



"""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




import numpy as np
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_=5       ,  lowerCAmelCase_=5       ):
		_lowercase   =depth
		_lowercase   =0
		_lowercase   =None
		_lowercase   =None
		_lowercase   =min_leaf_size
		_lowercase   =None


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if labels.ndim != 1:
			print("Error: Input labels must be one dimensional"       )

		return np.mean((labels - prediction) ** 2       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if x.ndim != 1:
			print("Error: Input data set must be one dimensional"       )
			return
		if len(lowerCAmelCase_       ) != len(lowerCAmelCase_       ):
			print("Error: X and y have different lengths"       )
			return
		if y.ndim != 1:
			print("Error: Data set labels must be one dimensional"       )
			return

		if len(lowerCAmelCase_       ) < 2 * self.min_leaf_size:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		if self.depth == 1:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		_lowercase   =0
		_lowercase   =self.mean_squared_error(lowerCAmelCase_       ,  np.mean(lowerCAmelCase_       )       ) * 2
		for i in range(len(lowerCAmelCase_       )       ):
			if len(x[:i]       ) < self.min_leaf_size:
				continue
			elif len(x[i:]       ) < self.min_leaf_size:
				continue
			else:
				_lowercase   =self.mean_squared_error(x[:i]       ,  np.mean(y[:i]       )       )
				_lowercase   =self.mean_squared_error(x[i:]       ,  np.mean(y[i:]       )       )
				_lowercase   =error_left + error_right
				if error < min_error:
					_lowercase   =i
					_lowercase   =error

		if best_split != 0:
			_lowercase   =x[:best_split]
			_lowercase   =y[:best_split]
			_lowercase   =x[best_split:]
			_lowercase   =y[best_split:]

			_lowercase   =x[best_split]
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			self.left.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
			self.right.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
		else:
			_lowercase   =np.mean(lowerCAmelCase_       )

		return


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		if self.prediction is not None:
			return self.prediction
		elif self.left or self.right is not None:
			if x >= self.decision_boundary:
				return self.right.predict(lowerCAmelCase_       )
			else:
				return self.left.predict(lowerCAmelCase_       )
		else:
			print("Error: Decision tree not yet trained"       )
			return None

class 	_a  :



	"""simple docstring"""



	@staticmethod
	def __lowerCAmelCase      (   lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =float(0       )
		for label in labels:
			squared_error_sum += (label - prediction) ** 2

		return float(squared_error_sum / labels.size       )

def 	__lowerCamelCase							(				)      ->       Union[str, Any]:
	_lowercase   =np.arange(-1.0      ,							1.0      ,							0.005		)
	_lowercase   =np.sin(__a		)

	_lowercase   =DecisionTree(depth=10      ,							min_leaf_size=10		)
	tree.train(__a      ,							__a		)

	_lowercase   =(np.random.rand(10		) * 2) - 1
	_lowercase   =np.array([tree.predict(__a		) for x in test_cases]		)
	_lowercase   =np.mean((predictions - test_cases) ** 2		)

	print("Test values: " + str(__a		)		)
	print("Predictions: " + str(__a		)		)
	print("Average error: " + str(__a		)		)


if __name__ == "__main__":
						main()
						import doctest

						doctest.testmod(name="mean_squarred_error", verbose=True)




