




"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					):
			# Initialise PyTorch model
			__lowercase	:       Optional[int]				=   RoFormerConfig.from_json_file(__UpperCamelCase					)
			print(f"""Building PyTorch model from configuration: {config}"""					)
			__lowercase	:       Union[str, Any]				=   RoFormerForMaskedLM(__UpperCamelCase					)

			# Load weights from tf checkpoint
			load_tf_weights_in_roformer(__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					)

			# Save pytorch-model
			print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
			torch.save(model.state_dict()					,							__UpperCamelCase					,							_use_new_zipfile_serialization=__UpperCamelCase					)


if __name__ == "__main__":
			a_            =   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.'
			)
			a_            =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









"""simple docstring"""




a_            =   1_0_0_0







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return x * x







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase = 4					):
			assert callable(
			    __UpperCamelCase					), f"""the function(object) passed should be callable your input : {function}"""
			assert isinstance(__UpperCamelCase					,							(float, int)					), f"""a should be float or integer your input : {a}"""
			assert isinstance(function(__UpperCamelCase					)					,							(float, int)					), (
			    "the function should return integer or float return type of your function, "
			    f"""{type(__UpperCamelCase					)}"""
			)
			assert isinstance(__UpperCamelCase					,							(float, int)					), f"""b should be float or integer your input : {b}"""
			assert (
			    isinstance(__UpperCamelCase					,							__UpperCamelCase					) and precision > 0
			), f"""precision should be positive integer your input : {precision}"""

			# just applying the formula of simpson for approximate integration written in
			# mentioned article in first comment of this file and above this function

			__lowercase	:       Optional[Any]				=   (b - a) / N_STEPS
			__lowercase	:       Optional[int]				=   function(__UpperCamelCase					) + function(__UpperCamelCase					)

			for i in range(1					,							__UpperCamelCase					):
						__lowercase	:       Union[str, Any]				=   a + h * i
						result += function(__UpperCamelCase					) * (4 if i % 2 else 2)

			result *= h / 3
			return round(__UpperCamelCase					,							__UpperCamelCase					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



