import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
__magic_name__								=[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]







def  __UpperCamelCase    (						A							):
     # Test all the extensions added in the setup
     for file in FILES_TO_FIND:
          if not (transformers_path / file).exists():
               return False
     return True


if __name__ == "__main__":
     __magic_name__								=argparse.ArgumentParser()
     parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
     __magic_name__								=parser.parse_args()
     if args.check_lib:
          __magic_name__								=importlib.import_module('''transformers''')
          __magic_name__								=Path(transformers_module.__file__).parent
     else:
          __magic_name__								=Path.cwd() / '''build/lib/transformers'''
     if not test_custom_files_are_present(transformers_path):
          raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')




import math

import tensorflow as tf
from packaging import version







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0							) ,							x.dtype							)							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(math.pi ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi							) * (x + coeff * tf.pow(A ,							3							))							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)

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







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.79_78_84_56_08 ,							x.dtype							)

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







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(1.7_02 ,							x.dtype							)
     return x * tf.math.sigmoid(coeff * x							)







def  __UpperCamelCase    (						A							):
     return tf.clip_by_value(_gelu(A							) ,							-10 ,							10							)







def  __UpperCamelCase    (						A ,							A=-1							):
     UpperCamelCase__						,       UpperCamelCase__										=      tf.split(A ,							2 ,							axis=A							)
     return a * tf.math.sigmoid(A							)


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







     def  __UpperCamelCase    (						A							):
          return tf.keras.activations.gelu(A ,							approximate=A							)

     __magic_name__								=tf.keras.activations.gelu



     __magic_name__								=approximate_gelu_wrap
else:
     __magic_name__								=_gelu
     __magic_name__								=_gelu_new


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



