






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			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = 42
		snake_case			 = 42







def   snake_case		(				UpperCAmelCase						:							Union[str, Any],    UpperCAmelCase						:							List[str]=0.999,    UpperCAmelCase						:							Dict="cosine",    ):
				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				A									=   []
				for i in range(UpperCAmelCase ):
								A									=   i / num_diffusion_timesteps
								A									=   (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   UpperCamelCase			(					snake_case__      ,							snake_case__			):







		"""simple docstring"""



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


						'''simple docstring'''
						if trained_betas is not None:
										A									=   torch.from_numpy(_SCREAMING_SNAKE_CASE		)
						elif beta_schedule == "linear":
										A									=   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									=   (
										    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									=   betas_for_alpha_bar(_SCREAMING_SNAKE_CASE		)
						elif beta_schedule == "sigmoid":
										# GeoDiff sigmoid schedule
										A									=   torch.linspace(-6    ,6    ,_SCREAMING_SNAKE_CASE		)
										A									=   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									=   1.0 - self.betas
						A									=   torch.cumprod(self.alphas    ,dim=0		)
						A									=   torch.tensor(1.0		)

						A									=   torch.tensor(1.0		)

						# standard deviation of the initial noise distribution
						A									=   1.0

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

						A									=   eta
		def    A(	self		:							List[str]    ,_SCREAMING_SNAKE_CASE		:							torch.FloatTensor    ,_SCREAMING_SNAKE_CASE		:							Optional[int] = None		)     -> torch.FloatTensor:


						'''simple docstring'''
						return sample
		def    A(	self		:							str    ,_SCREAMING_SNAKE_CASE		:							int    ,_SCREAMING_SNAKE_CASE		:							int = 1_0    ,_SCREAMING_SNAKE_CASE		:							int = 1_0    ,_SCREAMING_SNAKE_CASE		:							Union[str, torch.device] = None    ,)     -> Any:


						'''simple docstring'''
						A									=   min(self.config.num_train_timesteps    ,_SCREAMING_SNAKE_CASE		)
						A									=   num_inference_steps

						A									=   []

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

						A									=   num_inference_steps
						while t >= 1:
										A									=   t - 1
										timesteps.append(_SCREAMING_SNAKE_CASE		)

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

						A									=   np.array(_SCREAMING_SNAKE_CASE		) * (self.config.num_train_timesteps // self.num_inference_steps)
						A									=   torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							str		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   t - self.config.num_train_timesteps // self.num_inference_steps

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

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


						'''simple docstring'''
						A									=   timestep
						A									=   timestep - self.config.num_train_timesteps // self.num_inference_steps

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

						# 3. Clip "predicted x_0"
						if self.config.clip_sample:
										A									=   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									=   model_output.device
						A									=   randn_tensor(model_output.shape    ,generator=_SCREAMING_SNAKE_CASE    ,device=_SCREAMING_SNAKE_CASE    ,dtype=model_output.dtype		)
						A									=   self.eta * self._get_variance(_SCREAMING_SNAKE_CASE		) ** 0.5

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

						# 6. compute "direction pointing to x_t" of formula (12)
						# from https://arxiv.org/pdf/2010.02502.pdf
						A									=   (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									=   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									=   (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									=   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    A(	self		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							int    ,_SCREAMING_SNAKE_CASE		:							List[str]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]=None		)     -> int:


						'''simple docstring'''
						A									=   self.config.num_train_timesteps // self.num_inference_steps

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

						return sample
		def    A(	self		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							torch.FloatTensor    ,_SCREAMING_SNAKE_CASE		:							torch.FloatTensor    ,_SCREAMING_SNAKE_CASE		:							torch.IntTensor    ,)     -> torch.FloatTensor:


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







		def __len__(	self		:							Optional[int]		)     -> Union[str, Any]:


						'''simple docstring'''
						return self.config.num_train_timesteps













import numpy as np







def   snake_case		(				UpperCAmelCase						:							Any ):
				A									=   np.exp(UpperCAmelCase )

				# Add up the all the exponentials
				A									=   np.sum(UpperCAmelCase )

				# Divide every exponent by the sum of all exponents
				A									=   exponent_vector / sum_of_exponents

				return softmax_vector


if __name__ == "__main__":
					print(softmax((0,)))





