

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       :
      # setable values
      __a								=	None
      __a								=	None
      __a								=	None  # sigma(t_i)





      @classmethod
      def 					_lowerCamelCase  (  cls		)   ->					Any:
         return cls()






@dataclass
class 		_A       (			__a					):
      __a								=	42
      __a								=	42
      __a								=	42






class 		_A       (			__a				,       __a					):





      @property
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         return True





      @register_to_config
      def __init__(  self    ,						SCREAMING_SNAKE_CASE__ = 0.02    ,						SCREAMING_SNAKE_CASE__ = 100    ,						SCREAMING_SNAKE_CASE__ = 1.0_07    ,						SCREAMING_SNAKE_CASE__ = 80    ,						SCREAMING_SNAKE_CASE__ = 0.05    ,						SCREAMING_SNAKE_CASE__ = 50    ,						)   ->					Dict:
         pass





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         return KarrasVeSchedulerState.create()





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = ()		)   ->					KarrasVeSchedulerState:
         lowerCamelCase__						=			jnp.arange(0    ,						SCREAMING_SNAKE_CASE__		)[::-1].copy()
         lowerCamelCase__						=			[
             (
                 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=SCREAMING_SNAKE_CASE__    ,						schedule=jnp.array(SCREAMING_SNAKE_CASE__    ,						dtype=jnp.floataa		)    ,						timesteps=SCREAMING_SNAKE_CASE__    ,						)





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

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

         return sample_hat, sigma_hat





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

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

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = True    ,						)   ->					Union[FlaxKarrasVeOutput, Tuple]:
         lowerCamelCase__						=			sample_prev + sigma_prev * model_output
         lowerCamelCase__						=			(sample_prev - pred_original_sample) / sigma_prev
         lowerCamelCase__						=			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=SCREAMING_SNAKE_CASE__    ,						derivative=SCREAMING_SNAKE_CASE__    ,						state=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					str:
         raise NotImplementedError()







"""simple docstring"""




# Algorithm for the pigeonhole sorting





def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			min(A__   )  # min() finds the minimum value
   lowerCamelCase__						=			max(A__   )  # max() finds the maximum value

   lowerCamelCase__						=			max_val - min_val + 1  # size is difference of max and min values plus one

   # list of pigeonholes of size equal to the variable size
   lowerCamelCase__						=			[0] * size

   # Populate the pigeonholes.
   for x in a:
      assert isinstance(A__	,    A__   ), "integers only please"
      holes[x - min_val] += 1

   # Putting the elements back into the array in an order.
   lowerCamelCase__						=			0
   for count in range(A__   ):
      while holes[count] > 0:
         holes[count] -= 1
         lowerCamelCase__						=			count + min_val
         i += 1



def 	UpperCAmelCase__   (				)      ->      Optional[int]:




   """simple docstring"""
   lowerCamelCase__						=			[8, 3, 2, 7, 4, 6, 8]
   pigeonhole_sort(A__   )
   print("Sorted order is:"	,    " ".join(A__   )   )


if __name__ == "__main__":
     main()




