





'''simple docstring'''
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  int     ) ->      list:



       """simple docstring"""






       __UpperCamelCase       =  word.split()

       def justify(__lowercase						:  list				,  __lowercase						:  int				,  __lowercase						:  int     ) -> str:
              __UpperCamelCase       =  max_width - width
              __UpperCamelCase       =  len(__lowercase     )
              if len(__lowercase     ) == 1:
                     # if there is only word in line
                     # just insert overall_spaces_count for the remainder of line
                     return line[0] + " " * overall_spaces_count
              else:
                     __UpperCamelCase       =  words_count - 1
                     # num_spaces_between_words_list[i] : tells you to insert
                     # num_spaces_between_words_list[i] spaces
                     # after word on line[i]
                     __UpperCamelCase       =  spaces_to_insert_between_words * [
                         overall_spaces_count // spaces_to_insert_between_words
                     ]
                     __UpperCamelCase       =  (
                         overall_spaces_count % spaces_to_insert_between_words
                     )
                     # distribute spaces via round robin to the left words
                     for i in range(__lowercase     ):
                            num_spaces_between_words_list[i] += 1
                     __UpperCamelCase       =  []
                     for i in range(__lowercase     ):
                            # add the word
                            aligned_words_list.append(line[i]     )
                            # add the spaces to insert
                            aligned_words_list.append(num_spaces_between_words_list[i] * ' '     )
                     # just add the last word to the sentence
                     aligned_words_list.append(line[-1]     )
                     # join the aligned words list to form a justified line
                     return "".join(__lowercase     )

       __UpperCamelCase       =  []
       __UpperCamelCase       =  []
       __UpperCamelCase       =  0
       for word in words:
              if width + len(__lowercase     ) + len(__lowercase     ) <= max_width:
                     # keep adding words until we can fill out max_width
                     # width = sum of length of all words (without overall_spaces_count)
                     # len(word) = length of current word
                     # len(line) = number of overall_spaces_count to insert between words
                     line.append(__lowercase     )
                     width += len(__lowercase     )
              else:
                     # justify the line and add it to result
                     answer.append(justify(__lowercase				,  __lowercase				,  __lowercase     )     )
                     # reset new line and new width
                     __UpperCamelCase       ,						__UpperCamelCase       =  [word], len(__lowercase     )
       __UpperCamelCase       =  max_width - width - len(__lowercase     )
       answer.append(' '.join(__lowercase     ) + (remaining_spaces + 1) * ' '     )
       return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()




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     )


