
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	["image_processor", "tokenizer"]
		_lowerCAmelCase		=	"AutoImageProcessor"
		_lowerCAmelCase		=	"AutoTokenizer"





		def __init__(							self    :	str						,  lowerCamelCase__    :	int=None						,  lowerCamelCase__    :	List[Any]=None						,  **lowerCamelCase__    :	str				):
							lowerCAmelCase		:					Dict  	=     None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''						,  lowerCamelCase__						,  )
												lowerCAmelCase		:					List[Any]  	=     kwargs.pop('''feature_extractor'''				)

							lowerCAmelCase		:					Tuple  	=     image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError('''You have to specify an image_processor.'''				)
							if tokenizer is None:
												raise ValueError('''You have to specify a tokenizer.'''				)

							super().__init__(lowerCamelCase__						,  lowerCamelCase__				)
							lowerCAmelCase		:					List[str]  	=     self.image_processor





		def __call__(							self    :	Any						,  lowerCamelCase__    :	List[Any]=None						,  lowerCamelCase__    :	str=None						,  lowerCamelCase__    :	List[str]=None						,  **lowerCamelCase__    :	Optional[int]				):

							if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

							if text is not None:
												lowerCAmelCase		:					Union[str, Any]  	=     self.tokenizer(lowerCamelCase__						,  return_tensors=lowerCamelCase__						,  **lowerCamelCase__				)

							if images is not None:
												lowerCAmelCase		:					int  	=     self.image_processor(lowerCamelCase__						,  return_tensors=lowerCamelCase__						,  **lowerCamelCase__				)

							if text is not None and images is not None:
												lowerCAmelCase		:					List[Any]  	=     image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							else:
												return BatchEncoding(data=dict(**lowerCamelCase__				)						,  tensor_type=lowerCamelCase__				)





		def       _A							(							self    :	int						,  *lowerCamelCase__    :	Tuple						,  **lowerCamelCase__    :	Union[str, Any]				):
							return self.tokenizer.batch_decode(*lowerCamelCase__						,  **lowerCamelCase__				)





		def       _A							(							self    :	int						,  *lowerCamelCase__    :	Any						,  **lowerCamelCase__    :	str				):
							return self.tokenizer.decode(*lowerCamelCase__						,  **lowerCamelCase__				)





		@property
		def       _A							(							self    :	Optional[Any]				):
							lowerCAmelCase		:					Optional[int]  	=     self.tokenizer.model_input_names
							lowerCAmelCase		:					List[str]  	=     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)





		@property
		def       _A							(							self    :	Union[str, Any]				):
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''						,  lowerCamelCase__						,  )
							return self.image_processor_class





		@property
		def       _A							(							self    :	Union[str, Any]				):
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''						,  lowerCamelCase__						,  )
							return self.image_processor


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 		__magic_name__   (						snake_case,				unittest.TestCase    ):
		_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    :	Dict				):
							torch.manual_seed(0				)
							lowerCAmelCase		:					Optional[int]  	=     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[str]  	=     PNDMScheduler(skip_prk_steps=lowerCamelCase__				)
							torch.manual_seed(0				)
							lowerCAmelCase		:					List[str]  	=     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		:					Union[str, Any]  	=     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		:					List[Any]  	=     PaintByExampleImageEncoder(lowerCamelCase__						,  proj_size=3_2				)
							lowerCAmelCase		:					Union[str, Any]  	=     CLIPImageProcessor(crop_size=3_2						,  size=3_2				)

							lowerCAmelCase		:					str  	=     {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''image_encoder''': image_encoder,
							    '''safety_checker''': None,
							    '''feature_extractor''': feature_extractor,
							}
							return components





		def       _A							(							self    :	str						,  lowerCamelCase__    :	List[Any]				):
							lowerCAmelCase		:					List[Any]  	=     np.array(image.convert('''RGB'''				)				)
							lowerCAmelCase		:					int  	=     image[None].transpose(0						,  3						,  1						,  2				)
							lowerCAmelCase		:					Any  	=     torch.from_numpy(lowerCamelCase__				).to(dtype=torch.floataa				) / 1_2_7.5 - 1.0
							return image





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

							if str(lowerCamelCase__				).startswith('''mps'''				):
												lowerCAmelCase		:					Dict  	=     torch.manual_seed(lowerCamelCase__				)
							else:
												lowerCAmelCase		:					str  	=     torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
							lowerCAmelCase		:					Union[str, 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       _A							(							self    :	List[Any]				):
							lowerCAmelCase		:					Tuple  	=     self.get_dummy_components()

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

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

							lowerCAmelCase		:					Optional[int]  	=     image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							lowerCAmelCase		:					List[Any]  	=     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]				)

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





		def       _A							(							self    :	List[str]				):
							lowerCAmelCase		:					List[str]  	=     '''cpu'''
							lowerCAmelCase		:					Union[str, Any]  	=     self.get_dummy_inputs()
							inputs.pop('''mask_image'''				)
							lowerCAmelCase		:					Union[str, Any]  	=     self.convert_to_pt(inputs.pop('''image'''				)				)
							lowerCAmelCase		:					Dict  	=     image.clamp(0						,  1				) / 2

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					str  	=     PaintByExamplePipeline(**self.get_dummy_components()				)
							lowerCAmelCase		:					Tuple  	=     pipe.to(lowerCamelCase__				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					Optional[int]  	=     pipe(image=lowerCamelCase__						,  mask_image=mask_image[:, 0]						,  **lowerCamelCase__				)
							lowerCAmelCase		:					Optional[int]  	=     output.images

							lowerCAmelCase		:					int  	=     image.cpu().permute(0						,  2						,  3						,  1				)[0]
							lowerCAmelCase		:					int  	=     mask_image.cpu().permute(0						,  2						,  3						,  1				)[0]

							lowerCAmelCase		:					List[str]  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				)
							lowerCAmelCase		:					str  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				)

							lowerCAmelCase		:					List[Any]  	=     pipe(**self.get_dummy_inputs()				)
							lowerCAmelCase		:					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       _A							(							self    :	Optional[Any]				):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3				)



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





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





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

							lowerCAmelCase		:					Union[str, Any]  	=     PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''				)
							lowerCAmelCase		:					str  	=     pipe.to(lowerCamelCase__				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					int  	=     torch.manual_seed(3_2_1				)
							lowerCAmelCase		:					List[str]  	=     pipe(
							    image=lowerCamelCase__						,  mask_image=lowerCamelCase__						,  example_image=lowerCamelCase__						,  generator=lowerCamelCase__						,  guidance_scale=5.0						,  num_inference_steps=5_0						,  output_type='''np'''						,  )

							lowerCAmelCase		:					Any  	=     output.images

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

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							lowerCAmelCase		:					Dict  	=     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]				)

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