


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      __snake_case				(   _lowerCamelCase):
					SCREAMING_SNAKE_CASE_		:       Union[str, Any]								=			42
					SCREAMING_SNAKE_CASE_		:       Any								=			42







def 				UpperCamelCase_	(					__snake_case      :    Any						,      __snake_case      :    int=0.999						,      __snake_case      :    Optional[Any]="cosine"						,      )->   List[Any]:




						'''simple docstring'''






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(__snake_case      :    int  ):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(__snake_case      :    Optional[Any]  ):
																		return math.exp(t * -12.0  )

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

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




class      __snake_case				(   _lowerCamelCase	,      _lowerCamelCase):
					SCREAMING_SNAKE_CASE_		:       Any								=			1
					@register_to_config
					def __init__(						self       :						Any	,      _lowerCAmelCase       :						int = 1_0_0_0	,      _lowerCAmelCase       :						float = 0.0001	,      _lowerCAmelCase       :						float = 0.02	,      _lowerCAmelCase       :						str = "linear"	,      _lowerCAmelCase       :						float = 0.0	,      _lowerCAmelCase       :						Optional[np.ndarray] = None	,      _lowerCAmelCase       :						bool = True	,      )							->					Union[str, Any]:



											'''simple docstring'''





											if trained_betas is not None:
																	__magic_name__					=      torch.from_numpy(_UpperCamelCase  )
											elif beta_schedule == "linear":
																	__magic_name__					=      torch.linspace(_UpperCamelCase	,      _UpperCamelCase	,      _UpperCamelCase	,      dtype=torch.floataa  )
											elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	__magic_name__					=      (
																	    torch.linspace(beta_start**0.5	,      beta_end**0.5	,      _UpperCamelCase	,      dtype=torch.floataa  ) ** 2
																	)
											elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	__magic_name__					=      betas_for_alpha_bar(_UpperCamelCase  )
											elif beta_schedule == "sigmoid":
																	# GeoDiff sigmoid schedule
																	__magic_name__					=      torch.linspace(-6	,      6	,      _UpperCamelCase  )
																	__magic_name__					=      torch.sigmoid(_UpperCamelCase  ) * (beta_end - beta_start) + beta_start
											else:
																	raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''  )

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

											__magic_name__					=      torch.tensor(1.0  )

											# standard deviation of the initial noise distribution
											__magic_name__					=      1.0

											# setable values
											__magic_name__					=      None
											__magic_name__					=      torch.from_numpy(np.arange(0	,      _UpperCamelCase  )[::-1].copy()  )

											__magic_name__					=      eta
					def 	a			(						self       :						int	,      _lowerCAmelCase       :						torch.FloatTensor	,      _lowerCAmelCase       :						Optional[int] = None  )							->					torch.FloatTensor:



											'''simple docstring'''





											return sample
					def 	a			(						self       :						str	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						int = 1_0	,      _lowerCAmelCase       :						int = 1_0	,      _lowerCAmelCase       :						Union[str, torch.device] = None	,      )							->					Any:



											'''simple docstring'''





											__magic_name__					=      min(self.config.num_train_timesteps	,      _UpperCamelCase  )
											__magic_name__					=      num_inference_steps

											__magic_name__					=      []

											__magic_name__					=      {}
											for j in range(0	,      num_inference_steps - jump_length	,      _UpperCamelCase  ):
																	__magic_name__					=      jump_n_sample - 1

											__magic_name__					=      num_inference_steps
											while t >= 1:
																	__magic_name__					=      t - 1
																	timesteps.append(_UpperCamelCase  )

																	if jumps.get(_UpperCamelCase	,      0  ) > 0:
																							__magic_name__					=      jumps[t] - 1
																							for _ in range(_UpperCamelCase  ):
																													__magic_name__					=      t + 1
																													timesteps.append(_UpperCamelCase  )

											__magic_name__					=      np.array(_UpperCamelCase  ) * (self.config.num_train_timesteps // self.num_inference_steps)
											__magic_name__					=      torch.from_numpy(_UpperCamelCase  ).to(_UpperCamelCase  )
					def 	a			(						self       :						Tuple	,      _lowerCAmelCase       :						int  )							->					Dict:



											'''simple docstring'''





											__magic_name__					=      t - self.config.num_train_timesteps // self.num_inference_steps

											__magic_name__					=      self.alphas_cumprod[t]
											__magic_name__					=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											__magic_name__					=      1 - alpha_prod_t
											__magic_name__					=      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]
											__magic_name__					=      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance
					def 	a			(						self       :						Any	,      _lowerCAmelCase       :						torch.FloatTensor	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						torch.FloatTensor	,      _lowerCAmelCase       :						torch.FloatTensor	,      _lowerCAmelCase       :						torch.FloatTensor	,      _lowerCAmelCase       :						Optional[torch.Generator] = None	,      _lowerCAmelCase       :						bool = True	,      )							->					Union[RePaintSchedulerOutput, Tuple]:



											'''simple docstring'''





											__magic_name__					=      timestep
											__magic_name__					=      timestep - self.config.num_train_timesteps // self.num_inference_steps

											# 1. compute alphas, betas
											__magic_name__					=      self.alphas_cumprod[t]
											__magic_name__					=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											__magic_name__					=      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
											__magic_name__					=      (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																	__magic_name__					=      torch.clamp(_UpperCamelCase	,      -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
											__magic_name__					=      model_output.device
											__magic_name__					=      randn_tensor(model_output.shape	,      generator=_UpperCamelCase	,      device=_UpperCamelCase	,      dtype=model_output.dtype  )
											__magic_name__					=      self.eta * self._get_variance(_UpperCamelCase  ) ** 0.5

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

											# 6. compute "direction pointing to x_t" of formula (12)
											# from https://arxiv.org/pdf/2010.02502.pdf
											__magic_name__					=      (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
											__magic_name__					=      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
											__magic_name__					=      (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
											__magic_name__					=      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=_UpperCamelCase	,      pred_original_sample=_UpperCamelCase  )
					def 	a			(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						str	,      _lowerCAmelCase       :						str=None  )							->					List[Any]:



											'''simple docstring'''





											__magic_name__					=      self.config.num_train_timesteps // self.num_inference_steps

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

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

											return sample
					def 	a			(						self       :						Any	,      _lowerCAmelCase       :						torch.FloatTensor	,      _lowerCAmelCase       :						torch.FloatTensor	,      _lowerCAmelCase       :						torch.IntTensor	,      )							->					torch.FloatTensor:



											'''simple docstring'''





											raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'  )







					def __len__(						self       :						Tuple  )							->					Optional[int]:



											'''simple docstring'''





											return self.config.num_train_timesteps








import os



def _a							(      )      ->		int:







      '''simple docstring'''






      __A			  =							os.path.dirname(os.path.realpath(lowerCamelCase					)					)
      __A			  =							os.path.join(lowerCamelCase   ,							'''triangle.txt'''					)

      with open(lowerCamelCase					) as in_file:
            __A			  =							[[int(lowerCamelCase					) for i in line.split()] for line in in_file]

      while len(lowerCamelCase					) != 1:
            __A			  =							triangle.pop()
            __A			  =							triangle[-1]
            for j in range(len(lowerCamelCase					) - 1					):
                  curr_row[j] += max(last_row[j]   ,							last_row[j + 1]					)
      return triangle[0][0]


if __name__ == "__main__":
       print(solution())





