
import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: Any       ,				SCREAMING_SNAKE_CASE			: Optional[Any]     )	->		List[str]:
				# Initialise PyTorch model
				__lowercase								=   RoFormerConfig.from_json_file(SCREAMING_SNAKE_CASE     )
				print(F"""Building PyTorch model from configuration: {config}"""     )
				__lowercase								=   RoFormerForMaskedLM(SCREAMING_SNAKE_CASE     )

				# Load weights from tf checkpoint
				load_tf_weights_in_roformer(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       ,				_use_new_zipfile_serialization=SCREAMING_SNAKE_CASE     )


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







def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Union[str, Any]       ,				SCREAMING_SNAKE_CASE			: Any     )	->		Tuple:
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				__lowercase								=   (boundary[1] - boundary[0]) / steps
				__lowercase								=   boundary[0]
				__lowercase								=   boundary[1]
				__lowercase								=   make_points(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				__lowercase								=   0.0
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				__lowercase								=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE     )
								cnt += 1
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				return y




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: Dict       ,				SCREAMING_SNAKE_CASE			: int     )	->		Any:
				__lowercase								=   a + h
				while x < (b - h):
								yield x
								__lowercase								=   x + h




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any     )	->		List[Any]:  # enter your function here
				__lowercase								=   (x - 0) * (x - 0)
				return y




def 				__SCREAMING_SNAKE_CASE  (						)	->		List[str]:
				__lowercase								=   0.0  # Lower bound of integration
				__lowercase								=   1.0  # Upper bound of integration
				__lowercase								=   10.0  # define number of steps or resolution
				__lowercase								=   [a, b]  # define boundary of integration
				__lowercase								=   method_a(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				print(F"""y = {y}"""     )


if __name__ == "__main__":
							main()





