





"""simple docstring"""


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


a_       							=    logging.get_logger(__name__)






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )	->	Tuple[int, int]:
				def constraint_to_multiple_of(__lowercase     ,			__lowercase     ,			__lowercase=0     ,			__lowercase=None   ):
								_A					  =			round(val / multiple   ) * multiple

								if max_val is not None and x > max_val:
												_A					  =			math.floor(val / multiple   ) * multiple

								if x < min_val:
												_A					  =			math.ceil(val / multiple   ) * multiple

								return x

				_A					  =			(output_size, output_size) if isinstance(__lowercase     ,			__lowercase   ) else output_size

				_A  ,		_A					  =			get_image_size(__lowercase   )
				_A  ,		_A					  =			output_size

				# determine new height and width
				_A					  =			output_height / input_height
				_A					  =			output_width / input_width

				if keep_aspect_ratio:
								# scale as little as possible
								if abs(1 - scale_width   ) < abs(1 - scale_height   ):
												# fit width
												_A					  =			scale_width
								else:
												# fit height
												_A					  =			scale_height

				_A					  =			constraint_to_multiple_of(scale_height * input_height     ,			multiple=__lowercase   )
				_A					  =			constraint_to_multiple_of(scale_width * input_width     ,			multiple=__lowercase   )

				return (new_height, new_width)




class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			['pixel_values']







		def __init__(					self      :       Any    ,   a__      :       bool = True    ,   a__      :       Dict[str, int] = None    ,   a__      :       PILImageResampling = PILImageResampling.BILINEAR    ,   a__      :       bool = False    ,   a__      :       int = 1    ,   a__      :       bool = True    ,   a__      :       Union[int, float] = 1 / 2_55    ,   a__      :       bool = True    ,   a__      :       Optional[Union[float, List[float]]] = None    ,   a__      :       Optional[Union[float, List[float]]] = None    ,   **a__      :       Optional[Any]    ,   )       ->						None:





						'''simple docstring'''







						super().__init__(**a__		)
						_A					  =			size if size is not None else {"height": 3_84, "width": 3_84}
						_A					  =			get_size_dict(a__		)
						_A					  =			do_resize
						_A					  =			size
						_A					  =			keep_aspect_ratio
						_A					  =			ensure_multiple_of
						_A					  =			resample
						_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  a_ (					self      :       int    ,   a__      :       np.ndarray    ,   a__      :       Dict[str, int]    ,   a__      :       bool = False    ,   a__      :       int = 1    ,   a__      :       PILImageResampling = PILImageResampling.BICUBIC    ,   a__      :       Optional[Union[str, ChannelDimension]] = None    ,   **a__      :       List[str]    ,   )       ->						np.ndarray:





						'''simple docstring'''







						_A					  =			get_size_dict(a__		)
						if "height" not in size or "width" not in size:
										raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""		)
						_A					  =			get_resize_output_image_size(
						    a__    ,   output_size=(size["height"], size["width"])    ,   keep_aspect_ratio=a__    ,   multiple=a__    ,   )
						return resize(a__    ,   size=a__    ,   resample=a__    ,   data_format=a__    ,   **a__		)







		def  a_ (					self      :       int    ,   a__      :       np.ndarray    ,   a__      :       Union[int, float]    ,   a__      :       Optional[Union[str, ChannelDimension]] = None    ,   **a__      :       int    ,   )       ->						Any:





						'''simple docstring'''







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







		def  a_ (					self      :       List[Any]    ,   a__      :       np.ndarray    ,   a__      :       Union[float, List[float]]    ,   a__      :       Union[float, List[float]]    ,   a__      :       Optional[Union[str, ChannelDimension]] = None    ,   **a__      :       Dict    ,   )       ->						np.ndarray:





						'''simple docstring'''







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







		def  a_ (					self      :       Dict    ,   a__      :       ImageInput    ,   a__      :       bool = None    ,   a__      :       int = None    ,   a__      :       bool = None    ,   a__      :       int = None    ,   a__      :       PILImageResampling = 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__      :       List[str]    ,   )       ->						PIL.Image.Image:





						'''simple docstring'''







						_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__		)
						_A					  =			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						_A					  =			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						_A					  =			resample if resample is not None else self.resample
						_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 or resample is None:
										raise ValueError("Size and resample must be specified if do_resize 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_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__		)







		def  a_ (					self      :       int    ,   a__      :       str    ,   a__      :       List[Tuple] = None		)       ->						str:





						'''simple docstring'''







						_A					  =			outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
										if len(a__		) != len(a__		):
														raise ValueError(
														    "Make sure that you pass in as many target sizes as the batch dimension of the logits"		)

										if is_torch_tensor(a__		):
														_A					  =			target_sizes.numpy()

										_A					  =			[]

										for idx in range(len(a__		)		):
														_A					  =			torch.nn.functional.interpolate(
														    logits[idx].unsqueeze(dim=0		)    ,   size=target_sizes[idx]    ,   mode="bilinear"    ,   align_corners=a__		)
														_A					  =			resized_logits[0].argmax(dim=0		)
														semantic_segmentation.append(a__		)
						else:
										_A					  =			logits.argmax(dim=1		)
										_A					  =			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

						return semantic_segmentation



"""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 snake_case      (  _UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			StableDiffusionInpaintPipeline
		__UpperCamelCase           =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
		__UpperCamelCase           =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__UpperCamelCase           =			frozenset(
		    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		__UpperCamelCase           =			frozenset([])







		def  a_ (					self      :       Dict		)       ->						Optional[int]:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			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=a__    ,   )
						_A					  =			PNDMScheduler(skip_prk_steps=a__		)
						torch.manual_seed(0		)
						_A					  =			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=1_28    ,   )
						torch.manual_seed(0		)
						_A					  =			CLIPTextConfig(
						    bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=32    ,   intermediate_size=37    ,   layer_norm_eps=1E-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=10_00    ,   hidden_act="gelu"    ,   projection_dim=5_12    ,   )
						_A					  =			CLIPTextModel(a__		)
						_A					  =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

						_A					  =			{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components







		def  a_ (					self      :       Optional[Any]    ,   a__      :       List[str]    ,   a__      :       Tuple=0		)       ->						int:





						'''simple docstring'''







						_A					  =			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(a__		)		).to(a__		)
						_A					  =			image.cpu().permute(0    ,   2    ,   3    ,   1		)[0]
						_A					  =			Image.fromarray(np.uinta(a__		)		).convert("RGB"		).resize((64, 64)		)
						_A					  =			Image.fromarray(np.uinta(image + 4		)		).convert("RGB"		).resize((64, 64)		)
						if str(a__		).startswith("mps"		):
										_A					  =			torch.manual_seed(a__		)
						else:
										_A					  =			torch.Generator(device=a__		).manual_seed(a__		)
						_A					  =			{
						    "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  a_ (					self      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			"cpu"  # ensure determinism for the device-dependent torch.Generator
						_A					  =			self.get_dummy_components()
						_A					  =			StableDiffusionInpaintPipeline(**a__		)
						_A					  =			sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A					  =			self.get_dummy_inputs(a__		)
						_A					  =			sd_pipe(**a__		).images
						_A					  =			image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						_A					  =			np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]		)

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







		def  a_ (					self      :       str		)       ->						Union[str, Any]:





						'''simple docstring'''







						super().test_inference_batch_single_identical(expected_max_diff=3E-3		)





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







		def  a_ (					self      :       List[Any]		)       ->						Any:





						'''simple docstring'''







						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







		def  a_ (					self      :       Union[str, Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"		)
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)
						_A					  =			load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
						    "/yellow_cat_sitting_on_a_park_bench.npy"		)

						_A					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			StableDiffusionInpaintPipeline.from_pretrained(a__    ,   safety_checker=a__		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)
						pipe.enable_attention_slicing()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   output_type="np"    ,   )
						_A					  =			output.images[0]

						assert image.shape == (5_12, 5_12, 3)
						assert np.abs(expected_image - image		).max() < 9E-3







		def  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"		)
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)
						_A					  =			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					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			StableDiffusionInpaintPipeline.from_pretrained(
						    a__    ,   torch_dtype=torch.floataa    ,   safety_checker=a__    ,   )
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)
						pipe.enable_attention_slicing()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   output_type="np"    ,   )
						_A					  =			output.images[0]

						assert image.shape == (5_12, 5_12, 3)
						assert np.abs(expected_image - image		).max() < 5E-1







		def  a_ (					self      :       List[Any]		)       ->						Union[str, Any]:





						'''simple docstring'''







						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"		)
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)

						_A					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			PNDMScheduler.from_pretrained(a__    ,   subfolder="scheduler"		)
						_A					  =			StableDiffusionInpaintPipeline.from_pretrained(
						    a__    ,   safety_checker=a__    ,   scheduler=a__    ,   torch_dtype=torch.floataa    ,   )
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)
						pipe.enable_attention_slicing(1		)
						pipe.enable_sequential_cpu_offload()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   num_inference_steps=2    ,   output_type="np"    ,   )

						_A					  =			torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.6_5 * 10**9