



"""simple docstring"""
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 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				torch.FloatTensor
  UpperCamelCase	:				torch.FloatTensor
  UpperCamelCase	:				Optional[torch.FloatTensor]     						=					None







class 					a	(  UpperCAmelCase__	,      UpperCAmelCase__       ):
  UpperCamelCase	:				List[Any]     						=					2



  @register_to_config
  def __init__(						self :					List[Any]       ,		lowerCAmelCase :					float = 0.0_2       ,		lowerCAmelCase :					float = 100       ,		lowerCAmelCase :					float = 1.0_0_7       ,		lowerCAmelCase :					float = 80       ,		lowerCAmelCase :					float = 0.0_5       ,		lowerCAmelCase :					float = 50       ,		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=sigma_max

     # setable values
     SCREAMING_SNAKE_CASE_: int     			=None
     SCREAMING_SNAKE_CASE_: np.IntTensor     			=None
     SCREAMING_SNAKE_CASE_: torch.FloatTensor     			=None  # sigma(t_i)



  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					torch.FloatTensor       ,		lowerCAmelCase :					Optional[int] = None		)     ->					torch.FloatTensor:


     '''simple docstring'''




     return sample



  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					Union[str, torch.device] = None		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=num_inference_steps
     SCREAMING_SNAKE_CASE_: Dict     			=np.arange(0       ,		self.num_inference_steps		)[::-1].copy()
     SCREAMING_SNAKE_CASE_: str     			=torch.from_numpy(lowerCAmelCase		).to(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=[
         (
             self.config.sigma_max**2
             * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
         )
         for i in self.timesteps
     ]
     SCREAMING_SNAKE_CASE_: Tuple     			=torch.tensor(lowerCAmelCase       ,		dtype=torch.floataa       ,		device=lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					torch.FloatTensor       ,		lowerCAmelCase :					float       ,		lowerCAmelCase :					Optional[torch.Generator] = None		)     ->					Tuple[torch.FloatTensor, float]:


     '''simple docstring'''




     if self.config.s_min <= sigma <= self.config.s_max:
        SCREAMING_SNAKE_CASE_: str     			=min(self.config.s_churn / self.num_inference_steps       ,		2**0.5 - 1		)
     else:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=0

     # sample eps ~ N(0, S_noise^2 * I)
     SCREAMING_SNAKE_CASE_: List[str]     			=self.config.s_noise * randn_tensor(sample.shape       ,		generator=lowerCAmelCase		).to(sample.device		)
     SCREAMING_SNAKE_CASE_: Tuple     			=sigma + gamma * sigma
     SCREAMING_SNAKE_CASE_: List[str]     			=sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

     return sample_hat, sigma_hat



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					torch.FloatTensor       ,		lowerCAmelCase :					float       ,		lowerCAmelCase :					float       ,		lowerCAmelCase :					torch.FloatTensor       ,		lowerCAmelCase :					bool = True       ,		)     ->					Union[KarrasVeOutput, Tuple]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=sample_hat + sigma_hat * model_output
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=(sample_hat - pred_original_sample) / sigma_hat
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=sample_hat + (sigma_prev - sigma_hat) * derivative

     if not return_dict:
        return (sample_prev, derivative)

     return KarrasVeOutput(
         prev_sample=lowerCAmelCase       ,		derivative=lowerCAmelCase       ,		pred_original_sample=lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					torch.FloatTensor       ,		lowerCAmelCase :					float       ,		lowerCAmelCase :					float       ,		lowerCAmelCase :					torch.FloatTensor       ,		lowerCAmelCase :					torch.FloatTensor       ,		lowerCAmelCase :					torch.FloatTensor       ,		lowerCAmelCase :					bool = True       ,		)     ->					Union[KarrasVeOutput, Tuple]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=sample_prev + sigma_prev * model_output
     SCREAMING_SNAKE_CASE_: List[str]     			=(sample_prev - pred_original_sample) / sigma_prev
     SCREAMING_SNAKE_CASE_: str     			=sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

     if not return_dict:
        return (sample_prev, derivative)

     return KarrasVeOutput(
         prev_sample=lowerCAmelCase       ,		derivative=lowerCAmelCase       ,		pred_original_sample=lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					Union[str, Any]       ,		lowerCAmelCase :					Tuple       ,		lowerCAmelCase :					Tuple		)     ->					Any:


     '''simple docstring'''




     raise NotImplementedError()








"""simple docstring"""
def      __magic_name__				(							lowercase							,			lowercase ):
   return int((input_a, input_a).count(0 ) == 0 )





def      __magic_name__				(							):
   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))



