



"""simple docstring"""







import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class   UpperCamelCase	(     snake_case					,   unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       int						       =  RePaintPipeline
	SCREAMING_SNAKE_CASE_							:       Dict						       =  IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
	SCREAMING_SNAKE_CASE_							:       str						       =  PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  IMAGE_INPAINTING_BATCH_PARAMS
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  False






	def    lowerCamelCase__					(     self   ):
			torch.manual_seed(0   )
			torch.manual_seed(0   )
			_lowercase				: List[Any]     =   UNetaDModel(
			    block_out_channels=(32, 64)				,layers_per_block=2				,sample_size=32				,in_channels=3				,out_channels=3				,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")				,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")				,)
			_lowercase				: str     =   RePaintScheduler()
			_lowercase				: Union[str, Any]     =   {"""unet""": unet, """scheduler""": scheduler}
			return components






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=0   ):
			if str(UpperCAmelCase_   ).startswith("""mps"""   ):
					_lowercase				: Any     =   torch.manual_seed(UpperCAmelCase_   )
			else:
					_lowercase				: List[str]     =   torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
			_lowercase				: List[Any]     =   np.random.RandomState(UpperCAmelCase_   ).standard_normal((1, 3, 32, 32)   )
			_lowercase				: int     =   torch.from_numpy(UpperCAmelCase_   ).to(device=UpperCAmelCase_				,dtype=torch.floataa   )
			_lowercase				: int     =   (image > 0).to(device=UpperCAmelCase_				,dtype=torch.floataa   )
			_lowercase				: int     =   {
			    """image""": image,
			    """mask_image""": mask,
			    """generator""": generator,
			    """num_inference_steps""": 5,
			    """eta""": 0.0,
			    """jump_length""": 2,
			    """jump_n_sample""": 2,
			    """output_type""": """numpy""",
			}
			return inputs






	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
			_lowercase				: Dict     =   self.get_dummy_components()
			_lowercase				: Any     =   RePaintPipeline(**UpperCAmelCase_   )
			_lowercase				: Optional[Any]     =   sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			_lowercase				: Optional[int]     =   self.get_dummy_inputs(UpperCAmelCase_   )
			_lowercase				: List[Any]     =   sd_pipe(**UpperCAmelCase_   ).images
			_lowercase				: Optional[Any]     =   image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			_lowercase				: Optional[int]     =   np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]   )

			assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3






	@skip_mps
	def    lowerCamelCase__					(     self   ):
			return super().test_save_load_local()






	@unittest.skip("""non-deterministic pipeline"""   )
	def    lowerCamelCase__					(     self   ):
			return super().test_inference_batch_single_identical()






	@skip_mps
	def    lowerCamelCase__					(     self   ):
			return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def    lowerCamelCase__					(     self   ):
			return super().test_save_load_optional_components()






	@skip_mps
	def    lowerCamelCase__					(     self   ):
			return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Union[str, Any]     =   load_image(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
			    """repaint/celeba_hq_256.png"""   )
			_lowercase				: Optional[Any]     =   load_image(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""   )
			_lowercase				: Dict     =   load_numpy(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
			    """repaint/celeba_hq_256_result.npy"""   )

			_lowercase				: int     =   """google/ddpm-ema-celebahq-256"""
			_lowercase				: Optional[Any]     =   UNetaDModel.from_pretrained(UpperCAmelCase_   )
			_lowercase				: Union[str, Any]     =   RePaintScheduler.from_pretrained(UpperCAmelCase_   )

			_lowercase				: Optional[int]     =   RePaintPipeline(unet=UpperCAmelCase_				,scheduler=UpperCAmelCase_   ).to(UpperCAmelCase_   )
			repaint.set_progress_bar_config(disable=UpperCAmelCase_   )
			repaint.enable_attention_slicing()

			_lowercase				: Union[str, Any]     =   torch.manual_seed(0   )
			_lowercase				: List[Any]     =   repaint(
			    UpperCAmelCase_				,UpperCAmelCase_				,num_inference_steps=2_50				,eta=0.0				,jump_length=10				,jump_n_sample=10				,generator=UpperCAmelCase_				,output_type="""np"""				,)
			_lowercase				: Optional[Any]     =   output.images[0]

			assert image.shape == (2_56, 2_56, 3)
			assert np.abs(expected_image - image   ).mean() < 1E-2








'''simple docstring'''







import matplotlib.pyplot as plt
import numpy as np







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:str										=      len(SCREAMING_SNAKE_CASE     )  # Number of training samples
		SCREAMING_SNAKE_CASE_						:List[Any]										=      np.eye(SCREAMING_SNAKE_CASE     )  # Initialize weights as identity matrix
		for j in range(SCREAMING_SNAKE_CASE     ):
				SCREAMING_SNAKE_CASE_						:Tuple										=      point - x_train[j]
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.exp(diff @ diff.T / (-2.0 * tau**2)     )

		return weights







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      weight_matrix(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:str										=      np.linalg.inv(x_train.T @ weight_mat @ x_train     ) @ (
		    x_train.T @ weight_mat @ y_train.T
		)

		return weight







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      np.zeros(len(SCREAMING_SNAKE_CASE     )     )  # Initialize array of predictions
		for i, item in enumerate(SCREAMING_SNAKE_CASE     ):
				SCREAMING_SNAKE_CASE_						:Dict										=      item @ local_weight(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

		return y_pred







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		import seaborn as sns

		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      sns.load_dataset(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Tuple										=      np.array(data[x_name]     )
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.array(data[y_name]     )

		SCREAMING_SNAKE_CASE_						:str										=      np.ones(len(SCREAMING_SNAKE_CASE     )     )

		# pairing elements of one and x_data
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.column_stack((one, x_data)     )

		return x_train, x_data, y_data







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		):
		SCREAMING_SNAKE_CASE_						:Tuple										=      np.sort(SCREAMING_SNAKE_CASE ,		axis=0     )
		plt.scatter(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		color='blue'     )
		plt.plot(
		    x_train_sorted[:, 1] ,		preds[x_train[:, 1].argsort(0     )] ,		color='yellow' ,		linewidth=5 ,		)
		plt.title('Local Weighted Regression'     )
		plt.xlabel(SCREAMING_SNAKE_CASE     )
		plt.ylabel(SCREAMING_SNAKE_CASE     )
		plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__     : str				=   load_data("""tips""", """total_bill""", """tip""")
							SCREAMING_SNAKE_CASE__     : str				=   local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")
