperCamelCase  :							Optional[Any]	    =	get_size_dict(lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase  :							Union[str, Any]	    =	rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase  :							List[str]	    =	do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase  :							str	    =	image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase  :							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  :							Optional[Any]	    =	make_list_of_images(lowerCamelCase	)

        if not valid_images(lowerCamelCase	):
               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  :							List[Any]	    =	[convert_to_rgb(lowerCamelCase	) for image in images]

        # All transformations expect numpy arrays.
        _UpperCamelCase  :							Optional[int]	    =	[to_numpy_array(lowerCamelCase	) for image in images]

        if do_resize:
               _UpperCamelCase  :							List[str]	    =	[self.resize(image=lowerCamelCase							,      size=lowerCamelCase							,      resample=lowerCamelCase	) for image in images]

        if do_center_crop:
               _UpperCamelCase  :							Optional[int]	    =	[self.center_crop(image=lowerCamelCase							,      size=lowerCamelCase	) for image in images]

        if do_rescale:
               _UpperCamelCase  :							Optional[Any]	    =	[self.rescale(image=lowerCamelCase							,      scale=lowerCamelCase	) for image in images]

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

        _UpperCamelCase  :							Any	    =	[to_channel_dimension_format(lowerCamelCase							,      lowerCamelCase	) for image in images]

        _UpperCamelCase  :							Tuple	    =	{'pixel_values': images}
        return BatchFeature(data=lowerCamelCase							,      tensor_type=lowerCamelCase	)










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     )