
import math

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

def a__    (       A_,     A_,     A_ = 1,     A_ = 1,     A_ = 1.0e4,     A_ = False,     A_ = 1.0,     ):






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

  # scale embeddings
  __magic_name__				     =       scale * emb

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




class        UpperCAmelCase_			( nn.Module     ):


  '''simple docstring'''







  a__           =		32
  a__           =		jnp.floataa
  @nn.compact
  def __call__( self			: int			,							UpperCamelCase__			: Union[str, Any]    )			->	Union[str, Any]:







    """simple docstring"""






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







class        UpperCAmelCase_			( nn.Module     ):


  '''simple docstring'''







  a__           =		32
  a__           =		False
  a__           =		1
  @nn.compact
  def __call__( self			: Any			,							UpperCamelCase__			: Optional[Any]    )			->	Tuple:







    """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    )



import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       ArgumentParser(
      description=(
          """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
      )					)

  # Optional arguments for the launch helper
  parser.add_argument("""--num_cores""",     type=A_,     default=1,     help="""Number of TPU cores to use (1 or 8)."""					)

  # positional
  parser.add_argument(
      """training_script""",     type=A_,     help=(
          """The full path to the single TPU training """
          """program/script to be launched in parallel, """
          """followed by all the arguments for the """
          """training script"""
      ),     )

  # rest from the training program
  parser.add_argument("""training_script_args""",     nargs=A_					)

  return parser.parse_args()

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       parse_args()

  # Import training_script as a module.
  __magic_name__				     =       Path(args.training_script					)
  sys.path.append(str(script_fpath.parent.resolve()					)					)
  __magic_name__				     =       script_fpath.stem
  __magic_name__				     =       importlib.import_module(A_					)

  # Patch sys.argv
  __magic_name__				     =       [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores					)]

  xmp.spawn(mod._mp_fn,     args=(),     nprocs=args.num_cores					)


if __name__ == "__main__":
    main()

