





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"""





       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  __UpperCAmelCase			(     cls						:						Optional[int]	)    ->       Optional[int]:
         return cls()





@dataclass
class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			jnp.ndarray
       SCREAMING_SNAKE_CASE_   :			jnp.ndarray
       SCREAMING_SNAKE_CASE_   :			KarrasVeSchedulerState





class 					A_					(   UpperCAmelCase       ,						UpperCAmelCase						):







       """simple docstring"""





       @property
       def  __UpperCAmelCase			(     self						:						str	)    ->       Dict:
         return True







       @register_to_config
       def __init__(     self						:						List[Any]    ,__A						:						float = 0.02    ,__A						:						float = 100    ,__A						:						float = 1.007    ,__A						:						float = 80    ,__A						:						float = 0.05    ,__A						:						float = 50    ,)    ->       Optional[Any]:
         pass







       def  __UpperCAmelCase			(     self						:						Dict	)    ->       Union[str, Any]:
         return KarrasVeSchedulerState.create()







       def  __UpperCAmelCase			(     self						:						List[str]    ,__A						:						KarrasVeSchedulerState    ,__A						:						int    ,__A						:						Tuple = ()	)    ->       KarrasVeSchedulerState:
         _lowercase						    =			jnp.arange(0    ,__A	)[::-1].copy()
         _lowercase						    =			[
             (
                 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						:						List[Any]    ,__A						:						KarrasVeSchedulerState    ,__A						:						jnp.ndarray    ,__A						:						float    ,__A						:						random.KeyArray    ,)    ->       Tuple[jnp.ndarray, float]:
         if self.config.s_min <= sigma <= self.config.s_max:
           _lowercase						    =			min(self.config.s_churn / state.num_inference_steps    ,2**0.5 - 1	)
         else:
           _lowercase						    =			0

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

         return sample_hat, sigma_hat







       def  __UpperCAmelCase			(     self						:						Union[str, Any]    ,__A						:						KarrasVeSchedulerState    ,__A						:						jnp.ndarray    ,__A						:						float    ,__A						:						float    ,__A						:						jnp.ndarray    ,__A						:						bool = True    ,)    ->       Union[FlaxKarrasVeOutput, Tuple]:
         _lowercase						    =			sample_hat + sigma_hat * model_output
         _lowercase						    =			(sample_hat - pred_original_sample) / sigma_hat
         _lowercase						    =			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]    ,__A						:						KarrasVeSchedulerState    ,__A						:						jnp.ndarray    ,__A						:						float    ,__A						:						float    ,__A						:						jnp.ndarray    ,__A						:						jnp.ndarray    ,__A						:						jnp.ndarray    ,__A						:						bool = True    ,)    ->       Union[FlaxKarrasVeOutput, Tuple]:
         _lowercase						    =			sample_prev + sigma_prev * model_output
         _lowercase						    =			(sample_prev - pred_original_sample) / sigma_prev
         _lowercase						    =			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    ,__A						:						KarrasVeSchedulerState    ,__A						:						Optional[Any]    ,__A						:						Optional[int]    ,__A						:						Tuple	)    ->       Optional[int]:
         raise NotImplementedError()

import math




class   __lowercase						:






	def       _a(self				: Dict      ,	snake_case				: list[list[float]]      ,	snake_case				: list[int]    ) ->			int:
							_lowercase       :	List[Any]           =   0.0
							_lowercase       :	Any           =   0.0
							for i in range(len(snake_case    )    ):
													da += math.pow((sample[i] - weights[0][i])      ,	2    )
													da += math.pow((sample[i] - weights[1][i])      ,	2    )
													return 0 if da > da else 1
							return 0






	def       _a(self				: Dict      ,	snake_case				: list[list[int | float]]      ,	snake_case				: list[int]      ,	snake_case				: int      ,	snake_case				: float    ) ->			list[list[int | float]]:
							for i in range(len(snake_case    )    ):
													weights[j][i] += alpha * (sample[i] - weights[j][i])
							return weights



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

						# weight initialization ( n, C )
						_lowercase       :	Tuple           =   [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

						# training
						_lowercase       :	List[Any]           =   SelfOrganizingMap()
						_lowercase       :	Any           =   3
						_lowercase       :	Optional[Any]           =   0.5

						for _ in range(_UpperCAmelCase  ):
												for j in range(len(_UpperCAmelCase  )  ):
																		# training sample
																		_lowercase       :	Optional[int]           =   training_samples[j]

																		# Compute the winning vector
																		_lowercase       :	Tuple           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

																		# Update the winning vector
																		_lowercase       :	str           =   self_organizing_map.update(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

    # classify test sample
						_lowercase       :	Any           =   [0, 0, 0, 1]
						_lowercase       :	Dict           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

						# results
						print(f"""Clusters that the test sample belongs to : {winner}"""  )
						print(f"""Weights that have been trained : {weights}"""  )


# running the main() function
if __name__ == "__main__":
							main()




