



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 		lowerCamelCase_			(       SCREAMING_SNAKE_CASE__  ):






						'''simple docstring'''
						a__      =     42
						a__      =     42







def 		UpperCAmelCase      (  _lowerCamelCase       ,			_lowerCamelCase=0.9_99       ,			_lowerCamelCase="cosine"       ,			):
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_lowerCamelCase			):
									return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(_lowerCamelCase			):
									return math.exp(t * -12.0			)

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

			A							:    List[str]								=  []
			for i in range(_a			):
						A							:    Any								=  i / num_diffusion_timesteps
						A							:    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 		lowerCamelCase_			(       SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__  ):






						'''simple docstring'''
						a__      =     1







						@register_to_config
						def __init__(		self					:	Tuple		,		__lowerCamelCase					:	Optional[int] = 10_00		,		__lowerCamelCase					:	Any = 0.0001		,		__lowerCamelCase					:	List[str] = 0.02		,		__lowerCamelCase					:	Optional[int] = "linear"		,		__lowerCamelCase					:	Tuple = 0.0		,		__lowerCamelCase					:	List[Any] = None		,		__lowerCamelCase					:	int = True		,		)					-> Union[str, Any]:
									if trained_betas is not None:
												A							:    Any								=  torch.from_numpy(_SCREAMING_SNAKE_CASE       )
									elif beta_schedule == "linear":
												A							:    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.
												A							:    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
												A							:    Optional[int]								=  betas_for_alpha_bar(_SCREAMING_SNAKE_CASE       )
									elif beta_schedule == "sigmoid":
												# GeoDiff sigmoid schedule
												A							:    Optional[Any]								=  torch.linspace(-6		,		6		,		_SCREAMING_SNAKE_CASE       )
												A							:    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__}"""       )

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

									A							:    Dict								=  torch.tensor(1.0       )

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

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

									A							:    Any								=  eta







						def 	SCREAMING_SNAKE_CASE__						(		self					:	List[Any]		,		__lowerCamelCase					:	Union[str, Any]		,		__lowerCamelCase					:	Optional[Any] = None       )					-> torch.FloatTensor:
									return sample







						def 	SCREAMING_SNAKE_CASE__						(		self					:	str		,		__lowerCamelCase					:	List[str]		,		__lowerCamelCase					:	List[Any] = 10		,		__lowerCamelCase					:	str = 10		,		__lowerCamelCase					:	List[str] = None		,		)					-> Any:
									A							:    List[Any]								=  min(self.config.num_train_timesteps		,		_SCREAMING_SNAKE_CASE       )
									A							:    Any								=  num_inference_steps

									A							:    List[Any]								=  []

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

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

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

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







						def 	SCREAMING_SNAKE_CASE__						(		self					:	Any		,		__lowerCamelCase					:	int       )					-> str:
									A							:    Dict								=  t - self.config.num_train_timesteps // self.num_inference_steps

									A							:    Optional[Any]								=  self.alphas_cumprod[t]
									A							:    Dict								=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
									A							:    int								=  1 - alpha_prod_t
									A							:    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]
									A							:    List[str]								=  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

									return variance







						def 	SCREAMING_SNAKE_CASE__						(		self					:	Union[str, Any]		,		__lowerCamelCase					:	str		,		__lowerCamelCase					:	List[str]		,		__lowerCamelCase					:	List[str]		,		__lowerCamelCase					:	Union[str, Any]		,		__lowerCamelCase					:	Dict		,		__lowerCamelCase					:	str = None		,		__lowerCamelCase					:	Dict = True		,		)					-> Union[RePaintSchedulerOutput, Tuple]:
									A							:    Tuple								=  timestep
									A							:    Tuple								=  timestep - self.config.num_train_timesteps // self.num_inference_steps

									# 1. compute alphas, betas
									A							:    Optional[Any]								=  self.alphas_cumprod[t]
									A							:    List[Any]								=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
									A							:    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
									A							:    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:
												A							:    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
									A							:    Tuple								=  model_output.device
									A							:    Any								=  randn_tensor(model_output.shape		,		generator=_SCREAMING_SNAKE_CASE		,		device=_SCREAMING_SNAKE_CASE		,		dtype=model_output.dtype       )
									A							:    Union[str, Any]								=  self.eta * self._get_variance(_SCREAMING_SNAKE_CASE       ) ** 0.5

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

									# 6. compute "direction pointing to x_t" of formula (12)
									# from https://arxiv.org/pdf/2010.02502.pdf
									A							:    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
									A							:    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
									A							:    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
									A							:    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 	SCREAMING_SNAKE_CASE__						(		self					:	Dict		,		__lowerCamelCase					:	Dict		,		__lowerCamelCase					:	Any		,		__lowerCamelCase					:	str=None       )					-> Any:
									A							:    Union[str, Any]								=  self.config.num_train_timesteps // self.num_inference_steps

									for i in range(_SCREAMING_SNAKE_CASE       ):
												A							:    Tuple								=  self.betas[timestep + i]
												if sample.device.type == "mps":
															# randn does not work reproducibly on mps
															A							:    int								=  randn_tensor(sample.shape		,		dtype=sample.dtype		,		generator=_SCREAMING_SNAKE_CASE       )
															A							:    str								=  noise.to(sample.device       )
												else:
															A							:    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
												A							:    str								=  (1 - beta) ** 0.5 * sample + beta**0.5 * noise

									return sample







						def 	SCREAMING_SNAKE_CASE__						(		self					:	Union[str, Any]		,		__lowerCamelCase					:	str		,		__lowerCamelCase					:	Tuple		,		__lowerCamelCase					:	Tuple		,		)					-> torch.FloatTensor:
									raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."       )







						def __len__(		self					:	Dict       )					-> 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))
