



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   __lowerCAmelCase  :
					"""simple docstring"""






					A__					:		Optional[int]      							=     None
					A__					:		Optional[jnp.ndarray]      							=     None
					A__					:		Optional[jnp.ndarray]      							=     None  # sigma(t_i)



					@classmethod
					def      _a				(  cls       :							List[Any]       ):






								"""simple docstring"""


								return cls()







@dataclass
class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		jnp.ndarray
					A__					:		jnp.ndarray
					A__					:		KarrasVeSchedulerState







class   __lowerCAmelCase  (		UpperCAmelCase_				,  UpperCAmelCase_   ):
					"""simple docstring"""






					@property
					def      _a				(  self       :							int       ):






								"""simple docstring"""


								return True



					@register_to_config
					def __init__(  self       :							Optional[int]					,       _snake_case       :							float = 0.02					,       _snake_case       :							float = 1_00					,       _snake_case       :							float = 1.007					,       _snake_case       :							float = 80					,       _snake_case       :							float = 0.05					,       _snake_case       :							float = 50					,       ):






								"""simple docstring"""


								pass



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								return KarrasVeSchedulerState.create()



					def      _a				(  self       :							List[str]					,       _snake_case       :							KarrasVeSchedulerState					,       _snake_case       :							int					,       _snake_case       :							Tuple = ()       ):






								"""simple docstring"""


								A__	  =     jnp.arange(0					,       _snake_case       )[::-1].copy()
								A__	  =     [
								    (
								        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=_snake_case					,       schedule=jnp.array(_snake_case					,       dtype=jnp.floataa       )					,       timesteps=_snake_case					,       )



					def      _a				(  self       :							Tuple					,       _snake_case       :							KarrasVeSchedulerState					,       _snake_case       :							jnp.ndarray					,       _snake_case       :							float					,       _snake_case       :							random.KeyArray					,       ):






								"""simple docstring"""


								if self.config.s_min <= sigma <= self.config.s_max:
											A__	  =     min(self.config.s_churn / state.num_inference_steps					,       2**0.5 - 1       )
								else:
											A__	  =     0

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

								return sample_hat, sigma_hat



					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							KarrasVeSchedulerState					,       _snake_case       :							jnp.ndarray					,       _snake_case       :							float					,       _snake_case       :							float					,       _snake_case       :							jnp.ndarray					,       _snake_case       :							bool = True					,       ):






								"""simple docstring"""


								A__	  =     sample_hat + sigma_hat * model_output
								A__	  =     (sample_hat - pred_original_sample) / sigma_hat
								A__	  =     sample_hat + (sigma_prev - sigma_hat) * derivative

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

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



					def      _a				(  self       :							int					,       _snake_case       :							KarrasVeSchedulerState					,       _snake_case       :							jnp.ndarray					,       _snake_case       :							float					,       _snake_case       :							float					,       _snake_case       :							jnp.ndarray					,       _snake_case       :							jnp.ndarray					,       _snake_case       :							jnp.ndarray					,       _snake_case       :							bool = True					,       ):






								"""simple docstring"""


								A__	  =     sample_prev + sigma_prev * model_output
								A__	  =     (sample_prev - pred_original_sample) / sigma_prev
								A__	  =     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=_snake_case					,       derivative=_snake_case					,       state=_snake_case       )



					def      _a				(  self       :							List[Any]					,       _snake_case       :							KarrasVeSchedulerState					,       _snake_case       :							str					,       _snake_case       :							Optional[int]					,       _snake_case       :							str       ):






								"""simple docstring"""


								raise NotImplementedError()









def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						A__	  =     _modexpt(__UpperCamelCase			,						exponent // 2			,						__UpperCamelCase       ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(__UpperCamelCase			,						exponent - 1			,						__UpperCamelCase       )) % modulo_value


def A    (							__UpperCamelCase = 1_777			,						__UpperCamelCase = 1_855			,						__UpperCamelCase = 8       )    ->   int:
			A__	  =     base
			for _ in range(1			,						__UpperCamelCase       ):
						A__	  =     _modexpt(__UpperCamelCase			,						__UpperCamelCase			,						10**digits       )

			return result


if __name__ == "__main__":
							print(f'{solution() = }')




