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 (     SCREAMING_SNAKE_CASE__				):
  SCREAMING_SNAKE_CASE_						:						List[Any]    =42
  SCREAMING_SNAKE_CASE_						:						List[str]    =42



class      _A (     SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__				):
  SCREAMING_SNAKE_CASE_						:						Union[str, Any]    =1






  @register_to_config
  def __init__(self ,					SCREAMING_SNAKE_CASE_ = 2000 ,					SCREAMING_SNAKE_CASE_ = 0.15 ,					SCREAMING_SNAKE_CASE_ = 0.01 ,					SCREAMING_SNAKE_CASE_ = 1348.0 ,					SCREAMING_SNAKE_CASE_ = 1E-5 ,					SCREAMING_SNAKE_CASE_ = 1 ,					)		->   Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__					    =   sigma_max

       # setable values
       UpperCamelCase__					    =   None

       self.set_sigmas(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None						)		->   str:





       '''simple docstring'''





       return sample






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None						)		->   str:





       '''simple docstring'''





       UpperCamelCase__					    =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCamelCase__					    =   torch.linspace(1 ,					__lowerCamelCase ,					__lowerCamelCase ,					device=__lowerCamelCase						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None						)		->   List[str]:





       '''simple docstring'''





       UpperCamelCase__					    =   sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCamelCase__					    =   sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCamelCase__					    =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
            self.set_timesteps(__lowerCamelCase ,					__lowerCamelCase						)

       UpperCamelCase__					    =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       UpperCamelCase__					    =   torch.exp(torch.linspace(math.log(__lowerCamelCase						) ,					math.log(__lowerCamelCase						) ,					__lowerCamelCase						)						)
       UpperCamelCase__					    =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_						)		->   str:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = True ,					)		->   Optional[Any]:





       '''simple docstring'''





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

       UpperCamelCase__					    =   timestep * torch.ones(
           sample.shape[0] ,					device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
       UpperCamelCase__					    =   (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
       UpperCamelCase__					    =   timesteps.to(self.discrete_sigmas.device						)

       UpperCamelCase__					    =   self.discrete_sigmas[timesteps].to(sample.device						)
       UpperCamelCase__					    =   self.get_adjacent_sigma(__lowerCamelCase ,					__lowerCamelCase						).to(sample.device						)
       UpperCamelCase__					    =   torch.zeros_like(__lowerCamelCase						)
       UpperCamelCase__					    =   (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
       UpperCamelCase__					    =   diffusion.flatten()
       while len(diffusion.shape						) < len(sample.shape						):
            UpperCamelCase__					    =   diffusion.unsqueeze(-1						)
       UpperCamelCase__					    =   drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCamelCase__					    =   randn_tensor(
           sample.shape ,					layout=sample.layout ,					generator=__lowerCamelCase ,					device=sample.device ,					dtype=sample.dtype						)
       UpperCamelCase__					    =   sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCamelCase__					    =   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=__lowerCamelCase ,					prev_sample_mean=__lowerCamelCase						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = True ,					)		->   Optional[int]:





       '''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
       UpperCamelCase__					    =   randn_tensor(sample.shape ,					layout=sample.layout ,					generator=__lowerCamelCase						).to(sample.device						)

       # compute step size from the model_output, the noise, and the snr
       UpperCamelCase__					    =   torch.norm(model_output.reshape(model_output.shape[0] ,					-1						) ,					dim=-1						).mean()
       UpperCamelCase__					    =   torch.norm(noise.reshape(noise.shape[0] ,					-1						) ,					dim=-1						).mean()
       UpperCamelCase__					    =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCamelCase__					    =   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
       UpperCamelCase__					    =   step_size.flatten()
       while len(step_size.shape						) < len(sample.shape						):
            UpperCamelCase__					    =   step_size.unsqueeze(-1						)
       UpperCamelCase__					    =   sample + step_size * model_output
       UpperCamelCase__					    =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
            return (prev_sample,)

       return SchedulerOutput(prev_sample=__lowerCamelCase						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					)		->   Any:





       '''simple docstring'''





       UpperCamelCase__					    =   timesteps.to(original_samples.device						)
       UpperCamelCase__					    =   self.discrete_sigmas.to(original_samples.device						)[timesteps]
       UpperCamelCase__					    =   (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__lowerCamelCase						) * sigmas[:, None, None, None]
       )
       UpperCamelCase__					    =   noise + original_samples
       return noisy_samples






  def __len__(self						)		->   str:





       '''simple docstring'''





       return self.config.num_train_timesteps










"""simple docstring"""



def 						__lowercase	(  snake_case_    :    int							,snake_case_    :    int		)  ->int:



    '''simple docstring'''







    return int((input_a, input_a).count(0		) == 0		)







def 						__lowercase	(  )  ->None:



    '''simple docstring'''







    assert and_gate(0							,0		) == 0
    assert and_gate(0							,1		) == 0
    assert and_gate(1							,0		) == 0
    assert and_gate(1							,1		) == 1


if __name__ == "__main__":
       test_and_gate()
       print(and_gate(1, 0))
       print(and_gate(0, 0))
       print(and_gate(0, 1))
       print(and_gate(1, 1))




