


from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


_lowerCAmelCase:	List[str]      							=					logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class 			lowercase_				(lowercase__      ):
				snake_case  						=42







class 			lowercase_				(lowercase__			,				lowercase__      ):
				snake_case  						=1





				@register_to_config
				def __init__(							self					,     lowercase_ = 40					,     lowercase_ = 0.0_02					,     lowercase_ = 80.0					,     lowercase_ = 0.5					,     lowercase_ = 1.0					,     lowercase_ = 7.0					,     lowercase_ = True					,     )     ->   str:
											# standard deviation of the initial noise distribution
											a__     						=sigma_max

											a__     						=np.linspace(0					,     1					,     _A)
											a__     						=self._convert_to_karras(_A)
											a__     						=self.sigma_to_t(_A)

											# setable values
											a__     						=None
											a__     						=torch.from_numpy(_A)
											a__     						=torch.from_numpy(_A)
											a__     						=False
											a__     						=False





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_=None)     ->   List[Any]:
											if schedule_timesteps is None:
																		a__     						=self.timesteps

											a__     						=(schedule_timesteps == timestep).nonzero()
											return indices.item()





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_)     ->   torch.FloatTensor:
											# Get sigma corresponding to timestep
											if isinstance(_A					,     torch.Tensor):
																		a__     						=timestep.to(self.timesteps.device)
											a__     						=self.index_for_timestep(_A)
											a__     						=self.sigmas[step_idx]

											a__     						=sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

											a__     						=True
											return sample





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   List[Any]:
											if not isinstance(_A					,     np.ndarray):
																		a__     						=np.array(_A					,     dtype=np.floataa)

											a__     						=1000 * 0.25 * np.log(sigmas + 1e-44)

											return timesteps





				def 	__UpperCamelCase						(							self					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = None					,     )     ->   List[Any]:
											if num_inference_steps is None and timesteps is None:
																		raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.')

											if num_inference_steps is not None and timesteps is not None:
																		raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.')

											# Follow DDPMScheduler custom timesteps logic
											if timesteps is not None:
																		for i in range(1					,     len(_A)):
																									if timesteps[i] >= timesteps[i - 1]:
																																raise ValueError('`timesteps` must be in descending order.')

																		if timesteps[0] >= self.config.num_train_timesteps:
																									raise ValueError(
																									    F"""`timesteps` must start before `self.config.train_timesteps`:"""
																									    F""" {self.config.num_train_timesteps}.""")

																		a__     						=np.array(_A					,     dtype=np.intaa)
																		a__     						=True
											else:
																		if num_inference_steps > self.config.num_train_timesteps:
																									raise ValueError(
																									    F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																									    F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																									    F""" maximal {self.config.num_train_timesteps} timesteps.""")

																		a__     						=num_inference_steps

																		a__     						=self.config.num_train_timesteps // self.num_inference_steps
																		a__     						=(np.arange(0					,     _A) * step_ratio).round()[::-1].copy().astype(np.intaa)
																		a__     						=False

											# Map timesteps to Karras sigmas directly for multistep sampling
											# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
											a__     						=self.config.num_train_timesteps
											a__     						=timesteps[::-1].copy()
											a__     						=ramp / (num_train_timesteps - 1)
											a__     						=self._convert_to_karras(_A)
											a__     						=self.sigma_to_t(_A)

											a__     						=np.concatenate([sigmas, [self.sigma_min]]).astype(np.floataa)
											a__     						=torch.from_numpy(_A).to(device=_A)

											if str(_A).startswith('mps'):
																		# mps does not support float64
																		a__     						=torch.from_numpy(_A).to(_A					,     dtype=torch.floataa)
											else:
																		a__     						=torch.from_numpy(_A).to(device=_A)





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Any:
											a__     						=self.config.sigma_min
											a__     						=self.config.sigma_max

											a__     						=self.config.rho
											a__     						=sigma_min ** (1 / rho)
											a__     						=sigma_max ** (1 / rho)
											a__     						=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   str:
											a__     						=self.config.sigma_data

											a__     						=sigma_data**2 / (sigma**2 + sigma_data**2)
											a__     						=sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
											return c_skip, c_out





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   List[str]:
											a__     						=self.config.sigma_min
											a__     						=self.config.sigma_data

											a__     						=sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
											a__     						=(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
											return c_skip, c_out





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_ = None					,     lowercase_ = True					,     )     ->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:

											if (
											    isinstance(_A					,     _A)
											    or isinstance(_A					,     torch.IntTensor)
											    or isinstance(_A					,     torch.LongTensor)
											):
																		raise ValueError(
																		    (
																		        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
																		        F""" `{self.__class__}.step()` is not supported. Make sure to pass"""
																		        ' one of the `scheduler.timesteps` as a timestep.'
																		    )					,     )

											if not self.is_scale_input_called:
																		logger.warning(
																		    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
																		    'See `StableDiffusionPipeline` for a usage example.')

											if isinstance(_A					,     torch.Tensor):
																		a__     						=timestep.to(self.timesteps.device)

											a__     						=self.config.sigma_min
											a__     						=self.config.sigma_max

											a__     						=self.index_for_timestep(_A)

											# sigma_next corresponds to next_t in original implementation
											a__     						=self.sigmas[step_index]
											if step_index + 1 < self.config.num_train_timesteps:
																		a__     						=self.sigmas[step_index + 1]
											else:
																		# Set sigma_next to sigma_min
																		a__     						=self.sigmas[-1]

											# Get scalings for boundary conditions
											a__     						=self.get_scalings_for_boundary_condition(_A)

											# 1. Denoise model output using boundary conditions
											a__     						=c_out * model_output + c_skip * sample
											if self.config.clip_denoised:
																		a__     						=denoised.clamp(-1					,     1)

											# 2. Sample z ~ N(0, s_noise^2 * I)
											# Noise is not used for onestep sampling.
											if len(self.timesteps) > 1:
																		a__     						=randn_tensor(
																		    model_output.shape					,     dtype=model_output.dtype					,     device=model_output.device					,     generator=_A)
											else:
																		a__     						=torch.zeros_like(_A)
											a__     						=noise * self.config.s_noise

											a__     						=sigma_next.clamp(min=_A					,     max=_A)

											# 3. Return noisy sample
											# tau = sigma_hat, eps = sigma_min
											a__     						=denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

											if not return_dict:
																		return (prev_sample,)

											return CMStochasticIterativeSchedulerOutput(prev_sample=_A)





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     )     ->   torch.FloatTensor:
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											a__     						=self.sigmas.to(device=original_samples.device					,     dtype=original_samples.dtype)
											if original_samples.device.type == "mps" and torch.is_floating_point(_A):
																		# mps does not support float64
																		a__     						=self.timesteps.to(original_samples.device					,     dtype=torch.floataa)
																		a__     						=timesteps.to(original_samples.device					,     dtype=torch.floataa)
											else:
																		a__     						=self.timesteps.to(original_samples.device)
																		a__     						=timesteps.to(original_samples.device)

											a__     						=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

											a__     						=sigmas[step_indices].flatten()
											while len(sigma.shape) < len(original_samples.shape):
																		a__     						=sigma.unsqueeze(-1)

											a__     						=original_samples + noise * sigma
											return noisy_samples





				def __len__(							self)     ->   Optional[Any]:
											return self.config.num_train_timesteps








import numpy as np
from PIL import Image






def      _lowercase(				__a							:	np.array    ):
							a__						,    a__						,    a__     						=rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def      _lowercase(				__a							:	np.array    ):
							return (gray > 127) & (gray <= 255)






def      _lowercase(				__a							:	np.array						,    __a							:	np.array    ):
							a__     						=np.zeros_like(__a    )
							a__     						=np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

							# Copy image to padded image
							a__     						=image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]    ):
														for y in range(image.shape[0]    ):
																					a__     						=(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					a__     						=int(summation == 5    )
							return output


# kernel to be applied
_lowerCAmelCase:	Dict      							=					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
	# read original image
	_lowerCAmelCase:	Optional[int]      							=					np.array(Image.open(R'..\image_data\lena.jpg'))
	# Apply erosion operation to a binary image
	_lowerCAmelCase:	Any      							=					erosion(grayabinary(rgbagray(image)), structuring_element)
	# Save the output image
	_lowerCAmelCase:	Dict      							=					Image.fromarray(output).convert('RGB')
	pil_img.save('result_erosion.png')

