
"""simple docstring"""

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class      _UpperCAmelCase							:
      # setable values
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	None
      __SCREAMING_SNAKE_CASE   :							Optional[jnp.ndarray]							      =	None
      __SCREAMING_SNAKE_CASE   :							Optional[jnp.ndarray]							      =	None  # sigma(t_i)




      @classmethod
      def     a_      (							cls							)						->					Optional[int]:
          return cls()

@dataclass
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							jnp.ndarray
      __SCREAMING_SNAKE_CASE   :							jnp.ndarray
      __SCREAMING_SNAKE_CASE   :							KarrasVeSchedulerState

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			SCREAMING_SNAKE_CASE_							):




      @property
      def     a_      (							self							)						->					List[str]:
          return True




      @register_to_config
      def __init__(							self   ,       lowercase_ = 0.0_2   ,       lowercase_ = 1_0_0   ,       lowercase_ = 1.0_0_7   ,       lowercase_ = 8_0   ,       lowercase_ = 0.0_5   ,       lowercase_ = 5_0   ,       )						->					Optional[Any]:
          pass




      def     a_      (							self							)						->					Optional[int]:
          return KarrasVeSchedulerState.create()




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_ = ()							)						->					KarrasVeSchedulerState:
          UpperCAmelCase									=					jnp.arange(0   ,       lowercase_							)[::-1].copy()
          UpperCAmelCase									=					[
              (
                  self.config.sigma_max**2
                  * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
              )
              for i in timesteps
          ]

          return state.replace(
              num_inference_steps=lowercase_   ,       schedule=jnp.array(lowercase_   ,       dtype=jnp.floataa							)   ,       timesteps=lowercase_   ,       )




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       )						->					Tuple[jnp.ndarray, float]:
          if self.config.s_min <= sigma <= self.config.s_max:
              UpperCAmelCase									=					min(self.config.s_churn / state.num_inference_steps   ,       2**0.5 - 1							)
          else:
              UpperCAmelCase									=					0

          # sample eps ~ N(0, S_noise^2 * I)
          UpperCAmelCase									=					random.split(lowercase_   ,       num=1							)
          UpperCAmelCase									=					self.config.s_noise * random.normal(key=lowercase_   ,       shape=sample.shape							)
          UpperCAmelCase									=					sigma + gamma * sigma
          UpperCAmelCase									=					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

          return sample_hat, sigma_hat




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ = True   ,       )						->					Union[FlaxKarrasVeOutput, Tuple]:
          UpperCAmelCase									=					sample_hat + sigma_hat * model_output
          UpperCAmelCase									=					(sample_hat - pred_original_sample) / sigma_hat
          UpperCAmelCase									=					sample_hat + (sigma_prev - sigma_hat) * derivative

          if not return_dict:
              return (sample_prev, derivative, state)

          return FlaxKarrasVeOutput(prev_sample=lowercase_   ,       derivative=lowercase_   ,       state=lowercase_							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ = True   ,       )						->					Union[FlaxKarrasVeOutput, Tuple]:
          UpperCAmelCase									=					sample_prev + sigma_prev * model_output
          UpperCAmelCase									=					(sample_prev - pred_original_sample) / sigma_prev
          UpperCAmelCase									=					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

          if not return_dict:
              return (sample_prev, derivative, state)

          return FlaxKarrasVeOutput(prev_sample=lowercase_   ,       derivative=lowercase_   ,       state=lowercase_							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Optional[Any]:
          raise NotImplementedError()





"""simple docstring"""

def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					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))



