"""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        lowercase_  (  _a				,					unittest.TestCase						):



	"""simple docstring"""





	UpperCAmelCase_  : Dict       =    PaintByExamplePipeline
	UpperCAmelCase_  : Any       =    IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
	UpperCAmelCase_  : str       =    IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	UpperCAmelCase_  : int       =    frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess


	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[str]:
								torch.manual_seed(0    )
								lowerCAmelCase     		=		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		,      )
								lowerCAmelCase     		=		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__    )
								torch.manual_seed(0    )
								lowerCAmelCase     		=		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    )
								lowerCAmelCase     		=		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		,      )
								lowerCAmelCase     		=		PaintByExampleImageEncoder(SCREAMING_SNAKE_CASE__		,      proj_size=32    )
								lowerCAmelCase     		=		CLIPImageProcessor(crop_size=32		,      size=32    )

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


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								lowerCAmelCase     		=		np.array(image.convert('''RGB'''    )    )
								lowerCAmelCase     		=		image[None].transpose(0		,      3		,      1		,      2    )
								lowerCAmelCase     		=		torch.from_numpy(SCREAMING_SNAKE_CASE__    ).to(dtype=torch.floataa    ) / 1_2_7.5 - 1.0
								return image


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

								if str(SCREAMING_SNAKE_CASE__    ).startswith('''mps'''    ):
															lowerCAmelCase     		=		torch.manual_seed(SCREAMING_SNAKE_CASE__    )
								else:
															lowerCAmelCase     		=		torch.Generator(device=SCREAMING_SNAKE_CASE__    ).manual_seed(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase     		=		{
								    "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       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[Any]:
								lowerCAmelCase     		=		self.get_dummy_components()

								# make sure here that pndm scheduler skips prk
								lowerCAmelCase     		=		PaintByExamplePipeline(**SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase     		=		pipe.to('''cpu'''    )
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase     		=		self.get_dummy_inputs()
								lowerCAmelCase     		=		pipe(**SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase     		=		output.images

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

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

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

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

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

								lowerCAmelCase     		=		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__    )    ).convert('''RGB'''    )
								lowerCAmelCase     		=		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__    )    ).convert('''RGB'''    )

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

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




	def       SCREAMING_SNAKE_CASE_    (							self    )						->Union[str, Any]:
								super().test_inference_batch_single_identical(expected_max_diff=3e-3    )




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



	"""simple docstring"""





	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




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

								lowerCAmelCase     		=		PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''    )
								lowerCAmelCase     		=		pipe.to(SCREAMING_SNAKE_CASE__    )
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase     		=		torch.manual_seed(321    )
								lowerCAmelCase     		=		pipe(
								    image=SCREAMING_SNAKE_CASE__		,      mask_image=SCREAMING_SNAKE_CASE__		,      example_image=SCREAMING_SNAKE_CASE__		,      generator=SCREAMING_SNAKE_CASE__		,      guidance_scale=5.0		,      num_inference_steps=50		,      output_type='''np'''		,      )

								lowerCAmelCase     		=		output.images

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

								assert image.shape == (1, 512, 512, 3)
								lowerCAmelCase     		=		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




import warnings

from .generation import FlaxGenerationMixin




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	warnings.warn(
	    """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
	    """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""				,					UpperCamelCase_				,					)



