
from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__a  			=					logging.get_logger(__name__)
class  lowercase__:



	"""simple docstring"""




	a			:str
	a			:str         =	None
	@staticmethod
	def 						_lowercase  (       )       ->     Optional[int]:
					raise NotImplementedError
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			int       )       ->     Union[str, Any]:
					raise NotImplementedError
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     Optional[int]:
					raise NotImplementedError
	def 						_lowercase  (       self  :			Tuple       )       ->     Union[str, Any]:
					if not self.is_available():
									raise RuntimeError(
									    f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''       )




	@classmethod
	def 						_lowercase  (       cls  :			Dict       )       ->     Dict:
					return f'''`pip install {cls.pip_package or cls.name}`'''

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Any         =	'optuna'
	@staticmethod
	def 						_lowercase  (       )       ->     str:
					return is_optuna_available()
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			int       )       ->     str:
					return run_hp_search_optuna(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       )       ->     str:
					return default_hp_space_optuna(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Tuple         =	'ray'
	a			:Optional[int]         =	'\'ray[tune]\''
	@staticmethod
	def 						_lowercase  (       )       ->     Any:
					return is_ray_available()
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			Any       )       ->     str:
					return run_hp_search_ray(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     int:
					return default_hp_space_ray(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:List[str]         =	'sigopt'
	@staticmethod
	def 						_lowercase  (       )       ->     Union[str, Any]:
					return is_sigopt_available()
	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Union[str, Any]:
					return run_hp_search_sigopt(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Tuple:
					return default_hp_space_sigopt(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:str         =	'wandb'
	@staticmethod
	def 						_lowercase  (       )       ->     str:
					return is_wandb_available()
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Tuple:
					return run_hp_search_wandb(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     List[Any]:
					return default_hp_space_wandb(SCREAMING_SNAKE_CASE_       )

__a  			=					{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
				if len(snake_case__					) > 0:
								lowercase_       =					available_backends[0].name
								if len(snake_case__					) > 1:
												logger.info(
												    F'''{len(snake_case__					)} hyperparameter search backends available. Using {name} as the default.'''					)
								return name
				raise RuntimeError(
				    '''No hyperparameter search backend available.\n'''
				    + '''\n'''.join(
				        F''' - To install {backend.name} run {backend.pip_install()}'''
				        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()					)					)








import math

import flax.linen as nn
import jax.numpy as jnp







def        SCREAMING_SNAKE_CASE_						(			__A :			jnp.ndarray  ,    __A :			int  ,    __A :			float = 1  ,    __A :			float = 1  ,    __A :			float = 1.0e4  ,    __A :			bool = False  ,    __A :			float = 1.0  ,    )	->		jnp.ndarray:

       """simple docstring"""

       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
       a_					:		int							=  float(embedding_dim // 2				)
       a_					:		str							=  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
       a_					:		Optional[int]							=  min_timescale * jnp.exp(jnp.arange(__A  ,    dtype=jnp.floataa				) * -log_timescale_increment				)
       a_					:		Optional[int]							=  jnp.expand_dims(__A  ,    1				) * jnp.expand_dims(__A  ,    0				)

       # scale embeddings
       a_					:		str							=  scale * emb

       if flip_sin_to_cos:
              a_					:		str							=  jnp.concatenate([jnp.cos(__A				), jnp.sin(__A				)]  ,    axis=1				)
       else:
              a_					:		Any							=  jnp.concatenate([jnp.sin(__A				), jnp.cos(__A				)]  ,    axis=1				)
       a_					:		Optional[int]							=  jnp.reshape(__A  ,    [jnp.shape(__A				)[0], embedding_dim]				)
       return signal
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     jnp.dtype        =						jnp.floataa
      @nn.compact
      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Optional[Any]							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_1'     )(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.silu(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_2'     )(SCREAMING_SNAKE_CASE__     )
             return temb
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     bool        =						False
      snake_case__				:     float        =						1
      @nn.compact
      def __call__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Tuple:
             return get_sinusoidal_embeddings(
                 SCREAMING_SNAKE_CASE__     ,							embedding_dim=self.dim     ,							flip_sin_to_cos=self.flip_sin_to_cos     ,							freq_shift=self.freq_shift     )


