





import math

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



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE = 1 ,						_SCREAMING_SNAKE_CASE = 1 ,						_SCREAMING_SNAKE_CASE = 1.0E4 ,						_SCREAMING_SNAKE_CASE = False ,						_SCREAMING_SNAKE_CASE = 1.0 ,						) ->   Optional[Any]:


      '''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"""
      SCREAMING_SNAKE_CASE		=						float(embedding_dim // 2	)
      SCREAMING_SNAKE_CASE		=						math.log(max_timescale / min_timescale	) / (num_timescales - freq_shift)
      SCREAMING_SNAKE_CASE		=						min_timescale * jnp.exp(jnp.arange(_A ,						dtype=jnp.floataa	) * -log_timescale_increment	)
      SCREAMING_SNAKE_CASE		=						jnp.expand_dims(_A ,						1	) * jnp.expand_dims(_A ,						0	)

      # scale embeddings
      SCREAMING_SNAKE_CASE		=						scale * emb

      if flip_sin_to_cos:
            SCREAMING_SNAKE_CASE		=						jnp.concatenate([jnp.cos(_A	), jnp.sin(_A	)] ,						axis=1	)
      else:
            SCREAMING_SNAKE_CASE		=						jnp.concatenate([jnp.sin(_A	), jnp.cos(_A	)] ,						axis=1	)
      SCREAMING_SNAKE_CASE		=						jnp.reshape(_A ,						[jnp.shape(_A	)[0], embedding_dim]	)
      return signal

class 		UpperCamelCase__ (      nn.Module   ):



     '''simple docstring'''

     __snake_case    :	List[Any]			=			32
     __snake_case    :	Dict			=			jnp.floataa






     @nn.compact
     def __call__(							self						:    Dict						,lowerCamelCase__						:    Optional[Any]       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						nn.Dense(self.time_embed_dim						,dtype=self.dtype						,name="""linear_1"""       )(UpperCamelCase__       )
           SCREAMING_SNAKE_CASE		=						nn.silu(UpperCamelCase__       )
           SCREAMING_SNAKE_CASE		=						nn.Dense(self.time_embed_dim						,dtype=self.dtype						,name="""linear_2"""       )(UpperCamelCase__       )
           return temb

class 		UpperCamelCase__ (      nn.Module   ):



     '''simple docstring'''

     __snake_case    :	str			=			32
     __snake_case    :	Tuple			=			False
     __snake_case    :	Tuple			=			1






     @nn.compact
     def __call__(							self						:    List[str]						,lowerCamelCase__						:    Optional[int]       )     ->		Optional[int]:




           '''simple docstring'''


           return get_sinusoidal_embeddings(
               UpperCamelCase__						,embedding_dim=self.dim						,flip_sin_to_cos=self.flip_sin_to_cos						,freq_shift=self.freq_shift       )








def __UpperCamelCase			(       _A  ):
						if not numbers:
												return 0

						if not isinstance(_A ,  (list, tuple)  ) or not all(
						    isinstance(_A ,  _A  ) for number in numbers  ):
												raise ValueError('''numbers must be an iterable of integers'''  )

						lowerCAmelCase_      			=  lowerCAmelCase_      			=  lowerCAmelCase_      			=  numbers[0]

						for i in range(1 ,  len(_A  )  ):
												# update the maximum and minimum subarray products
												lowerCAmelCase_      			=  numbers[i]
												if number < 0:
																		lowerCAmelCase_			,					lowerCAmelCase_      			=  min_till_now, max_till_now
												lowerCAmelCase_      			=  max(_A ,  max_till_now * number  )
												lowerCAmelCase_      			=  min(_A ,  min_till_now * number  )

												# update the maximum product found till now
												lowerCAmelCase_      			=  max(_A ,  _A  )

						return max_prod




