
"""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      _SCREAMING_SNAKE_CASE   (     A__      ,					unittest.TestCase				):
			UpperCAmelCase_    :Any												=		RePaintPipeline
			UpperCAmelCase_    :List[Any]												=		IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
			UpperCAmelCase_    :List[Any]												=		PipelineTesterMixin.required_optional_params - {
			    "latents",
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			UpperCAmelCase_    :List[str]												=		IMAGE_INPAINTING_BATCH_PARAMS
			UpperCAmelCase_    :Tuple												=		False






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						torch.manual_seed(0			)
						torch.manual_seed(0			)
						lowerCAmelCase_       :Optional[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""")			,  )
						lowerCAmelCase_       :Any     	=    RePaintScheduler()
						lowerCAmelCase_       :Any     	=    {"""unet""": unet, """scheduler""": scheduler}
						return components






			def 					__lowerCAmelCase					(   self			,  __A			,  __A=0			)     ->	int:
						if str(__A			).startswith("""mps"""			):
									lowerCAmelCase_       :Any     	=    torch.manual_seed(__A			)
						else:
									lowerCAmelCase_       :Optional[int]     	=    torch.Generator(device=__A			).manual_seed(__A			)
						lowerCAmelCase_       :Optional[Any]     	=    np.random.RandomState(__A			).standard_normal((1, 3, 32, 32)			)
						lowerCAmelCase_       :List[Any]     	=    torch.from_numpy(__A			).to(device=__A			,  dtype=torch.floataa			)
						lowerCAmelCase_       :List[str]     	=    (image > 0).to(device=__A			,  dtype=torch.floataa			)
						lowerCAmelCase_       :str     	=    {
						    """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			)     ->	Any:
						lowerCAmelCase_       :Union[str, Any]     	=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase_       :Optional[int]     	=    self.get_dummy_components()
						lowerCAmelCase_       :Optional[int]     	=    RePaintPipeline(**__A			)
						lowerCAmelCase_       :Optional[int]     	=    sd_pipe.to(__A			)
						sd_pipe.set_progress_bar_config(disable=__A			)

						lowerCAmelCase_       :Optional[Any]     	=    self.get_dummy_inputs(__A			)
						lowerCAmelCase_       :Tuple     	=    sd_pipe(**__A			).images
						lowerCAmelCase_       :Any     	=    image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						lowerCAmelCase_       :List[str]     	=    np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]			)

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






			@skip_mps
			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						return super().test_save_load_local()






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






			@skip_mps
			def 					__lowerCAmelCase					(   self			)     ->	int:
						return super().test_dict_tuple_outputs_equivalent()






			@skip_mps
			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						return super().test_save_load_optional_components()






			@skip_mps
			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						return super().test_attention_slicing_forward_pass()




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






			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






			def 					__lowerCAmelCase					(   self			)     ->	Tuple:
						lowerCAmelCase_       :Optional[int]     	=    load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
						    """repaint/celeba_hq_256.png"""			)
						lowerCAmelCase_       :Optional[int]     	=    load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""			)
						lowerCAmelCase_       :int     	=    load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
						    """repaint/celeba_hq_256_result.npy"""			)

						lowerCAmelCase_       :Tuple     	=    """google/ddpm-ema-celebahq-256"""
						lowerCAmelCase_       :str     	=    UNetaDModel.from_pretrained(__A			)
						lowerCAmelCase_       :Optional[int]     	=    RePaintScheduler.from_pretrained(__A			)

						lowerCAmelCase_       :List[Any]     	=    RePaintPipeline(unet=__A			,  scheduler=__A			).to(__A			)
						repaint.set_progress_bar_config(disable=__A			)
						repaint.enable_attention_slicing()

						lowerCAmelCase_       :Optional[int]     	=    torch.manual_seed(0			)
						lowerCAmelCase_       :Optional[int]     	=    repaint(
						    __A			,  __A			,  num_inference_steps=250			,  eta=0.0			,  jump_length=10			,  jump_n_sample=10			,  generator=__A			,  output_type="""np"""			,  )
						lowerCAmelCase_       :Optional[int]     	=    output.images[0]

						assert image.shape == (256, 256, 3)
						assert np.abs(expected_image - image			).mean() < 1E-2








"""simple docstring"""






import math as m






def 		_snake_case			(      lowercase__						:    Any ,	lowercase__						:    Any ,	lowercase__						:    Optional[int]=0.001						)    ->							Tuple:


			'''simple docstring'''





			return (f(a + h						) - f(a - h						)) / (2 * h)






def 		_snake_case			(      lowercase__						:    Optional[Any] ,	lowercase__						:    List[str]=0 ,	lowercase__						:    Union[str, Any]=1_0_0 ,	lowercase__						:    Any=0.0001 ,	lowercase__						:    List[str]=1E-6 ,	lowercase__						:    str=False						)    ->							List[str]:


			'''simple docstring'''





			lowerCAmelCase_       :Dict     	=    xa  # set the initial guess
			lowerCAmelCase_       :int     	=    [a]
			lowerCAmelCase_       :int     	=    abs(f(lowercase__						)						)
			lowerCAmelCase_       :Optional[int]     	=    lambda lowercase__						: calc_derivative(lowercase__ ,	lowercase__ ,	h=lowercase__						)  # noqa: E731  Derivative of f(x)
			for _ in range(lowercase__						):
						if fa(lowercase__						) == 0:
									raise ValueError("""No converging solution found"""						)
						lowerCAmelCase_       :Tuple     	=    a - f(lowercase__						) / fa(lowercase__						)  # Calculate the next estimate
						if logsteps:
									steps.append(lowercase__						)
						if error < maxerror:
									break
			else:
						raise ValueError("""Iteration limit reached, no converging solution found"""						)
			if logsteps:
						# If logstep is true, then log intermediate steps
						return a, error, steps
			return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				__UpperCAmelCase 					=    lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				__UpperCAmelCase					,					__UpperCAmelCase					,					__UpperCAmelCase 					=    newton_raphson(
				    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel('step')
				plt.ylabel('error')
				plt.show()
				print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")






