






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

import numpy as np
import torch

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




@dataclass
class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      torch.FloatTensor
						A   :      torch.FloatTensor




def    lowerCAmelCase__		(			_a				:	Any     ,					_a				:	str=0.999     ,					_a				:	List[Any]="cosine"     ,					):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(_a				:	Optional[Any]			):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(_a				:	Union[str, Any]			):
						return math.exp(t * -12.0			)

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

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




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ,     SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      List[Any]						 =    1






						@register_to_config
						def __init__(		self						, _SCREAMING_SNAKE_CASE = 1000						, _SCREAMING_SNAKE_CASE = 0.0001						, _SCREAMING_SNAKE_CASE = 0.02						, _SCREAMING_SNAKE_CASE = "linear"						, _SCREAMING_SNAKE_CASE = 0.0						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = True						, )       -> Union[str, Any]:
								if trained_betas is not None:
										snake_case_ :							Any         =				torch.from_numpy(_SCREAMING_SNAKE_CASE )
								elif beta_schedule == "linear":
										snake_case_ :							List[str]         =				torch.linspace(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, dtype=torch.floataa )
								elif beta_schedule == "scaled_linear":
										# this schedule is very specific to the latent diffusion model.
										snake_case_ :							Dict         =				(
										    torch.linspace(beta_start**0.5						, beta_end**0.5						, _SCREAMING_SNAKE_CASE						, dtype=torch.floataa ) ** 2
										)
								elif beta_schedule == "squaredcos_cap_v2":
										# Glide cosine schedule
										snake_case_ :							Optional[int]         =				betas_for_alpha_bar(_SCREAMING_SNAKE_CASE )
								elif beta_schedule == "sigmoid":
										# GeoDiff sigmoid schedule
										snake_case_ :							Optional[Any]         =				torch.linspace(-6						, 6						, _SCREAMING_SNAKE_CASE )
										snake_case_ :							List[str]         =				torch.sigmoid(_SCREAMING_SNAKE_CASE ) * (beta_end - beta_start) + beta_start
								else:
										raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

								snake_case_ :							str         =				1.0 - self.betas
								snake_case_ :							Dict         =				torch.cumprod(self.alphas						, dim=0 )
								snake_case_ :							Dict         =				torch.tensor(1.0 )

								snake_case_ :							Dict         =				torch.tensor(1.0 )

								# standard deviation of the initial noise distribution
								snake_case_ :							Any         =				1.0

								# setable values
								snake_case_ :							str         =				None
								snake_case_ :							Any         =				torch.from_numpy(np.arange(0						, _SCREAMING_SNAKE_CASE )[::-1].copy() )

								snake_case_ :							Any         =				eta






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = None )       -> torch.FloatTensor:
								return sample






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = 10						, _SCREAMING_SNAKE_CASE = 10						, _SCREAMING_SNAKE_CASE = None						, )       -> Any:
								snake_case_ :							List[Any]         =				min(self.config.num_train_timesteps						, _SCREAMING_SNAKE_CASE )
								snake_case_ :							Any         =				num_inference_steps

								snake_case_ :							List[Any]         =				[]

								snake_case_ :							Tuple         =				{}
								for j in range(0						, num_inference_steps - jump_length						, _SCREAMING_SNAKE_CASE ):
										snake_case_ :							Union[str, Any]         =				jump_n_sample - 1

								snake_case_ :							str         =				num_inference_steps
								while t >= 1:
										snake_case_ :							Union[str, Any]         =				t - 1
										timesteps.append(_SCREAMING_SNAKE_CASE )

										if jumps.get(_SCREAMING_SNAKE_CASE						, 0 ) > 0:
												snake_case_ :							Any         =				jumps[t] - 1
												for _ in range(_SCREAMING_SNAKE_CASE ):
														snake_case_ :							List[str]         =				t + 1
														timesteps.append(_SCREAMING_SNAKE_CASE )

								snake_case_ :							List[str]         =				np.array(_SCREAMING_SNAKE_CASE ) * (self.config.num_train_timesteps // self.num_inference_steps)
								snake_case_ :							int         =				torch.from_numpy(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> str:
								snake_case_ :							Dict         =				t - self.config.num_train_timesteps // self.num_inference_steps

								snake_case_ :							Optional[Any]         =				self.alphas_cumprod[t]
								snake_case_ :							Dict         =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								snake_case_ :							int         =				1 - alpha_prod_t
								snake_case_ :							str         =				1 - alpha_prod_t_prev

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from
								# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
								# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
								# variance to pred_sample
								# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
								# without eta.
								# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
								snake_case_ :							List[str]         =				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

								return variance






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = True						, )       -> Union[RePaintSchedulerOutput, Tuple]:
								snake_case_ :							Tuple         =				timestep
								snake_case_ :							Tuple         =				timestep - self.config.num_train_timesteps // self.num_inference_steps

								# 1. compute alphas, betas
								snake_case_ :							Optional[Any]         =				self.alphas_cumprod[t]
								snake_case_ :							List[Any]         =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								snake_case_ :							Dict         =				1 - alpha_prod_t

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								snake_case_ :							Optional[int]         =				(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
										snake_case_ :							str         =				torch.clamp(_SCREAMING_SNAKE_CASE						, -1						, 1 )

								# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
								# substitute formula (7) in the algorithm coming from DDPM paper
								# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
								# DDIM schedule gives the same results as DDPM with eta = 1.0
								# Noise is being reused in 7. and 8., but no impact on quality has
								# been observed.

								# 5. Add noise
								snake_case_ :							Tuple         =				model_output.device
								snake_case_ :							Any         =				randn_tensor(model_output.shape						, generator=_SCREAMING_SNAKE_CASE						, device=_SCREAMING_SNAKE_CASE						, dtype=model_output.dtype )
								snake_case_ :							Union[str, Any]         =				self.eta * self._get_variance(_SCREAMING_SNAKE_CASE ) ** 0.5

								snake_case_ :							str         =				0
								if t > 0 and self.eta > 0:
										snake_case_ :							str         =				std_dev_t * noise

								# 6. compute "direction pointing to x_t" of formula (12)
								# from https://arxiv.org/pdf/2010.02502.pdf
								snake_case_ :							Tuple         =				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

								# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								snake_case_ :							List[str]         =				alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

								# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
								snake_case_ :							List[Any]         =				(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

								# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
								snake_case_ :							List[Any]         =				mask * prev_known_part + (1.0 - mask) * prev_unknown_part

								if not return_dict:
										return (
										    pred_prev_sample,
										    pred_original_sample,
										)

								return RePaintSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE						, pred_original_sample=_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE=None )       -> Any:
								snake_case_ :							Union[str, Any]         =				self.config.num_train_timesteps // self.num_inference_steps

								for i in range(_SCREAMING_SNAKE_CASE ):
										snake_case_ :							Tuple         =				self.betas[timestep + i]
										if sample.device.type == "mps":
												# randn does not work reproducibly on mps
												snake_case_ :							int         =				randn_tensor(sample.shape						, dtype=sample.dtype						, generator=_SCREAMING_SNAKE_CASE )
												snake_case_ :							str         =				noise.to(sample.device )
										else:
												snake_case_ :							Tuple         =				randn_tensor(sample.shape						, generator=_SCREAMING_SNAKE_CASE						, device=sample.device						, dtype=sample.dtype )

										# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
										snake_case_ :							str         =				(1 - beta) ** 0.5 * sample + beta**0.5 * noise

								return sample






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> torch.FloatTensor:
								raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint." )






						def __len__(		self )       -> List[str]:
								return self.config.num_train_timesteps








from __future__ import annotations

def    lowerCAmelCase__		(			_a				:	list[int]     ,					_a				:	int			):
		snake_case_       ,				snake_case_ :							Tuple         =				0, len(_a			) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
							print(double_linear_search(list(range(1_00)), 40))
