



import math

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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ = 1   ,   UpperCAmelCase__ = 1   ,   UpperCAmelCase__ = 1.0E4   ,   UpperCAmelCase__ = False   ,   UpperCAmelCase__ = 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"""
      lowerCamelCase								=		float(embedding_dim // 2 )
      lowerCamelCase								=		math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
      lowerCamelCase								=		min_timescale * jnp.exp(jnp.arange(UpperCAmelCase__   ,   dtype=jnp.floataa ) * -log_timescale_increment )
      lowerCamelCase								=		jnp.expand_dims(UpperCAmelCase__   ,   1 ) * jnp.expand_dims(UpperCAmelCase__   ,   0 )

      # scale embeddings
      lowerCamelCase								=		scale * emb

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





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     _A       =			32
     _A       =			jnp.floataa







     @nn.compact
     def __call__(self				,			__a    ):






           '''simple docstring'''



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





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     _A       =			32
     _A       =			False
     _A       =			1







     @nn.compact
     def __call__(self				,			__a    ):






           '''simple docstring'''



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




from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_						:     int	     =		{'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Dict	     =		[
         'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'VanForImageClassification',
         'VanModel',
         'VanPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_van import (
              VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              VanForImageClassification,
              VanModel,
              VanPreTrainedModel,
          )

else:
     import sys

     a_						:     Union[str, Any]	     =		_LazyModule(__name__, globals()['__file__'], _import_structure)