"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   MobileBertConfig.from_json_file(SCREAMING_SNAKE_CASE_     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   MobileBertForPreTraining(SCREAMING_SNAKE_CASE_     )
		# Load weights from tf checkpoint
		lowerCAmelCase__        =   load_tf_weights_in_mobilebert(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_     )
		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
			__lowerCAmelCase													=						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(
			    "--mobilebert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained MobileBERT 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													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





"""simple docstring"""




from math import pi, sqrt







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		if num <= 0:
				raise ValueError("""math domain error"""     )
		if num > 1_71.5:
				raise OverflowError("""math range error"""     )
		elif num - int(lowerCamelCase__     ) not in (0, 0.5):
				raise NotImplementedError("""num must be an integer or a half-integer"""     )
		elif num == 0.5:
				return sqrt(lowerCamelCase__     )
		else:
				return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		assert gamma(0.5     ) == sqrt(lowerCamelCase__     )
		assert gamma(1     ) == 1.0
		assert gamma(2     ) == 1.0


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			__lowerCAmelCase    :							Dict													=						1.0
			while num:
						__lowerCAmelCase    :							Any													=						float(input("Gamma of: "))
						print(F"gamma({num}) = {gamma(num)}")
						print("\nEnter 0 to exit...")




