




import math

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



def   lowerCAmelCase						(						_lowerCAmelCase							:      int  ,							_lowerCAmelCase							:      Union[str, Any]  ,							_lowerCAmelCase							:      Tuple = 1  ,							_lowerCAmelCase							:      Optional[Any] = 1  ,							_lowerCAmelCase							:      Optional[int] = 1.0E4  ,							_lowerCAmelCase							:      Tuple = False  ,							_lowerCAmelCase							:      str = 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'''
 UpperCAmelCase__							       =  float(embedding_dim // 2		)
 UpperCAmelCase__							       =  math.log(max_timescale / min_timescale		) / (num_timescales - freq_shift)
 UpperCAmelCase__							       =  min_timescale * jnp.exp(jnp.arange(A_  ,							dtype=jnp.floataa		) * -log_timescale_increment		)
 UpperCAmelCase__							       =  jnp.expand_dims(A_  ,							1		) * jnp.expand_dims(A_  ,							0		)

 # scale embeddings
 UpperCAmelCase__							       =  scale * emb

 if flip_sin_to_cos:
  UpperCAmelCase__							       =  jnp.concatenate([jnp.cos(A_		), jnp.sin(A_		)]  ,							axis=1		)
 else:
  UpperCAmelCase__							       =  jnp.concatenate([jnp.sin(A_		), jnp.cos(A_		)]  ,							axis=1		)
 UpperCAmelCase__							       =  jnp.reshape(A_  ,							[jnp.shape(A_		)[0], embedding_dim]		)
 return signal
class 	_UpperCamelCase					( nn.Module						):
      UpperCAmelCase_						=						32
      UpperCAmelCase_						=						jnp.floataa
      @nn.compact
      def __call__(	self      :int       ,					lowerCamelCase      :Union[str, Any]					)   ->	Union[str, Any]:
       UpperCAmelCase__							       =  nn.Dense(self.time_embed_dim       ,					dtype=self.dtype       ,					name="linear_1"					)(UpperCamelCase__					)
       UpperCAmelCase__							       =  nn.silu(UpperCamelCase__					)
       UpperCAmelCase__							       =  nn.Dense(self.time_embed_dim       ,					dtype=self.dtype       ,					name="linear_2"					)(UpperCamelCase__					)
       return temb



class 	_UpperCamelCase					( nn.Module						):
      UpperCAmelCase_						=						32
      UpperCAmelCase_						=						False
      UpperCAmelCase_						=						1
      @nn.compact
      def __call__(	self      :Any       ,					lowerCamelCase      :Optional[Any]					)   ->	Tuple:
       return get_sinusoidal_embeddings(
           UpperCamelCase__       ,					embedding_dim=self.dim       ,					flip_sin_to_cos=self.flip_sin_to_cos       ,					freq_shift=self.freq_shift					)







import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu




@require_cpu
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: List[Any]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       torch.nn.Linear(10			,							10    )
    __magic_name__				     =       torch.optim.SGD(model.parameters()			,							0.1    )
    __magic_name__				     =       Accelerator()
    __magic_name__				     =       accelerator.prepare(UpperCamelCase__    )
    try:
      pickle.loads(pickle.dumps(UpperCamelCase__    )    )
    except Exception as e:
      self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
    AcceleratorState._reset_state()

