




'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class     UpperCamelCase_     (SCREAMING_SNAKE_CASE__,      unittest.TestCase     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					PaintByExamplePipeline
						_lowerCAmelCase    						=					IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
						_lowerCAmelCase    						=					IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						_lowerCAmelCase    						=					frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







						def       _a    (     self      :       Any    ):





											"""simple docstring"""







											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							, )
											A_ :     int =							PNDMScheduler(skip_prk_steps=_lowerCamelCase    )
											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							, )
											torch.manual_seed(0    )
											A_ :     List[str] =							CLIPVisionConfig(
											    hidden_size=32							, projection_dim=32							, intermediate_size=37							, layer_norm_eps=1E-05							, num_attention_heads=4							, num_hidden_layers=5							, image_size=32							, patch_size=4							, )
											A_ :     Tuple =							PaintByExampleImageEncoder(_lowerCamelCase							, proj_size=32    )
											A_ :     str =							CLIPImageProcessor(crop_size=32							, size=32    )

											A_ :     Optional[Any] =							{
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''image_encoder''': image_encoder,
											    '''safety_checker''': None,
											    '''feature_extractor''': feature_extractor,
											}
											return components







						def       _a    (     self      :       Tuple							, _lowerCamelCase      :       List[Any]    ):





											"""simple docstring"""







											A_ :     int =							np.array(image.convert('''RGB'''    )    )
											A_ :     Optional[int] =							image[None].transpose(0							, 3							, 1							, 2    )
											A_ :     Union[str, Any] =							torch.from_numpy(_lowerCamelCase    ).to(dtype=torch.floataa    ) / 127.5 - 1.0
											return image







						def       _a    (     self      :       Any							, _lowerCamelCase      :       Union[str, Any]="cpu"							, _lowerCamelCase      :       Optional[Any]=0    ):





											"""simple docstring"""







											A_ :     Optional[int] =							floats_tensor((1, 3, 32, 32)							, rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
											A_ :     Optional[Any] =							image.cpu().permute(0							, 2							, 3							, 1    )[0]
											A_ :     Optional[Any] =							Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert('''RGB'''    ).resize((64, 64)    )
											A_ :     Optional[Any] =							Image.fromarray(np.uinta(image + 4    )    ).convert('''RGB'''    ).resize((64, 64)    )
											A_ :     Optional[int] =							Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert('''RGB'''    ).resize((32, 32)    )

											if str(_lowerCamelCase    ).startswith('''mps'''    ):
																A_ :     List[Any] =							torch.manual_seed(_lowerCamelCase    )
											else:
																A_ :     List[Any] =							torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
											A_ :     Optional[int] =							{
											    '''example_image''': example_image,
											    '''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"""







											A_ :     Optional[Any] =							self.get_dummy_components()

											# make sure here that pndm scheduler skips prk
											A_ :     Any =							PaintByExamplePipeline(**_lowerCamelCase    )
											A_ :     str =							pipe.to('''cpu'''    )
											pipe.set_progress_bar_config(disable=_lowerCamelCase    )

											A_ :     Any =							self.get_dummy_inputs()
											A_ :     str =							pipe(**_lowerCamelCase    )
											A_ :     int =							output.images

											A_ :     int =							image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											A_ :     int =							np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]    )

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







						def       _a    (     self      :       Tuple    ):





											"""simple docstring"""







											A_ :     List[str] =							'''cpu'''
											A_ :     Optional[int] =							self.get_dummy_inputs()
											inputs.pop('''mask_image'''    )
											A_ :     Any =							self.convert_to_pt(inputs.pop('''image'''    )    )
											A_ :     int =							image.clamp(0							, 1    ) / 2

											# make sure here that pndm scheduler skips prk
											A_ :     str =							PaintByExamplePipeline(**self.get_dummy_components()    )
											A_ :     Any =							pipe.to(_lowerCamelCase    )
											pipe.set_progress_bar_config(disable=_lowerCamelCase    )

											A_ :     List[Any] =							pipe(image=_lowerCamelCase							, mask_image=mask_image[:, 0]							, **_lowerCamelCase    )
											A_ :     Any =							output.images

											A_ :     List[Any] =							image.cpu().permute(0							, 2							, 3							, 1    )[0]
											A_ :     Tuple =							mask_image.cpu().permute(0							, 2							, 3							, 1    )[0]

											A_ :     Optional[int] =							Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert('''RGB'''    )
											A_ :     Any =							Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert('''RGB'''    )

											A_ :     int =							pipe(**self.get_dummy_inputs()    )
											A_ :     List[str] =							output.images

											assert out_a.shape == (1, 64, 64, 3)
											assert np.abs(out_a.flatten() - out_a.flatten()    ).max() < 5E-2







						def       _a    (     self      :       Union[str, Any]    ):





											"""simple docstring"""







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






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







						"""simple docstring"""


						def       _a    (     self      :       Union[str, Any]    ):





											"""simple docstring"""







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







						def       _a    (     self      :       Tuple    ):





											"""simple docstring"""







											A_ :     List[str] =							load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/paint_by_example/dog_in_bucket.png'''    )
											A_ :     Optional[Any] =							load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/paint_by_example/mask.png'''    )
											A_ :     Any =							load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/paint_by_example/panda.jpg'''    )

											A_ :     Optional[Any] =							PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''    )
											A_ :     Optional[int] =							pipe.to(_lowerCamelCase    )
											pipe.set_progress_bar_config(disable=_lowerCamelCase    )

											A_ :     Tuple =							torch.manual_seed(321    )
											A_ :     Optional[int] =							pipe(
											    image=_lowerCamelCase							, mask_image=_lowerCamelCase							, example_image=_lowerCamelCase							, generator=_lowerCamelCase							, guidance_scale=5.0							, num_inference_steps=50							, output_type='''np'''							, )

											A_ :     int =							output.images

											A_ :     str =							image[0, -3:, -3:, -1]

											assert image.shape == (1, 512, 512, 3)
											A_ :     int =							np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]    )

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









'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__          =						{"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__          =						[
							    """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NatForImageClassification""",
							    """NatModel""",
							    """NatPreTrainedModel""",
							    """NatBackbone""",
							]

if TYPE_CHECKING:
							from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nat import (
														    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NatBackbone,
														    NatForImageClassification,
														    NatModel,
														    NatPreTrainedModel,
														)

else:
							import sys

							snake_case__          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



