




"""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							(      snake_case_    , unittest.TestCase						):


				'''simple docstring'''


				_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    _lowerCamelCase       ( self :  Optional[Any]   ):
										torch.manual_seed(0   )
										snake_case__							:       str				      =       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   ,)
										snake_case__							:       str				      =       PNDMScheduler(skip_prk_steps=_snake_case   )
										torch.manual_seed(0   )
										snake_case__							:       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   )
										snake_case__							:       Optional[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   ,)
										snake_case__							:       str				      =       PaintByExampleImageEncoder(_snake_case   ,proj_size=3_2   )
										snake_case__							:       str				      =       CLIPImageProcessor(crop_size=3_2   ,size=3_2   )

										snake_case__							:       int				      =       {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''image_encoder''': image_encoder,
										    '''safety_checker''': None,
										    '''feature_extractor''': feature_extractor,
										}
										return components

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

				def    _lowerCamelCase       ( self :  Tuple   ,SCREAMING_SNAKE_CASE_ :  Dict="cpu"   ,SCREAMING_SNAKE_CASE_ :  int=0   ):
										snake_case__							:       List[str]				      =       floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(_snake_case   )   ).to(_snake_case   )
										snake_case__							:       Any				      =       image.cpu().permute(0   ,2   ,3   ,1   )[0]
										snake_case__							:       Optional[Any]				      =       Image.fromarray(np.uinta(_snake_case   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )
										snake_case__							:       Union[str, Any]				      =       Image.fromarray(np.uinta(image + 4   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )
										snake_case__							:       List[str]				      =       Image.fromarray(np.uinta(_snake_case   )   ).convert('''RGB'''   ).resize((3_2, 3_2)   )

										if str(_snake_case   ).startswith('''mps'''   ):
																snake_case__							:       Any				      =       torch.manual_seed(_snake_case   )
										else:
																snake_case__							:       List[str]				      =       torch.Generator(device=_snake_case   ).manual_seed(_snake_case   )
										snake_case__							:       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    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       List[str]				      =       self.get_dummy_components()

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

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

										snake_case__							:       Any				      =       image[0, -3:, -3:, -1]

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

										# make sure here that pndm scheduler skips prk
										snake_case__							:       str				      =       PaintByExamplePipeline(**self.get_dummy_components()   )
										snake_case__							:       Any				      =       pipe.to(_snake_case   )
										pipe.set_progress_bar_config(disable=_snake_case   )

										snake_case__							:       List[Any]				      =       pipe(image=_snake_case   ,mask_image=mask_image[:, 0]   ,**_snake_case   )
										snake_case__							:       Tuple				      =       output.images

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

										snake_case__							:       List[Any]				      =       Image.fromarray(np.uinta(_snake_case   )   ).convert('''RGB'''   )
										snake_case__							:       Any				      =       Image.fromarray(np.uinta(_snake_case   )   ).convert('''RGB'''   )

										snake_case__							:       Tuple				      =       pipe(**self.get_dummy_inputs()   )
										snake_case__							:       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    _lowerCamelCase       ( self :  Any   ):
										super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







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


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





				def    _lowerCamelCase       ( self :  Tuple   ):
										snake_case__							:       Any				      =       load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/paint_by_example/dog_in_bucket.png'''   )
										snake_case__							:       Union[str, Any]				      =       load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/paint_by_example/mask.png'''   )
										snake_case__							:       Any				      =       load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/paint_by_example/panda.jpg'''   )

										snake_case__							:       int				      =       PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''   )
										snake_case__							:       Optional[Any]				      =       pipe.to(_snake_case   )
										pipe.set_progress_bar_config(disable=_snake_case   )

										snake_case__							:       Tuple				      =       torch.manual_seed(3_2_1   )
										snake_case__							:       str				      =       pipe(
										    image=_snake_case   ,mask_image=_snake_case   ,example_image=_snake_case   ,generator=_snake_case   ,guidance_scale=5.0   ,num_inference_steps=5_0   ,output_type='''np'''   ,)

										snake_case__							:       List[str]				      =       output.images

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

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








def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5) + 32  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) + 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5) + 491.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) - 32) * 5 / 9  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(((float(__A			) - 32) * 5 / 9) + 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) + 459.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) - 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(((float(__A			) - 273.15) * 9 / 5) + 32  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) - 491.67) * 5 / 9  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) - 459.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 5 / 9)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 1.25 + 273.15)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 2.25 + 32)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 1.25)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 2.25 + 32 + 459.67)  ,							__A			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






