


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


if is_vision_available():
							import PIL


lowerCAmelCase				:Optional[int]        =      logging.get_logger(__name__)
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Dict				   =    ["""pixel_values"""]







		def __init__( self		:						int  ,					_A		:						bool = True  ,					_A		:						Dict[str, int] = None  ,					_A		:						int = 0.9  ,					_A		:						PILImageResampling = PILImageResampling.BICUBIC  ,					_A		:						bool = True  ,					_A		:						Dict[str, int] = None  ,					_A		:						Union[int, float] = 1 / 255  ,					_A		:						bool = True  ,					_A		:						bool = True  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					**_A		:						int  ,					)  ->			None:
			super().__init__(**_A      )
			__magic_name__	:   Optional[int]					     =					size if size is not None else {'shortest_edge': 224}
			__magic_name__	:   Dict					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   Any					     =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
			__magic_name__	:   Optional[int]					     =					get_size_dict(_A  ,					param_name='crop_size'      )

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







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						Optional[float] = None  ,					_A		:						PILImageResampling = PILImageResampling.BICUBIC  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						int  ,					)  ->			np.ndarray:
			__magic_name__	:   str					     =					get_size_dict(_A  ,					default_to_square=_A      )
			if "shortest_edge" not in size and ("height" not in size or "width" not in size):
				raise ValueError(F'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'      )
			if crop_pct is not None:
				if "shortest_edge" in size:
					__magic_name__	:   Optional[int]					     =					int(size['shortest_edge'] / crop_pct      )
				elif "height" in size and "width" in size:
					if size["height"] == size["width"]:
						__magic_name__	:   Optional[Any]					     =					int(size['height'] / crop_pct      )
					else:
						__magic_name__	:   Union[str, Any]					     =					(int(size['height'] / crop_pct      ), int(size['width'] / crop_pct      ))
				else:
					raise ValueError('Invalid size for resize: {}'.format(_A      )      )

				__magic_name__	:   Dict					     =					get_resize_output_image_size(_A  ,					size=_A  ,					default_to_square=_A      )
			else:
				if "shortest_edge" in size:
					__magic_name__	:   Union[str, Any]					     =					get_resize_output_image_size(_A  ,					size=size['shortest_edge']  ,					default_to_square=_A      )
				elif "height" in size and "width" in size:
					__magic_name__	:   Any					     =					(size['height'], size['width'])
				else:
					raise ValueError('Invalid size for resize: {}'.format(_A      )      )

			return resize(_A  ,					size=_A  ,					resample=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						Any  ,					)  ->			np.ndarray:
			__magic_name__	:   Optional[Any]					     =					get_size_dict(_A      )
			if "height" not in size or "width" not in size:
				raise ValueError(F'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'      )
			return center_crop(_A  ,					size=(size['height'], size['width'])  ,					data_format=_A  ,					**_A      )







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







		def        __lowerCAmelCase		( self		:						Any  ,					_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        __lowerCAmelCase		( self		:						Dict  ,					_A		:						ImageInput  ,					_A		:						bool = None  ,					_A		:						Dict[str, int] = None  ,					_A		:						int = None  ,					_A		:						PILImageResampling = None  ,					_A		:						bool = None  ,					_A		:						Dict[str, int] = None  ,					_A		:						bool = None  ,					_A		:						float = None  ,					_A		:						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		:						int  ,					)  ->			PIL.Image.Image:
			__magic_name__	:   Union[str, Any]					     =					do_resize if do_resize is not None else self.do_resize
			__magic_name__	:   Tuple					     =					crop_pct if crop_pct is not None else self.crop_pct
			__magic_name__	:   Optional[int]					     =					resample if resample is not None else self.resample
			__magic_name__	:   int					     =					do_center_crop if do_center_crop is not None else self.do_center_crop
			__magic_name__	:   Tuple					     =					do_rescale if do_rescale is not None else self.do_rescale
			__magic_name__	:   Optional[Any]					     =					rescale_factor if rescale_factor is not None else self.rescale_factor
			__magic_name__	:   Dict					     =					do_normalize if do_normalize is not None else self.do_normalize
			__magic_name__	:   Union[str, Any]					     =					image_mean if image_mean is not None else self.image_mean
			__magic_name__	:   Optional[int]					     =					image_std if image_std is not None else self.image_std

			__magic_name__	:   str					     =					size if size is not None else self.size
			__magic_name__	:   Any					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   Optional[int]					     =					crop_size if crop_size is not None else self.crop_size
			__magic_name__	:   List[str]					     =					get_size_dict(_A  ,					param_name='crop_size'      )

			__magic_name__	:   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_resize and size is None or resample is None:
				raise ValueError('Size and resample must be specified if do_resize is True.'      )

			if do_center_crop and crop_pct is None:
				raise ValueError('Crop_pct 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.
			__magic_name__	:   str					     =					[to_numpy_array(_A      ) for image in images]

			if do_resize:
				__magic_name__	:   Any					     =					[self.resize(image=_A  ,					size=_A  ,					crop_pct=_A  ,					resample=_A      ) for image in images]

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

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

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

			__magic_name__	:   Any					     =					[to_channel_dimension_format(_A  ,					_A      ) for image in images]

			__magic_name__	:   Union[str, Any]					     =					{'pixel_values': images}
			return BatchFeature(data=_A  ,					tensor_type=_A      )



'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		@property
		def        __lowerCAmelCase		( self		:						Dict      )  ->			List[str]:
			torch.manual_seed(0      )
			__magic_name__	:   Dict					     =					UNetaDModel(
			    block_out_channels=(32, 64)  ,					layers_per_block=2  ,					sample_size=32  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,					)
			return model







		def        __lowerCAmelCase		( self		:						str      )  ->			Any:
			__magic_name__	:   Union[str, Any]					     =					self.dummy_uncond_unet
			__magic_name__	:   str					     =					KarrasVeScheduler()

			__magic_name__	:   List[Any]					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Dict					     =					torch.manual_seed(0      )
			__magic_name__	:   int					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   str					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'  ,					return_dict=_A      )[0]

			__magic_name__	:   int					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   str					     =					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__magic_name__	:   List[Any]					     =					np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2







@slow
@require_torch
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			str:
			__magic_name__	:   Optional[int]					     =					'google/ncsnpp-celebahq-256'
			__magic_name__	:   List[str]					     =					UNetaDModel.from_pretrained(_A      )
			__magic_name__	:   int					     =					KarrasVeScheduler()

			__magic_name__	:   str					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   Union[str, Any]					     =					pipe(num_inference_steps=20  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   List[str]					     =					image[0, -3:, -3:, -1]
			assert image.shape == (1, 256, 256, 3)
			__magic_name__	:   int					     =					np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2