


'''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,
    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


lowerCamelCase							:   List[str]  						=	logging.get_logger(__name__)


if is_vision_available():
 import PIL
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Dict  					=							["""pixel_values"""]



      def __init__(self    :    Any   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Dict[str, int] = None   ,						UpperCamelCase    :    PILImageResampling = PILImageResampling.BICUBIC   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Dict[str, int] = None   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Union[int, float] = 1 / 255   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    bool = True   ,						**UpperCamelCase    :    Any   ,						):

             '''simple docstring'''







             super().__init__(**UpperCamelCase )
             lowercase__      			= size if size is not None else {'''shortest_edge''': 224}
             lowercase__      			= get_size_dict(UpperCamelCase   ,						default_to_square=UpperCamelCase )
             lowercase__      			= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
             lowercase__      			= get_size_dict(UpperCamelCase   ,						default_to_square=UpperCamelCase   ,						param_name='''crop_size''' )

             lowercase__      			= do_resize
             lowercase__      			= size
             lowercase__      			= resample
             lowercase__      			= do_center_crop
             lowercase__      			= crop_size
             lowercase__      			= do_rescale
             lowercase__      			= rescale_factor
             lowercase__      			= do_normalize
             lowercase__      			= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             lowercase__      			= image_std if image_std is not None else OPENAI_CLIP_STD
             lowercase__      			= do_convert_rgb



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Dict[str, int]   ,						UpperCamelCase    :    PILImageResampling = PILImageResampling.BICUBIC   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    List[Any]   ,						):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Dict[str, int]   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    str   ,						):

             '''simple docstring'''







             lowercase__      			= get_size_dict(UpperCamelCase )
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}" )
             return center_crop(UpperCamelCase   ,						size=(size['''height'''], size['''width'''])   ,						data_format=UpperCamelCase   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    Dict   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Union[int, float]   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    List[Any]   ,						):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Union[float, List[float]]   ,						UpperCamelCase    :    Union[float, List[float]]   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    List[str]   ,						):

             '''simple docstring'''







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






      def       UpperCamelCase__		(self    :    List[str]   ,						UpperCamelCase    :    ImageInput   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    Dict[str, int] = None   ,						UpperCamelCase    :    PILImageResampling = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    int = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    float = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    Optional[Union[str, TensorType]] = None   ,						UpperCamelCase    :    Optional[ChannelDimension] = ChannelDimension.FIRST   ,						**UpperCamelCase    :    Optional[int]   ,						):

             '''simple docstring'''







             lowercase__      			= do_resize if do_resize is not None else self.do_resize
             lowercase__      			= size if size is not None else self.size
             lowercase__      			= get_size_dict(UpperCamelCase   ,						param_name='''size'''   ,						default_to_square=UpperCamelCase )
             lowercase__      			= resample if resample is not None else self.resample
             lowercase__      			= do_center_crop if do_center_crop is not None else self.do_center_crop
             lowercase__      			= crop_size if crop_size is not None else self.crop_size
             lowercase__      			= get_size_dict(UpperCamelCase   ,						param_name='''crop_size'''   ,						default_to_square=UpperCamelCase )
             lowercase__      			= do_rescale if do_rescale is not None else self.do_rescale
             lowercase__      			= rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__      			= do_normalize if do_normalize is not None else self.do_normalize
             lowercase__      			= image_mean if image_mean is not None else self.image_mean
             lowercase__      			= image_std if image_std is not None else self.image_std
             lowercase__      			= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             lowercase__      			= make_list_of_images(UpperCamelCase )

             if not valid_images(UpperCamelCase ):
                    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:
                    lowercase__      			= [convert_to_rgb(UpperCamelCase ) for image in images]

             # All transformations expect numpy arrays.
             lowercase__      			= [to_numpy_array(UpperCamelCase ) for image in images]

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

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

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

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

             lowercase__      			= [to_channel_dimension_format(UpperCamelCase   ,						UpperCamelCase ) for image in images]

             lowercase__      			= {'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase   ,						tensor_type=UpperCamelCase )






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
