
'''simple docstring'''





import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
 import tensorflow as tf

 from transformers.activations_tf import get_tf_activation


@require_tf
class 			UpperCamelCase__	(     unittest.TestCase):






 def 						lowercase_    (      self	:Dict      )					->      Any:







       '''simple docstring'''



       __A													=      tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]      )
       __A													=      get_tf_activation('gelu'      )
       __A													=      get_tf_activation('gelu_10'      )

       __A													=      gelu(_A      )
       __A													=      geluaa(_A      )

       __A													=      tf.where(y_gelu_aa < 10.0						,					1.0						,					0.0      )

       self.assertEqual(tf.math.reduce_max(_A      ).numpy().item()						,					10.0      )
       self.assertTrue(np.allclose(y_gelu * clipped_mask						,					y_gelu_aa * clipped_mask      )      )






 def 						lowercase_    (      self	:Union[str, Any]      )					->      int:







       '''simple docstring'''



       get_tf_activation('gelu'      )
       get_tf_activation('gelu_10'      )
       get_tf_activation('gelu_fast'      )
       get_tf_activation('gelu_new'      )
       get_tf_activation('glu'      )
       get_tf_activation('mish'      )
       get_tf_activation('quick_gelu'      )
       get_tf_activation('relu'      )
       get_tf_activation('sigmoid'      )
       get_tf_activation('silu'      )
       get_tf_activation('swish'      )
       get_tf_activation('tanh'      )
       with self.assertRaises(_A      ):
             get_tf_activation('bogus'      )
       with self.assertRaises(_A      ):
             get_tf_activation(_A      )








import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[Any]       =    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--convbert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ConvBERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 lowerCamelCase_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
