





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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






@dataclass
class        _a(    __A    ):
		lowerCamelCase__     :torch.FloatTensor
		lowerCamelCase__     :torch.FloatTensor






class        _a(    __A	,   __A    ):
		lowerCamelCase__     :List[Any]   				=		1







		@register_to_config
		def __init__(    self ,						__snake_case = 2_0_0_0 ,						__snake_case = 0.15 ,						__snake_case = 0.01 ,						__snake_case = 13_48.0 ,						__snake_case = 1E-5 ,						__snake_case = 1 ,						)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   sigma_max

							# setable values
							_snake_case       :					Union[str, Any]				       =   None

							self.set_sigmas(__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None				)      ->      torch.FloatTensor:


							'''simple docstring'''

							return sample







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None ,						__snake_case = None				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Any				       =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

							_snake_case       :					List[Any]				       =   torch.linspace(1 ,						__snake_case ,						__snake_case ,						device=__snake_case				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None ,						__snake_case = None ,						__snake_case = None				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   sigma_min if sigma_min is not None else self.config.sigma_min
							_snake_case       :					Union[str, Any]				       =   sigma_max if sigma_max is not None else self.config.sigma_max
							_snake_case       :					Optional[Any]				       =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if self.timesteps is None:
												self.set_timesteps(__snake_case ,						__snake_case				)

							_snake_case       :					Optional[int]				       =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
							_snake_case       :					int				       =   torch.exp(torch.linspace(math.log(__snake_case				) ,						math.log(__snake_case				) ,						__snake_case				)				)
							_snake_case       :					Optional[Any]				       =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

							return torch.where(
							    timesteps == 0 ,						torch.zeros_like(t.to(timesteps.device				)				) ,						self.discrete_sigmas[timesteps - 1].to(timesteps.device				) ,						)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case = None ,						__snake_case = True ,						)      ->      Union[SdeVeOutput, Tuple]:


							'''simple docstring'''

							if self.timesteps is None:
												raise ValueError(
												    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

							_snake_case       :					Union[str, Any]				       =   timestep * torch.ones(
							    sample.shape[0] ,						device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
							_snake_case       :					Optional[int]				       =   (timestep * (len(self.timesteps				) - 1)).long()

							# mps requires indices to be in the same device, so we use cpu as is the default with cuda
							_snake_case       :					Dict				       =   timesteps.to(self.discrete_sigmas.device				)

							_snake_case       :					Tuple				       =   self.discrete_sigmas[timesteps].to(sample.device				)
							_snake_case       :					Optional[int]				       =   self.get_adjacent_sigma(__snake_case ,						__snake_case				).to(sample.device				)
							_snake_case       :					Union[str, Any]				       =   torch.zeros_like(__snake_case				)
							_snake_case       :					Tuple				       =   (sigma**2 - adjacent_sigma**2) ** 0.5

							# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
							# also equation 47 shows the analog from SDE models to ancestral sampling methods
							_snake_case       :					Optional[int]				       =   diffusion.flatten()
							while len(diffusion.shape				) < len(sample.shape				):
												_snake_case       :					Union[str, Any]				       =   diffusion.unsqueeze(-1				)
							_snake_case       :					Union[str, Any]				       =   drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							_snake_case       :					Tuple				       =   randn_tensor(
							    sample.shape ,						layout=sample.layout ,						generator=__snake_case ,						device=sample.device ,						dtype=sample.dtype				)
							_snake_case       :					Optional[Any]				       =   sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							_snake_case       :					Optional[Any]				       =   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

							if not return_dict:
												return (prev_sample, prev_sample_mean)

							return SdeVeOutput(prev_sample=__snake_case ,						prev_sample_mean=__snake_case				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case = None ,						__snake_case = True ,						)      ->      Union[SchedulerOutput, Tuple]:


							'''simple docstring'''

							if self.timesteps is None:
												raise ValueError(
												    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

							# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
							# sample noise for correction
							_snake_case       :					Tuple				       =   randn_tensor(sample.shape ,						layout=sample.layout ,						generator=__snake_case				).to(sample.device				)

							# compute step size from the model_output, the noise, and the snr
							_snake_case       :					List[str]				       =   torch.norm(model_output.reshape(model_output.shape[0] ,						-1				) ,						dim=-1				).mean()
							_snake_case       :					List[Any]				       =   torch.norm(noise.reshape(noise.shape[0] ,						-1				) ,						dim=-1				).mean()
							_snake_case       :					Tuple				       =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
							_snake_case       :					Optional[int]				       =   step_size * torch.ones(sample.shape[0]				).to(sample.device				)
							# self.repeat_scalar(step_size, sample.shape[0])

							# compute corrected sample: model_output term and noise term
							_snake_case       :					Tuple				       =   step_size.flatten()
							while len(step_size.shape				) < len(sample.shape				):
												_snake_case       :					str				       =   step_size.unsqueeze(-1				)
							_snake_case       :					Tuple				       =   sample + step_size * model_output
							_snake_case       :					str				       =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
												return (prev_sample,)

							return SchedulerOutput(prev_sample=__snake_case				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						)      ->      torch.FloatTensor:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   timesteps.to(original_samples.device				)
							_snake_case       :					Any				       =   self.discrete_sigmas.to(original_samples.device				)[timesteps]
							_snake_case       :					Optional[int]				       =   (
							    noise * sigmas[:, None, None, None]
							    if noise is not None
							    else torch.randn_like(__snake_case				) * sigmas[:, None, None, None]
							)
							_snake_case       :					Optional[Any]				       =   noise + original_samples
							return noisy_samples







		def __len__(    self				)      ->      Any:


							'''simple docstring'''

							return self.config.num_train_timesteps






import re







def    A				(							UpperCAmelCase				):
					_snake_case       :					Any				       =   re.compile(R"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$"				)
					if match := re.search(UpperCAmelCase  ,  UpperCAmelCase				):
										return match.string == phone
					return False


if __name__ == "__main__":
							print(indian_phone_validator('+918827897895'))