





"""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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_snake_case     :		Optional[Any]       						= logging.get_logger(__name__)






class 					_UpperCAmelCase   (	_SCREAMING_SNAKE_CASE ):
					UpperCamelCase      =							["pixel_values"]


					def __init__(					self			:Union[str, Any]     ,       __UpperCamelCase			:int = True     ,       __UpperCamelCase			:List[str] = None     ,       __UpperCamelCase			:Any = PILImageResampling.BILINEAR     ,       __UpperCamelCase			:Tuple = True     ,       __UpperCamelCase			:List[Any] = None     ,       __UpperCamelCase			:Union[str, Any] = True     ,       __UpperCamelCase			:Optional[int] = 1 / 2_55     ,       __UpperCamelCase			:List[str] = True     ,       __UpperCamelCase			:List[str] = None     ,       __UpperCamelCase			:str = None     ,       **__UpperCamelCase			:int     ,       ):
												super().__init__(**A_		)
												A		=  size if size is not None else {"shortest_edge": 2_56}
												A		=  get_size_dict(A_     ,       default_to_square=A_		)
												A		=  crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
												A		=  get_size_dict(A_		)
												A		=  do_resize
												A		=  size
												A		=  resample
												A		=  do_center_crop
												A		=  crop_size
												A		=  do_rescale
												A		=  rescale_factor
												A		=  do_normalize
												A		=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												A		=  image_std if image_std is not None else IMAGENET_STANDARD_STD


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:List[Any] = PILImageResampling.BICUBIC     ,       __UpperCamelCase			:Union[str, Any] = None     ,       **__UpperCamelCase			:int     ,       ):
												A		=  get_size_dict(A_     ,       default_to_square=A_		)
												if "shortest_edge" not in size:
																			raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"		)
												A		=  get_resize_output_image_size(A_     ,       size=size["shortest_edge"]     ,       default_to_square=A_		)
												return resize(A_     ,       size=A_     ,       resample=A_     ,       data_format=A_     ,       **A_		)


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Any = None     ,       **__UpperCamelCase			:Optional[Any]     ,       ):
												A		=  get_size_dict(A_		)
												return center_crop(A_     ,       size=(size["height"], size["width"])     ,       data_format=A_     ,       **A_		)


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[Any]     ,       __UpperCamelCase			:Optional[int] = None     ,       **__UpperCamelCase			:Tuple		):
												return rescale(A_     ,       scale=A_     ,       data_format=A_     ,       **A_		)


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:List[Any]     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Optional[int] = None     ,       **__UpperCamelCase			:Dict     ,       ):
												return normalize(A_     ,       mean=A_     ,       std=A_     ,       data_format=A_     ,       **A_		)


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

												A		=  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:
																			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.
												A		=  [to_numpy_array(A_		) for image in images]

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

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

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

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

												A		=  [to_channel_dimension_format(A_     ,       A_		) for image in images]

												A		=  {"pixel_values": images}
												return BatchFeature(data=A_     ,       tensor_type=A_		)




import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


@slow
@require_torch_gpu
class 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]       )

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



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-1



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_dpmpp_2m'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe(
				    [prompt]				,      generator=A_				,      guidance_scale=7.5				,      num_inference_steps=15				,      output_type='np'				,      use_karras_sigmas=A_				,      )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]       )

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