





'''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        _lowerCAmelCase						(       a__				,			unittest.TestCase     ):






     """simple docstring"""






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



     def 			lowerCAmelCase       (					self				:       str		)->     Optional[int]:
        torch.manual_seed(0		)
        snake_case      		=					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					,    )
        snake_case      		=					PNDMScheduler(skip_prk_steps=lowerCamelCase_		)
        torch.manual_seed(0		)
        snake_case      		=					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		)
        snake_case      		=					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					,    )
        snake_case      		=					PaintByExampleImageEncoder(lowerCamelCase_					,    proj_size=32		)
        snake_case      		=					CLIPImageProcessor(crop_size=32					,    size=32		)

        snake_case      		=					{
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """image_encoder""": image_encoder,
            """safety_checker""": None,
            """feature_extractor""": feature_extractor,
        }
        return components



     def 			lowerCAmelCase       (					self				:       str					,    __snake_case				:       Union[str, Any]		)->     List[str]:
        snake_case      		=					np.array(image.convert("""RGB"""		)		)
        snake_case      		=					image[None].transpose(0					,    3					,    1					,    2		)
        snake_case      		=					torch.from_numpy(lowerCamelCase_		).to(dtype=torch.floataa		) / 1_27.5 - 1.0
        return image



     def 			lowerCAmelCase       (					self				:       Optional[int]					,    __snake_case				:       Optional[int]="cpu"					,    __snake_case				:       Optional[Any]=0		)->     Optional[int]:
        # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
        snake_case      		=					floats_tensor((1, 3, 32, 32)					,    rng=random.Random(lowerCamelCase_		)		).to(lowerCamelCase_		)
        snake_case      		=					image.cpu().permute(0					,    2					,    3					,    1		)[0]
        snake_case      		=					Image.fromarray(np.uinta(lowerCamelCase_		)		).convert("""RGB"""		).resize((64, 64)		)
        snake_case      		=					Image.fromarray(np.uinta(image + 4		)		).convert("""RGB"""		).resize((64, 64)		)
        snake_case      		=					Image.fromarray(np.uinta(lowerCamelCase_		)		).convert("""RGB"""		).resize((32, 32)		)

        if str(lowerCamelCase_		).startswith("""mps"""		):
           snake_case      		=					torch.manual_seed(lowerCamelCase_		)
        else:
           snake_case      		=					torch.Generator(device=lowerCamelCase_		).manual_seed(lowerCamelCase_		)
        snake_case      		=					{
            """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 			lowerCAmelCase       (					self				:       Any		)->     List[Any]:
        snake_case      		=					self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        snake_case      		=					PaintByExamplePipeline(**lowerCamelCase_		)
        snake_case      		=					pipe.to("""cpu"""		)
        pipe.set_progress_bar_config(disable=lowerCamelCase_		)

        snake_case      		=					self.get_dummy_inputs()
        snake_case      		=					pipe(**lowerCamelCase_		)
        snake_case      		=					output.images

        snake_case      		=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        snake_case      		=					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 			lowerCAmelCase       (					self				:       List[str]		)->     int:
        snake_case      		=					"""cpu"""
        snake_case      		=					self.get_dummy_inputs()
        inputs.pop("""mask_image"""		)
        snake_case      		=					self.convert_to_pt(inputs.pop("""image"""		)		)
        snake_case      		=					image.clamp(0					,    1		) / 2

        # make sure here that pndm scheduler skips prk
        snake_case      		=					PaintByExamplePipeline(**self.get_dummy_components()		)
        snake_case      		=					pipe.to(lowerCamelCase_		)
        pipe.set_progress_bar_config(disable=lowerCamelCase_		)

        snake_case      		=					pipe(image=lowerCamelCase_					,    mask_image=mask_image[:, 0]					,    **lowerCamelCase_		)
        snake_case      		=					output.images

        snake_case      		=					image.cpu().permute(0					,    2					,    3					,    1		)[0]
        snake_case      		=					mask_image.cpu().permute(0					,    2					,    3					,    1		)[0]

        snake_case      		=					Image.fromarray(np.uinta(lowerCamelCase_		)		).convert("""RGB"""		)
        snake_case      		=					Image.fromarray(np.uinta(lowerCamelCase_		)		).convert("""RGB"""		)

        snake_case      		=					pipe(**self.get_dummy_inputs()		)
        snake_case      		=					output.images

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



     def 			lowerCAmelCase       (					self				:       List[str]		)->     List[Any]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3		)




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






     """simple docstring"""






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



     def 			lowerCAmelCase       (					self				:       Dict		)->     Tuple:
        # make sure here that pndm scheduler skips prk
        snake_case      		=					load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/paint_by_example/dog_in_bucket.png"""		)
        snake_case      		=					load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/paint_by_example/mask.png"""		)
        snake_case      		=					load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/paint_by_example/panda.jpg"""		)

        snake_case      		=					PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""		)
        snake_case      		=					pipe.to(lowerCamelCase_		)
        pipe.set_progress_bar_config(disable=lowerCamelCase_		)

        snake_case      		=					torch.manual_seed(3_21		)
        snake_case      		=					pipe(
            image=lowerCamelCase_					,    mask_image=lowerCamelCase_					,    example_image=lowerCamelCase_					,    generator=lowerCamelCase_					,    guidance_scale=5.0					,    num_inference_steps=50					,    output_type="""np"""					,    )

        snake_case      		=					output.images

        snake_case      		=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case      		=					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'''






import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
		from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import PoolFormerImageProcessor




class 		a__				(							a__  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	self.config_class(**self.inputs_dict   )
														self.parent.assertTrue(hasattr(lowerCamelCase_					,    '''hidden_sizes'''   )   )
														self.parent.assertTrue(hasattr(lowerCamelCase_					,    '''num_encoder_blocks'''   )   )




class 		a__				:





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_=13					,    lowerCamelCase_=64					,    lowerCamelCase_=3					,    lowerCamelCase_=4					,    lowerCamelCase_=[2, 2, 2, 2]					,    lowerCamelCase_=[8, 4, 2, 1]					,    lowerCamelCase_=[16, 32, 64, 1_28]					,    lowerCamelCase_=[1, 4, 8, 16]					,    lowerCamelCase_=False					,    lowerCamelCase_=True					,    lowerCamelCase_="gelu"					,    lowerCamelCase_=0.1					,    lowerCamelCase_=0.02					,    lowerCamelCase_=3					,    lowerCamelCase_=None					,    )       ->							Any:
														lowerCAmelCase__          =	parent
														lowerCAmelCase__          =	batch_size
														lowerCAmelCase__          =	image_size
														lowerCAmelCase__          =	num_channels
														lowerCAmelCase__          =	num_encoder_blocks
														lowerCAmelCase__          =	sr_ratios
														lowerCAmelCase__          =	depths
														lowerCAmelCase__          =	hidden_sizes
														lowerCAmelCase__          =	downsampling_rates
														lowerCAmelCase__          =	is_training
														lowerCAmelCase__          =	use_labels
														lowerCAmelCase__          =	hidden_act
														lowerCAmelCase__          =	hidden_dropout_prob
														lowerCAmelCase__          =	initializer_range
														lowerCAmelCase__          =	num_labels
														lowerCAmelCase__          =	scope







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														lowerCAmelCase__          =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

														lowerCAmelCase__          =	None
														if self.use_labels:
																					lowerCAmelCase__          =	ids_tensor([self.batch_size, self.image_size, self.image_size]					,    self.num_labels   )

														lowerCAmelCase__          =	PoolFormerConfig(
														    image_size=self.image_size					,    num_channels=self.num_channels					,    num_encoder_blocks=self.num_encoder_blocks					,    depths=self.depths					,    hidden_sizes=self.hidden_sizes					,    hidden_act=self.hidden_act					,    hidden_dropout_prob=self.hidden_dropout_prob					,    initializer_range=self.initializer_range					,    )

														return config, pixel_values, labels







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Any:
														lowerCAmelCase__          =	PoolFormerModel(config=lowerCamelCase_   )
														model.to(lowerCamelCase_   )
														model.eval()
														lowerCAmelCase__          =	model(lowerCamelCase_   )
														lowerCAmelCase__          =	lowerCAmelCase__          =	self.image_size // 32.0
														self.parent.assertEqual(
														    result.last_hidden_state.shape					,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														lowerCAmelCase__          =	self.prepare_config_and_inputs()
														lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	config_and_inputs
														lowerCAmelCase__          =	{'''pixel_values''': pixel_values}
														return config, inputs_dict




@require_torch
class 		a__				(							a__				,				a__				,				unittest.TestCase  ):





							'''simple docstring'''






							lowercase__			:   Dict							=      (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
							lowercase__			:   Any							=      (
							    {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__			:   Optional[int]							=      False
							lowercase__			:   Optional[int]							=      False
							lowercase__			:   Union[str, Any]							=      False
							lowercase__			:   List[Any]							=      False
							lowercase__			:   Tuple							=      False







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	PoolFormerModelTester(self   )
														lowerCAmelCase__          =	PoolFormerConfigTester(self					,    config_class=lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														self.config_tester.run_common_tests()







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_   )







							@unittest.skip('''PoolFormer does not use inputs_embeds'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														pass







							@unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														def check_hidden_states_output(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   ):
																					lowerCAmelCase__          =	model_class(lowerCamelCase_   )
																					model.to(lowerCamelCase_   )
																					model.eval()

																					with torch.no_grad():
																												lowerCAmelCase__          =	model(**self._prepare_for_class(lowerCamelCase_					,    lowerCamelCase_   )   )

																					lowerCAmelCase__          =	outputs.hidden_states

																					lowerCAmelCase__          =	self.model_tester.num_encoder_blocks
																					self.assertEqual(len(lowerCamelCase_   )					,    lowerCamelCase_   )

																					# verify the first hidden states (first block)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-3:]   )					,    [
																					        self.model_tester.hidden_sizes[0],
																					        self.model_tester.image_size // 4,
																					        self.model_tester.image_size // 4,
																					    ]					,    )

														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__          =	True
																					check_hidden_states_output(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCAmelCase__          =	True

																					check_hidden_states_output(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														if not self.model_tester.is_training:
																					return

														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs_for_common()
														lowerCAmelCase__          =	True

														for model_class in self.all_model_classes:
																					if model_class in get_values(lowerCamelCase_   ):
																												continue
																					lowerCAmelCase__          =	model_class(lowerCamelCase_   )
																					model.to(lowerCamelCase_   )
																					model.train()
																					lowerCAmelCase__          =	self._prepare_for_class(lowerCamelCase_					,    lowerCamelCase_					,    return_labels=lowerCamelCase_   )
																					lowerCAmelCase__          =	model(**lowerCamelCase_   ).loss
																					loss.backward()







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__          =	model_class(lowerCamelCase_   )
																					lowerCAmelCase__          =	inspect.signature(model.forward   )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCAmelCase__          =	[*signature.parameters.keys()]

																					lowerCAmelCase__          =	['''pixel_values''']
																					self.assertListEqual(arg_names[:1]					,    lowerCamelCase_   )







							@slow
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__          =	PoolFormerModel.from_pretrained(lowerCamelCase_   )
																					self.assertIsNotNone(lowerCamelCase_   )


def 					_snake_case   (     )						->    Optional[int]:
							lowerCAmelCase__          =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
							return image






@require_torch
class 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							@slow
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														lowerCAmelCase__          =	PoolFormerImageProcessor()
														lowerCAmelCase__          =	PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12'''   ).to(lowerCamelCase_   )

														lowerCAmelCase__          =	image_processor(images=prepare_img()					,    return_tensors='''pt'''   ).to(lowerCamelCase_   )

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase__          =	model(**lowerCamelCase_   )

														# verify the logits
														lowerCAmelCase__          =	torch.Size((1, 10_00)   )
														self.assertEqual(outputs.logits.shape					,    lowerCamelCase_   )

														lowerCAmelCase__          =	torch.tensor([-0.6_113, 0.1_685, -0.0_492]   ).to(lowerCamelCase_   )
														self.assertTrue(torch.allclose(outputs.logits[0, :3]					,    lowerCamelCase_					,    atol=1e-4   )   )