



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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class 			snake_case_     :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Union[str, Any]		    =       42

			# setable values
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       42
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       42
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       42
			SCREAMING_SNAKE_CASE  :     int		    =       None

			# running values
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       None







			@classmethod
			def        snake_case__(  cls				:       str							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       str							,     _UpperCamelCase				:       List[Any]							,     _UpperCamelCase				:       str							)   ->int:
									return cls(common=_lowerCAmelCase							,     init_noise_sigma=_lowerCAmelCase							,     timesteps=_lowerCAmelCase							,     sigmas=_lowerCAmelCase							)






@dataclass
class 			snake_case_     (	__lowerCamelCase      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Dict		    =       42






class 			snake_case_     (	__lowerCamelCase			, __lowerCamelCase      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     str		    =       [e.name for e in FlaxKarrasDiffusionSchedulers]

			SCREAMING_SNAKE_CASE  :     List[Any]		    =       42







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







			@register_to_config
			def __init__(  self				:       Optional[int]							,     _UpperCamelCase				:       Any = 1_0_0_0							,     _UpperCamelCase				:       Any = 0.0001							,     _UpperCamelCase				:       Dict = 0.02							,     _UpperCamelCase				:       Tuple = "linear"							,     _UpperCamelCase				:       Any = None							,     _UpperCamelCase				:       str = "epsilon"							,     _UpperCamelCase				:       Optional[Any] = jnp.floataa							,     )   ->Any:
									snake_case_     =			dtype







			def        snake_case__(  self				:       Any							,     _UpperCamelCase				:       Any = None							)   ->Union[str, Any]:
									if common is None:
															snake_case_     =			CommonSchedulerState.create(self							)

									snake_case_     =			jnp.arange(0							,     self.config.num_train_timesteps							).round()[::-1]
									snake_case_     =			((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

									# standard deviation of the initial noise distribution
									snake_case_     =			sigmas.max()

									return LMSDiscreteSchedulerState.create(
									    common=_lowerCAmelCase							,     init_noise_sigma=_lowerCAmelCase							,     timesteps=_lowerCAmelCase							,     sigmas=_lowerCAmelCase							,     )







			def        snake_case__(  self				:       Any							,     _UpperCamelCase				:       Any							,     _UpperCamelCase				:       Optional[Any]							,     _UpperCamelCase				:       List[Any]							)   ->str:
									(snake_case_		)     =			jnp.where(state.timesteps == timestep							,     size=1							)
									snake_case_     =			step_index[0]

									snake_case_     =			state.sigmas[step_index]
									snake_case_     =			sample / ((sigma**2 + 1) ** 0.5)
									return sample







			def        snake_case__(  self				:       Tuple							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       str							,     _UpperCamelCase				:       List[Any]							,     _UpperCamelCase				:       List[str]							)   ->Optional[Any]:

									def lms_derivative(_UpperCamelCase				:       List[Any]							):
															snake_case_     =			1.0
															for k in range(_lowerCAmelCase							):
																					if current_order == k:
																											continue
																					prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
															return prod

									snake_case_     =			integrate.quad(_lowerCAmelCase							,     state.sigmas[t]							,     state.sigmas[t + 1]							,     epsrel=1e-4							)[0]

									return integrated_coeff







			def        snake_case__(  self				:       Optional[int]							,     _UpperCamelCase				:       int							,     _UpperCamelCase				:       Tuple							,     _UpperCamelCase				:       Tuple = ()							)   ->Optional[Any]:
									snake_case_     =			jnp.linspace(self.config.num_train_timesteps - 1							,     0							,     _lowerCAmelCase							,     dtype=self.dtype							)

									snake_case_     =			jnp.floor(_lowerCAmelCase							).astype(jnp.intaa							)
									snake_case_     =			jnp.ceil(_lowerCAmelCase							).astype(jnp.intaa							)

									snake_case_     =			jnp.mod(_lowerCAmelCase							,     1.0							)

									snake_case_     =			((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
									snake_case_     =			(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
									snake_case_     =			jnp.concatenate([sigmas, jnp.array([0.0]							,     dtype=self.dtype							)]							)

									snake_case_     =			timesteps.astype(jnp.intaa							)

									# initial running values
									snake_case_     =			jnp.zeros((0,) + shape							,     dtype=self.dtype							)

									return state.replace(
									    timesteps=_lowerCAmelCase							,     sigmas=_lowerCAmelCase							,     num_inference_steps=_lowerCAmelCase							,     derivatives=_lowerCAmelCase							,     )







			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       Optional[int]							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       Tuple = 4							,     _UpperCamelCase				:       int = True							,     )   ->List[str]:
									if state.num_inference_steps is None:
															raise ValueError(
															    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''							)

									snake_case_     =			state.sigmas[timestep]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
															snake_case_     =			sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															snake_case_     =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									else:
															raise ValueError(
															    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''							)

									# 2. Convert to an ODE derivative
									snake_case_     =			(sample - pred_original_sample) / sigma
									snake_case_     =			state.replace(derivatives=jnp.append(state.derivatives							,     _lowerCAmelCase							)							)
									if len(state.derivatives							) > order:
															snake_case_     =			state.replace(derivatives=jnp.delete(state.derivatives							,     0							)							)

									# 3. Compute linear multistep coefficients
									snake_case_     =			min(timestep + 1							,     _lowerCAmelCase							)
									snake_case_     =			[self.get_lms_coefficient(_lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							) for curr_order in range(_lowerCAmelCase							)]

									# 4. Compute previous sample based on the derivatives path
									snake_case_     =			sample + sum(
									    coeff * derivative for coeff, derivative in zip(_lowerCAmelCase							,     reversed(state.derivatives							)							)							)

									if not return_dict:
															return (prev_sample, state)

									return FlaxLMSSchedulerOutput(prev_sample=_lowerCAmelCase							,     state=_lowerCAmelCase							)







			def        snake_case__(  self				:       List[str]							,     _UpperCamelCase				:       Dict							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       Optional[int]							,     )   ->List[Any]:
									snake_case_     =			state.sigmas[timesteps].flatten()
									snake_case_     =			broadcast_to_shape_from_left(_lowerCAmelCase							,     noise.shape							)

									snake_case_     =			original_samples + noise * sigma

									return noisy_samples







			def __len__(  self				:       List[str]							)   ->Optional[Any]:
									return self.config.num_train_timesteps


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						return (input_a, input_a).count(0 ) % 2


def 						__SCREAMING_SNAKE_CASE      ():
						assert xor_gate(0						,	0 ) == 0
						assert xor_gate(0						,	1 ) == 1
						assert xor_gate(1						,	0 ) == 1
						assert xor_gate(1						,	1 ) == 0


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