



'''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		     =  logging.get_logger(__name__)


if is_vision_available():
							import PIL

class       __magic_name__		(       _UpperCamelCase  ):
							lowerCAmelCase			:       Optional[Any]  							=   ['pixel_values']







							def __init__(				self   :			Union[str, 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   :			Optional[Any]      ,):
									super().__init__(**_UpperCAmelCase						)
									_a	: Optional[int]								=				size if size is not None else {'shortest_edge': 224}
									_a	: Dict								=				get_size_dict(_UpperCAmelCase      ,default_to_square=_UpperCAmelCase						)
									_a	: str								=				crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_a	: Tuple								=				get_size_dict(_UpperCAmelCase      ,default_to_square=_UpperCAmelCase      ,param_name='crop_size'						)

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







							def 				__lowercase     (				self   :			str      ,_UpperCAmelCase   :			np.ndarray      ,_UpperCAmelCase   :			Dict[str, int]      ,_UpperCAmelCase   :			PILImageResampling = PILImageResampling.BICUBIC      ,_UpperCAmelCase   :			Optional[Union[str, ChannelDimension]] = None      ,**_UpperCAmelCase   :			Any      ,):
									_a	: Dict								=				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()}"""						)
									_a	: List[Any]								=				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 				__lowercase     (				self   :			str      ,_UpperCAmelCase   :			np.ndarray      ,_UpperCAmelCase   :			Dict[str, int]      ,_UpperCAmelCase   :			Optional[Union[str, ChannelDimension]] = None      ,**_UpperCAmelCase   :			Union[str, Any]      ,):
									_a	: str								=				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 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			np.ndarray      ,_UpperCAmelCase   :			Union[int, float]      ,_UpperCAmelCase   :			Optional[Union[str, ChannelDimension]] = None      ,**_UpperCAmelCase   :			Optional[int]      ,):
									return rescale(_UpperCAmelCase      ,scale=_UpperCAmelCase      ,data_format=_UpperCAmelCase      ,**_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[int]      ,_UpperCAmelCase   :			np.ndarray      ,_UpperCAmelCase   :			Union[float, List[float]]      ,_UpperCAmelCase   :			Union[float, List[float]]      ,_UpperCAmelCase   :			Optional[Union[str, ChannelDimension]] = None      ,**_UpperCAmelCase   :			List[Any]      ,):
									return normalize(_UpperCAmelCase      ,mean=_UpperCAmelCase      ,std=_UpperCAmelCase      ,data_format=_UpperCAmelCase      ,**_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Any      ,_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]      ,):
									_a	: Optional[Any]								=				do_resize if do_resize is not None else self.do_resize
									_a	: Any								=				size if size is not None else self.size
									_a	: Tuple								=				get_size_dict(_UpperCAmelCase      ,param_name='size'      ,default_to_square=_UpperCAmelCase						)
									_a	: str								=				resample if resample is not None else self.resample
									_a	: List[Any]								=				do_center_crop if do_center_crop is not None else self.do_center_crop
									_a	: Optional[int]								=				crop_size if crop_size is not None else self.crop_size
									_a	: Any								=				get_size_dict(_UpperCAmelCase      ,param_name='crop_size'      ,default_to_square=_UpperCAmelCase						)
									_a	: Union[str, Any]								=				do_rescale if do_rescale is not None else self.do_rescale
									_a	: List[str]								=				rescale_factor if rescale_factor is not None else self.rescale_factor
									_a	: Any								=				do_normalize if do_normalize is not None else self.do_normalize
									_a	: Any								=				image_mean if image_mean is not None else self.image_mean
									_a	: int								=				image_std if image_std is not None else self.image_std
									_a	: List[str]								=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									_a	: Tuple								=				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:
											_a	: List[str]								=				[convert_to_rgb(_UpperCAmelCase						) for image in images]

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

									if do_resize:
											_a	: Union[str, Any]								=				[self.resize(image=_UpperCAmelCase      ,size=_UpperCAmelCase      ,resample=_UpperCAmelCase						) for image in images]

									if do_center_crop:
											_a	: Dict								=				[self.center_crop(image=_UpperCAmelCase      ,size=_UpperCAmelCase						) for image in images]

									if do_rescale:
											_a	: Optional[int]								=				[self.rescale(image=_UpperCAmelCase      ,scale=_UpperCAmelCase						) for image in images]

									if do_normalize:
											_a	: List[Any]								=				[self.normalize(image=_UpperCAmelCase      ,mean=_UpperCAmelCase      ,std=_UpperCAmelCase						) for image in images]

									_a	: Union[str, Any]								=				[to_channel_dimension_format(_UpperCAmelCase      ,_UpperCAmelCase						) for image in images]

									_a	: str								=				{'pixel_values': images}
									return BatchFeature(data=_UpperCAmelCase      ,tensor_type=_UpperCAmelCase						)









'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   StableDiffusionInpaintPipeline
							lowerCAmelCase			:       int  							=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							lowerCAmelCase			:       Optional[Any]  							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowerCAmelCase			:       List[str]  							=   frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase			:       Optional[Any]  							=   frozenset([]  )







							def 				__lowercase     (				self   :			Dict						):
									torch.manual_seed(0						)
									_a	: Optional[int]								=				UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,cross_attention_dim=32      ,attention_head_dim=(2, 4)      ,use_linear_projection=_UpperCAmelCase      ,)
									_a	: Optional[int]								=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase						)
									torch.manual_seed(0						)
									_a	: Dict								=				AutoencoderKL(
									    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,latent_channels=4      ,sample_size=128      ,)
									torch.manual_seed(0						)
									_a	: Union[str, Any]								=				CLIPTextConfig(
									    bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=1000      ,hidden_act='gelu'      ,projection_dim=512      ,)
									_a	: str								=				CLIPTextModel(_UpperCAmelCase						)
									_a	: Any								=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_a	: Optional[Any]								=				{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			int=0						):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_a	: Any								=				floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
									_a	: Any								=				image.cpu().permute(0      ,2      ,3      ,1						)[0]
									_a	: Optional[int]								=				Image.fromarray(np.uinta(_UpperCAmelCase						)						).convert('RGB'						).resize((64, 64)						)
									_a	: Tuple								=				Image.fromarray(np.uinta(image + 4						)						).convert('RGB'						).resize((64, 64)						)
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: str								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Dict								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
									_a	: Any								=				{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': init_image,
									    'mask_image': mask_image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Optional[int]								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a	: Dict								=				self.get_dummy_components()
									_a	: int								=				StableDiffusionInpaintPipeline(**_UpperCAmelCase						)
									_a	: Any								=				sd_pipe.to(_UpperCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				self.get_dummy_inputs(_UpperCAmelCase						)
									_a	: Tuple								=				sd_pipe(**_UpperCAmelCase						).images
									_a	: Dict								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: List[Any]								=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]						)

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







							def 				__lowercase     (				self   :			List[Any]						):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

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







							def 				__lowercase     (				self   :			List[str]						):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 				__lowercase     (				self   :			int						):
									_a	: Tuple								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Union[str, Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'						)

									_a	: List[Any]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Tuple								=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase      ,safety_checker=_UpperCAmelCase						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: int								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: Any								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: List[str]								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 9E-3







							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'						)

									_a	: int								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: List[str]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,torch_dtype=torch.floataa      ,safety_checker=_UpperCAmelCase      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: Dict								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: int								=				torch.manual_seed(0						)
									_a	: Dict								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: str								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 5E-1







							def 				__lowercase     (				self   :			Tuple						):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_a	: Dict								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[int]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

									_a	: List[str]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Any								=				PNDMScheduler.from_pretrained(_UpperCAmelCase      ,subfolder='scheduler'						)
									_a	: Optional[Any]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,safety_checker=_UpperCAmelCase      ,scheduler=_UpperCAmelCase      ,torch_dtype=torch.floataa      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									_a	: Any								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Optional[Any]								=				torch.manual_seed(0						)
									_a	: int								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: int								=				torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9




