



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        __snake_case		(  UpperCamelCase_		,unittest.TestCase		):
							_a       =				PaintByExamplePipeline
							_a       =				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
							_a       =				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							_a       =				frozenset([]		)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									torch.manual_seed(0)
									lowerCAmelCase_    :  Union[str, Any]             =				UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)       ,       layers_per_block=2       ,       sample_size=3_2       ,       in_channels=9       ,       out_channels=4       ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,       cross_attention_dim=3_2       ,       )
									lowerCAmelCase_    :  List[Any]             =				PNDMScheduler(skip_prk_steps=A_)
									torch.manual_seed(0)
									lowerCAmelCase_    :  List[Any]             =				AutoencoderKL(
									    block_out_channels=[3_2, 6_4]       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,       latent_channels=4       ,       )
									torch.manual_seed(0)
									lowerCAmelCase_    :  Dict             =				CLIPVisionConfig(
									    hidden_size=3_2       ,       projection_dim=3_2       ,       intermediate_size=3_7       ,       layer_norm_eps=1e-05       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       image_size=3_2       ,       patch_size=4       ,       )
									lowerCAmelCase_    :  Optional[Any]             =				PaintByExampleImageEncoder(A_       ,       proj_size=3_2)
									lowerCAmelCase_    :  Tuple             =				CLIPImageProcessor(crop_size=3_2       ,       size=3_2)

									lowerCAmelCase_    :  List[str]             =				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''image_encoder''': image_encoder,
									    '''safety_checker''': None,
									    '''feature_extractor''': feature_extractor,
									}
									return components




							def      UpperCAmelCase__    ( self  : Union[str, Any]       ,       A_  : Union[str, Any]):
									lowerCAmelCase_    :  Tuple             =				np.array(image.convert('''RGB'''))
									lowerCAmelCase_    :  Tuple             =				image[None].transpose(0       ,       3       ,       1       ,       2)
									lowerCAmelCase_    :  Dict             =				torch.from_numpy(A_).to(dtype=torch.floataa) / 127.5 - 1.0
									return image




							def      UpperCAmelCase__    ( self  : Dict       ,       A_  : Union[str, Any]="cpu"       ,       A_  : Union[str, Any]=0):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									lowerCAmelCase_    :  Optional[int]             =				floats_tensor((1, 3, 3_2, 3_2)       ,       rng=random.Random(A_)).to(A_)
									lowerCAmelCase_    :  List[Any]             =				image.cpu().permute(0       ,       2       ,       3       ,       1)[0]
									lowerCAmelCase_    :  Optional[Any]             =				Image.fromarray(np.uinta(A_)).convert('''RGB''').resize((6_4, 6_4))
									lowerCAmelCase_    :  Dict             =				Image.fromarray(np.uinta(image + 4)).convert('''RGB''').resize((6_4, 6_4))
									lowerCAmelCase_    :  Any             =				Image.fromarray(np.uinta(A_)).convert('''RGB''').resize((3_2, 3_2))

									if str(A_).startswith('''mps'''):
											lowerCAmelCase_    :  Optional[Any]             =				torch.manual_seed(A_)
									else:
											lowerCAmelCase_    :  str             =				torch.Generator(device=A_).manual_seed(A_)
									lowerCAmelCase_    :  Optional[Any]             =				{
									    '''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      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  Optional[Any]             =				self.get_dummy_components()

									# make sure here that pndm scheduler skips prk
									lowerCAmelCase_    :  Any             =				PaintByExamplePipeline(**A_)
									lowerCAmelCase_    :  Optional[int]             =				pipe.to('''cpu''')
									pipe.set_progress_bar_config(disable=A_)

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

									lowerCAmelCase_    :  Tuple             =				image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)
									lowerCAmelCase_    :  Tuple             =				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      UpperCAmelCase__    ( self  : Optional[Any]):
									lowerCAmelCase_    :  Optional[Any]             =				'''cpu'''
									lowerCAmelCase_    :  str             =				self.get_dummy_inputs()
									inputs.pop('''mask_image''')
									lowerCAmelCase_    :  int             =				self.convert_to_pt(inputs.pop('''image'''))
									lowerCAmelCase_    :  Optional[int]             =				image.clamp(0       ,       1) / 2

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

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

									lowerCAmelCase_    :  Dict             =				image.cpu().permute(0       ,       2       ,       3       ,       1)[0]
									lowerCAmelCase_    :  List[str]             =				mask_image.cpu().permute(0       ,       2       ,       3       ,       1)[0]

									lowerCAmelCase_    :  List[Any]             =				Image.fromarray(np.uinta(A_)).convert('''RGB''')
									lowerCAmelCase_    :  Dict             =				Image.fromarray(np.uinta(A_)).convert('''RGB''')

									lowerCAmelCase_    :  List[str]             =				pipe(**self.get_dummy_inputs())
									lowerCAmelCase_    :  Optional[Any]             =				output.images

									assert out_a.shape == (1, 6_4, 6_4, 3)
									assert np.abs(out_a.flatten() - out_a.flatten()).max() < 5e-2







							def      UpperCAmelCase__    ( self  : Dict):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3)





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




							def      UpperCAmelCase__    ( self  : str):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def      UpperCAmelCase__    ( self  : Optional[Any]):
									# make sure here that pndm scheduler skips prk
									lowerCAmelCase_    :  Tuple             =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/paint_by_example/dog_in_bucket.png''')
									lowerCAmelCase_    :  List[Any]             =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/paint_by_example/mask.png''')
									lowerCAmelCase_    :  str             =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/paint_by_example/panda.jpg''')

									lowerCAmelCase_    :  str             =				PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example''')
									lowerCAmelCase_    :  Dict             =				pipe.to(A_)
									pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_    :  Union[str, Any]             =				torch.manual_seed(3_2_1)
									lowerCAmelCase_    :  Tuple             =				pipe(
									    image=A_       ,       mask_image=A_       ,       example_image=A_       ,       generator=A_       ,       guidance_scale=5.0       ,       num_inference_steps=5_0       ,       output_type='''np'''       ,       )

									lowerCAmelCase_    :  Tuple             =				output.images

									lowerCAmelCase_    :  List[str]             =				image[0, -3:, -3:, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									lowerCAmelCase_    :  int             =				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










import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


A__						:						str          =      logging.get_logger(__name__)



class        __snake_case		(  UpperCamelCase_		):




							def __init__( self  : Union[str, Any]       ,       *A_  : int       ,       **A_  : List[str]):
									warnings.warn(
									    '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
									    ''' Please use DetrImageProcessor instead.'''       ,       A_       ,       )
									super().__init__(*A_       ,       **A_)





