
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Optional[int]	=		"swinv2"

						UpperCamelCase_							: Union[str, Any]	=		{
						    "num_attention_heads": "num_heads",
						    "num_hidden_layers": "num_layers",
						}

						def __init__(			self       ,							a=2_24       ,							a=4       ,							a=3       ,							a=96       ,							a=[2, 2, 6, 2]       ,							a=[3, 6, 12, 24]       ,							a=7       ,							a=4.0       ,							a=True       ,							a=0.0       ,							a=0.0       ,							a=0.1       ,							a="gelu"       ,							a=False       ,							a=0.02       ,							a=1e-5       ,							a=32       ,							**a       ,							)					->      Any:


								'''simple docstring'''

								super().__init__(**a				)

								_UpperCamelCase						    =	image_size
								_UpperCamelCase						    =	patch_size
								_UpperCamelCase						    =	num_channels
								_UpperCamelCase						    =	embed_dim
								_UpperCamelCase						    =	depths
								_UpperCamelCase						    =	len(a				)
								_UpperCamelCase						    =	num_heads
								_UpperCamelCase						    =	window_size
								_UpperCamelCase						    =	mlp_ratio
								_UpperCamelCase						    =	qkv_bias
								_UpperCamelCase						    =	hidden_dropout_prob
								_UpperCamelCase						    =	attention_probs_dropout_prob
								_UpperCamelCase						    =	drop_path_rate
								_UpperCamelCase						    =	hidden_act
								_UpperCamelCase						    =	use_absolute_embeddings
								_UpperCamelCase						    =	layer_norm_eps
								_UpperCamelCase						    =	initializer_range
								_UpperCamelCase						    =	encoder_stride
								# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								_UpperCamelCase						    =	int(embed_dim * 2 ** (len(a				) - 1)				)
								_UpperCamelCase						    =	(0, 0, 0, 0)





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        lowerCAmelCase__      (   __lowercase		,	__lowercase		,	__lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: List[Any]	=		StableDiffusionInpaintPipeline
						UpperCamelCase_							: int	=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						UpperCamelCase_							: Union[str, Any]	=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						UpperCamelCase_							: Tuple	=		frozenset(
						    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						UpperCamelCase_							: int	=		frozenset([]	)

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


								'''simple docstring'''

								torch.manual_seed(0				)
								_UpperCamelCase						    =	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       ,							)
								_UpperCamelCase						    =	PNDMScheduler(skip_prk_steps=a				)
								torch.manual_seed(0				)
								_UpperCamelCase						    =	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				)
								_UpperCamelCase						    =	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=10_00       ,							hidden_act="""gelu"""       ,							projection_dim=5_12       ,							)
								_UpperCamelCase						    =	CLIPTextModel(a				)
								_UpperCamelCase						    =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

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

						def  A_				(			self       ,							a       ,							a=0				)					->      List[Any]:


								'''simple docstring'''

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


								'''simple docstring'''

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

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

								assert image.shape == (1, 64, 64, 3)
								_UpperCamelCase						    =	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]				)

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







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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







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

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


								'''simple docstring'''

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

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

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

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

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

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

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

						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png"""				)
								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)
								_UpperCamelCase						    =	load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""				)

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	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()

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

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

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







						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

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

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

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	PNDMScheduler.from_pretrained(a       ,							subfolder="""scheduler"""				)
								_UpperCamelCase						    =	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()

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

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

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



