

"""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			(		__a    ,     unittest.TestCase):
			__a :       int									=							PaintByExamplePipeline
			__a :       str									=							IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
			__a :       Optional[int]									=							IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__a :       Optional[int]									=							frozenset([])  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				torch.manual_seed(0     )
				_UpperCAmelCase    :							Union[str, Any]									=					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      ,		)
				_UpperCAmelCase    :							Tuple									=					PNDMScheduler(skip_prk_steps=_A     )
				torch.manual_seed(0     )
				_UpperCAmelCase    :							Union[str, Any]									=					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     )
				_UpperCAmelCase    :							Optional[Any]									=					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      ,		)
				_UpperCAmelCase    :							Union[str, Any]									=					PaintByExampleImageEncoder(_A      ,		proj_size=32     )
				_UpperCAmelCase    :							Dict									=					CLIPImageProcessor(crop_size=32      ,		size=32     )

				_UpperCAmelCase    :							Tuple									=					{
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """vae""": vae,
				    """image_encoder""": image_encoder,
				    """safety_checker""": None,
				    """feature_extractor""": feature_extractor,
				}
				return components
			def 							__snake_case (		self      ,		_A     )    ->    int:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					np.array(image.convert("""RGB"""     )     )
				_UpperCAmelCase    :							Optional[Any]									=					image[None].transpose(0      ,		3      ,		1      ,		2     )
				_UpperCAmelCase    :							Tuple									=					torch.from_numpy(_A     ).to(dtype=torch.floataa     ) / 127.5 - 1.0
				return image
			def 							__snake_case (		self      ,		_A="cpu"      ,		_A=0     )    ->    Dict:






				'''simple docstring'''







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

				if str(_A     ).startswith("""mps"""     ):
					_UpperCAmelCase    :							List[str]									=					torch.manual_seed(_A     )
				else:
					_UpperCAmelCase    :							Tuple									=					torch.Generator(device=_A     ).manual_seed(_A     )
				_UpperCAmelCase    :							List[str]									=					{
				    """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 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				_UpperCAmelCase    :							Tuple									=					self.get_dummy_components()

				# make sure here that pndm scheduler skips prk
				_UpperCAmelCase    :							List[Any]									=					PaintByExamplePipeline(**_A     )
				_UpperCAmelCase    :							Union[str, Any]									=					pipe.to("""cpu"""     )
				pipe.set_progress_bar_config(disable=_A     )

				_UpperCAmelCase    :							Any									=					self.get_dummy_inputs()
				_UpperCAmelCase    :							List[Any]									=					pipe(**_A     )
				_UpperCAmelCase    :							Tuple									=					output.images

				_UpperCAmelCase    :							Union[str, Any]									=					image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				_UpperCAmelCase    :							str									=					np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				_UpperCAmelCase    :							str									=					"""cpu"""
				_UpperCAmelCase    :							Union[str, Any]									=					self.get_dummy_inputs()
				inputs.pop("""mask_image"""     )
				_UpperCAmelCase    :							Union[str, Any]									=					self.convert_to_pt(inputs.pop("""image"""     )     )
				_UpperCAmelCase    :							Optional[Any]									=					image.clamp(0      ,		1     ) / 2

				# make sure here that pndm scheduler skips prk
				_UpperCAmelCase    :							Tuple									=					PaintByExamplePipeline(**self.get_dummy_components()     )
				_UpperCAmelCase    :							List[Any]									=					pipe.to(_A     )
				pipe.set_progress_bar_config(disable=_A     )

				_UpperCAmelCase    :							List[str]									=					pipe(image=_A      ,		mask_image=mask_image[:, 0]      ,		**_A     )
				_UpperCAmelCase    :							Any									=					output.images

				_UpperCAmelCase    :							str									=					image.cpu().permute(0      ,		2      ,		3      ,		1     )[0]
				_UpperCAmelCase    :							Optional[Any]									=					mask_image.cpu().permute(0      ,		2      ,		3      ,		1     )[0]

				_UpperCAmelCase    :							Tuple									=					Image.fromarray(np.uinta(_A     )     ).convert("""RGB"""     )
				_UpperCAmelCase    :							str									=					Image.fromarray(np.uinta(_A     )     ).convert("""RGB"""     )

				_UpperCAmelCase    :							Dict									=					pipe(**self.get_dummy_inputs()     )
				_UpperCAmelCase    :							Union[str, Any]									=					output.images

				assert out_a.shape == (1, 64, 64, 3)
				assert np.abs(out_a.flatten() - out_a.flatten()     ).max() < 5e-2
			def 							__snake_case (		self     )    ->    Dict:






				'''simple docstring'''







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






@slow
@require_torch_gpu
class     _UpperCAmelCase			(		unittest.TestCase):
			def 							__snake_case (		self     )    ->    Any:






				'''simple docstring'''







				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







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

				_UpperCAmelCase    :							Dict									=					PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""     )
				_UpperCAmelCase    :							Optional[Any]									=					pipe.to(_A     )
				pipe.set_progress_bar_config(disable=_A     )

				_UpperCAmelCase    :							Any									=					torch.manual_seed(3_21     )
				_UpperCAmelCase    :							Dict									=					pipe(
				    image=_A      ,		mask_image=_A      ,		example_image=_A      ,		generator=_A      ,		guidance_scale=5.0      ,		num_inference_steps=50      ,		output_type="""np"""      ,		)

				_UpperCAmelCase    :							List[Any]									=					output.images

				_UpperCAmelCase    :							Dict									=					image[0, -3:, -3:, -1]

				assert image.shape == (1, 5_12, 5_12, 3)
				_UpperCAmelCase    :							Union[str, Any]									=					np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]     )

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








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__				:  Union[str, Any]													=  {
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  List[Any]													=  [
						    '''DPRContextEncoderTokenizerFast''',
						    '''DPRQuestionEncoderTokenizerFast''',
						    '''DPRReaderTokenizerFast''',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Dict													=  [
						    '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DPRContextEncoder''',
						    '''DPRPretrainedContextEncoder''',
						    '''DPRPreTrainedModel''',
						    '''DPRPretrainedQuestionEncoder''',
						    '''DPRPretrainedReader''',
						    '''DPRQuestionEncoder''',
						    '''DPRReader''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Any													=  [
						    '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFDPRContextEncoder''',
						    '''TFDPRPretrainedContextEncoder''',
						    '''TFDPRPretrainedQuestionEncoder''',
						    '''TFDPRPretrainedReader''',
						    '''TFDPRQuestionEncoder''',
						    '''TFDPRReader''',
						]


if TYPE_CHECKING:
						from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
						from .tokenization_dpr import (
						    DPRContextEncoderTokenizer,
						    DPRQuestionEncoderTokenizer,
						    DPRReaderOutput,
						    DPRReaderTokenizer,
						)

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_dpr_fast import (
												    DPRContextEncoderTokenizerFast,
												    DPRQuestionEncoderTokenizerFast,
												    DPRReaderTokenizerFast,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_dpr import (
												    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPRContextEncoder,
												    DPRPretrainedContextEncoder,
												    DPRPreTrainedModel,
												    DPRPretrainedQuestionEncoder,
												    DPRPretrainedReader,
												    DPRQuestionEncoder,
												    DPRReader,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_dpr import (
												    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDPRContextEncoder,
												    TFDPRPretrainedContextEncoder,
												    TFDPRPretrainedQuestionEncoder,
												    TFDPRPretrainedReader,
												    TFDPRQuestionEncoder,
												    TFDPRReader,
												)

else:
						import sys

						lowerCamelCase__				:  Optional[Any]													=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





