






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     a    :



      """simple docstring"""


      a  : Optional[int]							       =	None
      a  : int							       =	None
      a  : Optional[Any]							       =	None  # sigma(t_i)
      @classmethod
      def 	UpperCAmelCase      (      cls					:			List[Any]					)							->      Optional[Any]:
        return cls()


@dataclass
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Any							       =	42
      a  : Union[str, Any]							       =	42
      a  : Optional[Any]							       =	42


class     a    ( lowercase__							,       lowercase__   ):



      """simple docstring"""


      @property
      def 	UpperCAmelCase      (      self					:			Any					)							->      List[str]:
        return True
      @register_to_config
      def __init__(      self					:			Dict		,      __lowercase					:			Optional[Any] = 0.02		,      __lowercase					:			int = 100		,      __lowercase					:			List[str] = 1.007		,      __lowercase					:			Any = 80		,      __lowercase					:			Tuple = 0.05		,      __lowercase					:			Optional[Any] = 50		,      )							->      Union[str, Any]:
        pass
      def 	UpperCAmelCase      (      self					:			int					)							->      Any:
        return KarrasVeSchedulerState.create()
      def 	UpperCAmelCase      (      self					:			List[Any]		,      __lowercase					:			str		,      __lowercase					:			str		,      __lowercase					:			List[Any] = ()					)							->      KarrasVeSchedulerState:
        __UpperCAmelCase							:	Optional[int]           =  jnp.arange(0		,      __a					)[::-1].copy()
        __UpperCAmelCase							:	List[Any]           =  [
            (
                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=__a		,      schedule=jnp.array(__a		,      dtype=jnp.floataa					)		,      timesteps=__a		,      )
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			Any		,      __lowercase					:			Optional[int]		,      __lowercase					:			List[str]		,      __lowercase					:			Dict		,      )							->      Tuple[jnp.ndarray, float]:
        if self.config.s_min <= sigma <= self.config.s_max:
          __UpperCAmelCase							:	Dict           =  min(self.config.s_churn / state.num_inference_steps		,      2**0.5 - 1					)
        else:
          __UpperCAmelCase							:	Dict           =  0

        # sample eps ~ N(0, S_noise^2 * I)
        __UpperCAmelCase							:	str           =  random.split(__a		,      num=1					)
        __UpperCAmelCase							:	Union[str, Any]           =  self.config.s_noise * random.normal(key=__a		,      shape=sample.shape					)
        __UpperCAmelCase							:	Optional[Any]           =  sigma + gamma * sigma
        __UpperCAmelCase							:	Any           =  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

        return sample_hat, sigma_hat
      def 	UpperCAmelCase      (      self					:			List[Any]		,      __lowercase					:			List[str]		,      __lowercase					:			List[str]		,      __lowercase					:			List[Any]		,      __lowercase					:			List[Any]		,      __lowercase					:			Optional[Any]		,      __lowercase					:			Union[str, Any] = True		,      )							->      Union[FlaxKarrasVeOutput, Tuple]:
        __UpperCAmelCase							:	Union[str, Any]           =  sample_hat + sigma_hat * model_output
        __UpperCAmelCase							:	List[str]           =  (sample_hat - pred_original_sample) / sigma_hat
        __UpperCAmelCase							:	Union[str, Any]           =  sample_hat + (sigma_prev - sigma_hat) * derivative

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

        return FlaxKarrasVeOutput(prev_sample=__a		,      derivative=__a		,      state=__a					)
      def 	UpperCAmelCase      (      self					:			List[Any]		,      __lowercase					:			Any		,      __lowercase					:			Any		,      __lowercase					:			Tuple		,      __lowercase					:			Optional[Any]		,      __lowercase					:			List[Any]		,      __lowercase					:			Optional[int]		,      __lowercase					:			List[Any]		,      __lowercase					:			List[str] = True		,      )							->      Union[FlaxKarrasVeOutput, Tuple]:
        __UpperCAmelCase							:	Any           =  sample_prev + sigma_prev * model_output
        __UpperCAmelCase							:	int           =  (sample_prev - pred_original_sample) / sigma_prev
        __UpperCAmelCase							:	int           =  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=__a		,      derivative=__a		,      state=__a					)




      def 	UpperCAmelCase      (      self					:			int		,      __lowercase					:			Dict		,      __lowercase					:			List[str]		,      __lowercase					:			Optional[Any]		,      __lowercase					:			Dict					)							->      Tuple:
        raise NotImplementedError()




"""simple docstring"""





from __future__ import annotations



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		float:


					"""simple docstring"""





					_UpperCamelCase							 =  sorted(numsa + numsa	)
					_UpperCamelCase ,  _UpperCamelCase							 =  divmod(len(__snake_case	),     2	)
					if mod == 1:
										return all_numbers[div]
					else:
										return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				_a														=		[float(x) for x in input("""Enter the elements of first array: """).split()]
				_a														=		[float(x) for x in input("""Enter the elements of second array: """).split()]
				print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")






