
"""simple docstring"""





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_a								=						logging.get_logger(__name__)  # pylint: disable=invalid-name



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





					warnings.warn(
					    '''The preprocess method is deprecated and will be removed in a future version. Please'''
					    ''' use VaeImageProcessor.preprocess instead''',	__snake_case,	)
					if isinstance(__snake_case,	torch.Tensor      ):
										return image
					elif isinstance(__snake_case,	PIL.Image.Image      ):
										_UpperCamelCase									=    [image]

					if isinstance(image[0],	PIL.Image.Image      ):
										_UpperCamelCase					,    _UpperCamelCase									=    image[0].size
										_UpperCamelCase					,    _UpperCamelCase									=    (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

										_UpperCamelCase									=    [np.array(i.resize((w, h),	resample=PIL_INTERPOLATION['''lanczos''']      )      )[None, :] for i in image]
										_UpperCamelCase									=    np.concatenate(__snake_case,	axis=0      )
										_UpperCamelCase									=    np.array(__snake_case      ).astype(np.floataa      ) / 255.0
										_UpperCamelCase									=    image.transpose(0,	3,	1,	2      )
										_UpperCamelCase									=    2.0 * image - 1.0
										_UpperCamelCase									=    torch.from_numpy(__snake_case      )
					elif isinstance(image[0],	torch.Tensor      ):
										_UpperCamelCase									=    torch.cat(__snake_case,	dim=0      )
					return image



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					if isinstance(__snake_case,	torch.Tensor      ):
										return mask
					elif isinstance(__snake_case,	PIL.Image.Image      ):
										_UpperCamelCase									=    [mask]

					if isinstance(mask[0],	PIL.Image.Image      ):
										_UpperCamelCase					,    _UpperCamelCase									=    mask[0].size
										_UpperCamelCase					,    _UpperCamelCase									=    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
										_UpperCamelCase									=    [np.array(m.convert('''L'''      ).resize((w, h),	resample=PIL_INTERPOLATION['''nearest''']      )      )[None, :] for m in mask]
										_UpperCamelCase									=    np.concatenate(__snake_case,	axis=0      )
										_UpperCamelCase									=    mask.astype(np.floataa      ) / 255.0
										_UpperCamelCase									=    0
										_UpperCamelCase									=    1
										_UpperCamelCase									=    torch.from_numpy(__snake_case      )
					elif isinstance(mask[0],	torch.Tensor      ):
										_UpperCamelCase									=    torch.cat(__snake_case,	dim=0      )
					return mask






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 42
		lowercase__         = 42



		def __init__(   self							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(unet=__a							,						scheduler=__a)




		@torch.no_grad()
		def __call__(   self							,						__a							,						__a							,						__a = 2_50							,						__a = 0.0							,						__a = 10							,						__a = 10							,						__a = None							,						__a = "pil"							,						__a = True							,						)			->			Union[ImagePipelineOutput, Tuple]:






							'''simple docstring'''
							_UpperCamelCase									=    image

							_UpperCamelCase									=    _preprocess_image(__a)
							_UpperCamelCase									=    original_image.to(device=self.device							,						dtype=self.unet.dtype)
							_UpperCamelCase									=    _preprocess_mask(__a)
							_UpperCamelCase									=    mask_image.to(device=self.device							,						dtype=self.unet.dtype)

							_UpperCamelCase									=    original_image.shape[0]

							# sample gaussian noise to begin the loop
							if isinstance(__a							,						__a) and len(__a) != batch_size:
												raise ValueError(
												    F'''You have passed a list of generators of length {len(__a)}, but requested an effective batch'''
												    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''')

							_UpperCamelCase									=    original_image.shape
							_UpperCamelCase									=    randn_tensor(__a							,						generator=__a							,						device=self.device							,						dtype=self.unet.dtype)

							# set step values
							self.scheduler.set_timesteps(__a							,						__a							,						__a							,						self.device)
							_UpperCamelCase									=    eta

							_UpperCamelCase									=    self.scheduler.timesteps[0] + 1
							_UpperCamelCase									=    generator[0] if isinstance(__a							,						__a) else generator
							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
												if t < t_last:
																	# predict the noise residual
																	_UpperCamelCase									=    self.unet(__a							,						__a).sample
																	# compute previous image: x_t -> x_t-1
																	_UpperCamelCase									=    self.scheduler.step(__a							,						__a							,						__a							,						__a							,						__a							,						__a).prev_sample

												else:
																	# compute the reverse: x_t-1 -> x_t
																	_UpperCamelCase									=    self.scheduler.undo_step(__a							,						__a							,						__a)
												_UpperCamelCase									=    t

							_UpperCamelCase									=    (image / 2 + 0.5).clamp(0							,						1)
							_UpperCamelCase									=    image.cpu().permute(0							,						2							,						3							,						1).numpy()
							if output_type == "pil":
												_UpperCamelCase									=    self.numpy_to_pil(__a)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=__a)








from __future__ import annotations


def    a      ( a	)     ->float:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 0.00
			SCREAMING_SNAKE_CASE     						= 0
			for resistor in resistors:
						if resistor <= 0:
									SCREAMING_SNAKE_CASE     						= F"""Resistor at index {index} has a negative or zero value!"""
									raise ValueError(a	)
						first_sum += 1 / float(a	)
						index += 1
			return 1 / first_sum


def    a      ( a	)     ->float:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 0.00
			SCREAMING_SNAKE_CASE     						= 0
			for resistor in resistors:
						sum_r += resistor
						if resistor < 0:
									SCREAMING_SNAKE_CASE     						= F"""Resistor at index {index} has a negative value!"""
									raise ValueError(a	)
						index += 1
			return sum_r


if __name__ == "__main__":
							import doctest

							doctest.testmod()