
import math

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



def 		UpperCamelCase     (       _UpperCAmelCase  :   jnp.ndarray ,			_UpperCAmelCase  :   int ,			_UpperCAmelCase  :   float = 1 ,			_UpperCAmelCase  :   float = 1 ,			_UpperCAmelCase  :   float = 1.0e4 ,			_UpperCAmelCase  :   bool = False ,			_UpperCAmelCase  :   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"""
						_lowercase       :	Optional[Any]           =   float(embedding_dim // 2  )
						_lowercase       :	Union[str, Any]           =   math.log(max_timescale / min_timescale  ) / (num_timescales - freq_shift)
						_lowercase       :	List[Any]           =   min_timescale * jnp.exp(jnp.arange(_snake_case ,			dtype=jnp.floataa  ) * -log_timescale_increment  )
						_lowercase       :	str           =   jnp.expand_dims(_snake_case ,			1  ) * jnp.expand_dims(_snake_case ,			0  )

						# scale embeddings
						_lowercase       :	Any           =   scale * emb

						if flip_sin_to_cos:
												_lowercase       :	Tuple           =   jnp.concatenate([jnp.cos(_snake_case  ), jnp.sin(_snake_case  )] ,			axis=1  )
						else:
												_lowercase       :	Dict           =   jnp.concatenate([jnp.sin(_snake_case  ), jnp.cos(_snake_case  )] ,			axis=1  )
						_lowercase       :	Optional[Any]           =   jnp.reshape(_snake_case ,			[jnp.shape(_snake_case  )[0], embedding_dim]  )
						return signal




class   __lowercase						(						nn.Module       ):
	_A       =   32
	_A       =   jnp.floataa






	@nn.compact
	def __call__(self				: Union[str, Any]      ,	snake_case				: str    ) ->			Tuple:
							_lowercase       :	Optional[Any]           =   nn.Dense(self.time_embed_dim      ,	dtype=self.dtype      ,	name="linear_1"    )(_UpperCAmelCase    )
							_lowercase       :	Dict           =   nn.silu(_UpperCAmelCase    )
							_lowercase       :	List[str]           =   nn.Dense(self.time_embed_dim      ,	dtype=self.dtype      ,	name="linear_2"    )(_UpperCAmelCase    )
							return temb




class   __lowercase						(						nn.Module       ):
	_A       =   32
	_A       =   False
	_A       =   1






	@nn.compact
	def __call__(self				: Optional[Any]      ,	snake_case				: str    ) ->			int:
							return get_sinusoidal_embeddings(
							    _UpperCAmelCase      ,	embedding_dim=self.dim      ,	flip_sin_to_cos=self.flip_sin_to_cos      ,	freq_shift=self.freq_shift    )










"""simple docstring"""

from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      for param in module.parameters():
            _A     =  False






def 		_snake_case	(   )       ->   Tuple:






      '''simple docstring'''


      _A     =  'cuda' if torch.cuda.is_available() else 'cpu'
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            _A     =  'mps'
      if device == "mps":
            print(
                'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                ' with generations.'				)
      return device






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  plt.imshow(_snake_case				)
      fig.axes.get_xaxis().set_visible(_snake_case				)
      fig.axes.get_yaxis().set_visible(_snake_case				)
      plt.show()







def 		_snake_case	(   )       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  datetime.now()
      _A     =  current_time.strftime('%H:%M:%S'				)
      return timestamp

