




'''simple docstring'''







import math

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





def 		snake_case_							(_a			:							jnp.ndarray					,     _a			:							int					,     _a			:							float = 1					,     _a			:							float = 1					,     _a			:							float = 1.0E4					,     _a			:							bool = False					,     _a			:							float = 1.0					,     ):
    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     _a						(			nn.Module				):
       __a       :  int            =      32
       __a       :  jnp.dtype            =      jnp.floataa
       @nn.compact
       def __call__(			self		:			List[Any]     ,       lowercase		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	nn.Dense(self.time_embed_dim     ,       dtype=self.dtype     ,       name='''linear_1'''       )(lowercase       )
           UpperCAmelCase     =	nn.silu(lowercase       )
           UpperCAmelCase     =	nn.Dense(self.time_embed_dim     ,       dtype=self.dtype     ,       name='''linear_2'''       )(lowercase       )
           return temb






class     _a						(			nn.Module				):
       __a       :  int            =      32
       __a       :  bool            =      False
       __a       :  float            =      1
       @nn.compact
       def __call__(			self		:			Union[str, Any]     ,       lowercase		:			Tuple       ):



           '''simple docstring'''

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










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A									={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A									=[
       'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TableTransformerForObjectDetection',
       'TableTransformerModel',
       'TableTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_table_transformer import (
       TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TableTransformerConfig,
       TableTransformerOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_table_transformer import (
          TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TableTransformerForObjectDetection,
          TableTransformerModel,
          TableTransformerPreTrainedModel,
      )

else:
   import sys

   A									=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




