




"""simple docstring"""




import math

import tensorflow as tf
from packaging import version



def     a__ (				snake_case__				)						->					int:
			lowerCamelCase			      =					tf.convert_to_tensor(SCREAMING_SNAKE_CASE__				)
			lowerCamelCase			      =					0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0				)       , x.dtype				)				))

			return x * cdf



def     a__ (				snake_case__				)						->					Optional[Any]:
			lowerCamelCase			      =					tf.convert_to_tensor(SCREAMING_SNAKE_CASE__				)
			lowerCamelCase			      =					tf.cast(math.pi       , x.dtype				)
			lowerCamelCase			      =					tf.cast(0.04_4715       , x.dtype				)
			lowerCamelCase			      =					0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi				) * (x + coeff * tf.pow(SCREAMING_SNAKE_CASE__       , 3				))				))

			return x * cdf



def     a__ (				snake_case__				)						->					Optional[Any]:
			lowerCamelCase			      =					tf.convert_to_tensor(SCREAMING_SNAKE_CASE__				)

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



def     a__ (				snake_case__				)						->					int:
			lowerCamelCase			      =					tf.convert_to_tensor(SCREAMING_SNAKE_CASE__				)
			lowerCamelCase			      =					tf.cast(0.04_4715       , x.dtype				)
			lowerCamelCase			      =					tf.cast(0.79_7884_5608       , x.dtype				)

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



def     a__ (				snake_case__				)						->					Union[str, Any]:
			lowerCamelCase			      =					tf.convert_to_tensor(SCREAMING_SNAKE_CASE__				)
			lowerCamelCase			      =					tf.cast(1.702       , x.dtype				)
			return x * tf.math.sigmoid(coeff * x				)



def     a__ (				snake_case__				)						->					Optional[int]:
			return tf.clip_by_value(_gelu(SCREAMING_SNAKE_CASE__				)       , -10       , 10				)



def     a__ (				snake_case__       , snake_case__=-1				)						->					List[Any]:
			lowerCamelCase      ,   lowerCamelCase			      =					tf.split(SCREAMING_SNAKE_CASE__       , 2       , axis=SCREAMING_SNAKE_CASE__				)
			return a * tf.math.sigmoid(SCREAMING_SNAKE_CASE__				)


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



					def     a__ (				snake_case__				)						->					Dict:
								return tf.keras.activations.gelu(SCREAMING_SNAKE_CASE__       , approximate=SCREAMING_SNAKE_CASE__				)

					lowerCAmelCase							:			int     							=							tf.keras.activations.gelu


					lowerCAmelCase							:			List[str]     							=							approximate_gelu_wrap
else:
					lowerCAmelCase							:			str     							=							_gelu
					lowerCAmelCase							:			List[str]     							=							_gelu_new


lowerCAmelCase							:			Tuple     							=							{
    """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     a__ (				snake_case__				)						->					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()				)}'				)






from typing import Union

import fire
import torch
from tqdm import tqdm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str = "cpu"    ,			SCREAMING_SNAKE_CASE__			:	Union[str, None] = None      )							->      None:





    '''simple docstring'''






    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location=SCREAMING_SNAKE_CASE__      )
    for k, v in tqdm(state_dict.items()      ):
        if not isinstance(SCREAMING_SNAKE_CASE__    ,			torch.Tensor      ):
            raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'      )
        A__          =   v.half()
    if save_path is None:  # overwrite src_path
        A__          =   src_path
    torch.save(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     fire.Fire(convert)

