




'''simple docstring'''

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase=0.9_9_9, __UpperCAmelCase="cosine", )						->   str:

					'''simple docstring'''

					if alpha_transform_type == "cosine":

										def alpha_bar_fn(__UpperCAmelCase      ):
															return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2      ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(__UpperCAmelCase      ):
															return math.exp(t * -1_2.0      )

					else:
										raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"      )

					snake_case_		     =							[]
					for i in range(__UpperCAmelCase      ):
										snake_case_		     =							i / num_diffusion_timesteps
										snake_case_		     =							(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase      ) / alpha_bar_fn(__UpperCAmelCase      ), __UpperCAmelCase      )      )
					return torch.tensor(__UpperCAmelCase, dtype=torch.floataa      )






class 		a							(  _lowerCamelCase  ,	_lowerCamelCase	):
				snake_case_           =						[e.name for e in KarrasDiffusionSchedulers]
				snake_case_           =						2


				@register_to_config
				def __init__(						self       :   Tuple			,							lowercase_       :   int = 1000			,							lowercase_       :   float = 0.0_0085			,							lowercase_       :   float = 0.012			,							lowercase_       :   str = "linear"			,							lowercase_       :   Optional[Union[np.ndarray, List[float]]] = None			,							lowercase_       :   str = "epsilon"			,							lowercase_       :   str = "linspace"			,							lowercase_       :   int = 0			,							):
									if trained_betas is not None:
														snake_case_		     =							torch.tensor(lowercase_			,							dtype=torch.floataa   )
									elif beta_schedule == "linear":
														snake_case_		     =							torch.linspace(lowercase_			,							lowercase_			,							lowercase_			,							dtype=torch.floataa   )
									elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														snake_case_		     =							(
														    torch.linspace(beta_start**0.5			,							beta_end**0.5			,							lowercase_			,							dtype=torch.floataa   ) ** 2
														)
									elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														snake_case_		     =							betas_for_alpha_bar(lowercase_   )
									else:
														raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"   )

									snake_case_		     =							1.0 - self.betas
									snake_case_		     =							torch.cumprod(self.alphas			,							dim=0   )

									#  set all values
									self.set_timesteps(lowercase_			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any			,							lowercase_       :   List[str]			,							lowercase_       :   List[Any]=None   ):
									if schedule_timesteps is None:
														snake_case_		     =							self.timesteps

									snake_case_		     =							(schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter   ) == 0:
														snake_case_		     =							1 if len(lowercase_   ) > 1 else 0
									else:
														snake_case_		     =							timestep.cpu().item() if torch.is_tensor(lowercase_   ) else timestep
														snake_case_		     =							self._index_counter[timestep_int]

									return indices[pos].item()


				@property
				def     A_					(						self       :   Dict   ):
									# standard deviation of the initial noise distribution
									if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5


				def     A_					(						self       :   Tuple			,							lowercase_       :   torch.FloatTensor			,							lowercase_       :   Union[float, torch.FloatTensor]			,							):
									snake_case_		     =							self.index_for_timestep(lowercase_   )

									if self.state_in_first_order:
														snake_case_		     =							self.sigmas[step_index]
									else:
														snake_case_		     =							self.sigmas_interpol[step_index]

									snake_case_		     =							sample / ((sigma**2 + 1) ** 0.5)
									return sample


				def     A_					(						self       :   int			,							lowercase_       :   int			,							lowercase_       :   Union[str, torch.device] = None			,							lowercase_       :   Optional[int] = None			,							):
									snake_case_		     =							num_inference_steps

									snake_case_		     =							num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
														snake_case_		     =							np.linspace(0			,							num_train_timesteps - 1			,							lowercase_			,							dtype=lowercase_   )[::-1].copy()
									elif self.config.timestep_spacing == "leading":
														snake_case_		     =							num_train_timesteps // self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														snake_case_		     =							(np.arange(0			,							lowercase_   ) * step_ratio).round()[::-1].copy().astype(lowercase_   )
														timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
														snake_case_		     =							num_train_timesteps / self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														snake_case_		     =							(np.arange(lowercase_			,							0			,							-step_ratio   )).round().copy().astype(lowercase_   )
														timesteps -= 1
									else:
														raise ValueError(
														    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."   )

									snake_case_		     =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
									snake_case_		     =							torch.from_numpy(np.log(lowercase_   )   ).to(lowercase_   )

									snake_case_		     =							np.interp(lowercase_			,							np.arange(0			,							len(lowercase_   )   )			,							lowercase_   )
									snake_case_		     =							np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
									snake_case_		     =							torch.from_numpy(lowercase_   ).to(device=lowercase_   )

									# interpolate sigmas
									snake_case_		     =							sigmas.log().lerp(sigmas.roll(1   ).log()			,							0.5   ).exp()

									snake_case_		     =							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
									snake_case_		     =							torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )

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

									# interpolate timesteps
									snake_case_		     =							self.sigma_to_t(lowercase_   ).to(lowercase_			,							dtype=timesteps.dtype   )
									snake_case_		     =							torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])			,							dim=-1   ).flatten()

									snake_case_		     =							torch.cat([timesteps[:1], interleaved_timesteps]   )

									snake_case_		     =							None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									snake_case_		     =							defaultdict(lowercase_   )


				def     A_					(						self       :   List[Any]			,							lowercase_       :   Optional[Any]   ):
									# get log sigma
									snake_case_		     =							sigma.log()

									# get distribution
									snake_case_		     =							log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									snake_case_		     =							dists.ge(0   ).cumsum(dim=0   ).argmax(dim=0   ).clamp(max=self.log_sigmas.shape[0] - 2   )
									snake_case_		     =							low_idx + 1

									snake_case_		     =							self.log_sigmas[low_idx]
									snake_case_		     =							self.log_sigmas[high_idx]

									# interpolate sigmas
									snake_case_		     =							(low - log_sigma) / (low - high)
									snake_case_		     =							w.clamp(0			,							1   )

									# transform interpolation to time range
									snake_case_		     =							(1 - w) * low_idx + w * high_idx
									snake_case_		     =							t.view(sigma.shape   )
									return t


				@property
				def     A_					(						self       :   Optional[Any]   ):
									return self.sample is None


				def     A_					(						self       :   int			,							lowercase_       :   Union[torch.FloatTensor, np.ndarray]			,							lowercase_       :   Union[float, torch.FloatTensor]			,							lowercase_       :   Union[torch.FloatTensor, np.ndarray]			,							lowercase_       :   bool = True			,							):
									snake_case_		     =							self.index_for_timestep(lowercase_   )

									# advance index counter by 1
									snake_case_		     =							timestep.cpu().item() if torch.is_tensor(lowercase_   ) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
														snake_case_		     =							self.sigmas[step_index]
														snake_case_		     =							self.sigmas_interpol[step_index + 1]
														snake_case_		     =							self.sigmas[step_index + 1]
									else:
														# 2nd order / KDPM2's method
														snake_case_		     =							self.sigmas[step_index - 1]
														snake_case_		     =							self.sigmas_interpol[step_index]
														snake_case_		     =							self.sigmas[step_index]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									snake_case_		     =							0
									snake_case_		     =							sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
														snake_case_		     =							sigma_hat if self.state_in_first_order else sigma_interpol
														snake_case_		     =							sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
														snake_case_		     =							sigma_hat if self.state_in_first_order else sigma_interpol
														snake_case_		     =							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
														    sample / (sigma_input**2 + 1)
														)
									elif self.config.prediction_type == "sample":
														raise NotImplementedError('''prediction_type not implemented yet: sample'''   )
									else:
														raise ValueError(
														    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"   )

									if self.state_in_first_order:
														# 2. Convert to an ODE derivative for 1st order
														snake_case_		     =							(sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														snake_case_		     =							sigma_interpol - sigma_hat

														# store for 2nd order step
														snake_case_		     =							sample
									else:
														# DPM-Solver-2
														# 2. Convert to an ODE derivative for 2nd order
														snake_case_		     =							(sample - pred_original_sample) / sigma_interpol

														# 3. delta timestep
														snake_case_		     =							sigma_next - sigma_hat

														snake_case_		     =							self.sample
														snake_case_		     =							None

									snake_case_		     =							sample + derivative * dt

									if not return_dict:
														return (prev_sample,)

									return SchedulerOutput(prev_sample=lowercase_   )


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   torch.FloatTensor			,							lowercase_       :   torch.FloatTensor			,							lowercase_       :   torch.FloatTensor			,							):
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									snake_case_		     =							self.sigmas.to(device=original_samples.device			,							dtype=original_samples.dtype   )
									if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_   ):
														# mps does not support float64
														snake_case_		     =							self.timesteps.to(original_samples.device			,							dtype=torch.floataa   )
														snake_case_		     =							timesteps.to(original_samples.device			,							dtype=torch.floataa   )
									else:
														snake_case_		     =							self.timesteps.to(original_samples.device   )
														snake_case_		     =							timesteps.to(original_samples.device   )

									snake_case_		     =							[self.index_for_timestep(lowercase_			,							lowercase_   ) for t in timesteps]

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

									snake_case_		     =							original_samples + noise * sigma
									return noisy_samples


				def __len__(						self       :   int   ):
									return self.config.num_train_timesteps







'''simple docstring'''

from PIL import Image


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Image:

					'''simple docstring'''

					def brightness(__UpperCAmelCase      ) -> float:
										return 128 + level + (c - 128)

					if not -2_5_5.0 <= level <= 2_5_5.0:
										raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''      )
					return img.point(__UpperCAmelCase      )


if __name__ == "__main__":
							# Load image
							with Image.open('image_data/lena.jpg') as img:
														# Change brightness to 100
														a    :							int										=    change_brightness(img, 100)
														brigt_img.save('image_data/lena_brightness.png', format='png')

