





"""simple docstring"""
import math

import tensorflow as tf
from packaging import version



def 					__lowerCamelCase       (							a_					:		Optional[Any]							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :str		    =  tf.convert_to_tensor(lowerCamelCase__							)
      __SCREAMING_SNAKE_CASE       :int		    =  0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0							)			,       x.dtype							)							))

      return x * cdf



def 					__lowerCamelCase       (							a_					:		Dict							)      ->				Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :Dict		    =  tf.convert_to_tensor(lowerCamelCase__							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  tf.cast(math.pi			,       x.dtype							)
      __SCREAMING_SNAKE_CASE       :str		    =  tf.cast(0.04_4715			,       x.dtype							)
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi							) * (x + coeff * tf.pow(lowerCamelCase__			,       3							))							))

      return x * cdf



def 					__lowerCamelCase       (							a_					:		Any							)      ->				Any:
      __SCREAMING_SNAKE_CASE       :str		    =  tf.convert_to_tensor(lowerCamelCase__							)

      return x * tf.tanh(tf.math.softplus(lowerCamelCase__							)							)



def 					__lowerCamelCase       (							a_					:		List[Any]							)      ->				str:
      __SCREAMING_SNAKE_CASE       :str		    =  tf.convert_to_tensor(lowerCamelCase__							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  tf.cast(0.04_4715			,       x.dtype							)
      __SCREAMING_SNAKE_CASE       :str		    =  tf.cast(0.79_7884_5608			,       x.dtype							)

      return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)							))



def 					__lowerCamelCase       (							a_					:		str							)      ->				Dict:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  tf.convert_to_tensor(lowerCamelCase__							)
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  tf.cast(1.702			,       x.dtype							)
      return x * tf.math.sigmoid(coeff * x							)



def 					__lowerCamelCase       (							a_					:		List[str]							)      ->				Optional[int]:
      return tf.clip_by_value(_gelu(lowerCamelCase__							)			,       -10			,       10							)



def 					__lowerCamelCase       (							a_					:		Any			,       a_					:		Optional[int]=-1							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :int		    =  tf.split(lowerCamelCase__			,       2			,       axis=lowerCamelCase__							)
      return a * tf.math.sigmoid(lowerCamelCase__							)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):



 def 					__lowerCamelCase       (							a_					:		List[str]							)      ->				str:
       return tf.keras.activations.gelu(lowerCamelCase__			,       approximate=lowerCamelCase__							)

 lowerCamelCase_       =		tf.keras.activations.gelu







 lowerCamelCase_       =		approximate_gelu_wrap
else:
 lowerCamelCase_       =		_gelu
 lowerCamelCase_       =		_gelu_new


lowerCamelCase_       =		{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}







def 					__lowerCamelCase       (							a_					:		List[Any]							)      ->				Optional[int]:




      if activation_string in ACTaFN:
            return ACTaFN[activation_string]
      else:
            raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()							)}'''							)






"""simple docstring"""
from torch import nn






class  _SCREAMING_SNAKE_CASE(       nn.Module  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             super().__init__()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  class_size
             __SCREAMING_SNAKE_CASE       :str		    =  embed_size
             # self.mlp1 = nn.Linear(embed_size, embed_size)
             # self.mlp2 = (nn.Linear(embed_size, class_size))
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  nn.Linear(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.mlp(SCREAMING_SNAKE_CASE__							)
             return logits