


"""simple docstring"""






import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     List[str]:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     LxmertConfig.from_json_file(_lowerCamelCase       )
   print(F"""Building PyTorch model from configuration: {config}"""       )
   _lowerCamelCase   :				int							=     LxmertForPreTraining(_lowerCamelCase       )

   # Load weights from tf checkpoint
   load_tf_weights_in_lxmert(_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
   torch.save(model.state_dict()			,		_lowerCamelCase       )


if __name__ == "__main__":
  _lowerCAmelCase							:    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(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  _lowerCAmelCase							:    List[Any]					   =	parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

import math





def      lowerCamelCase_					(							lowerCamelCase__       ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5      ,					int(math.sqrt(lowerCamelCase__       ) + 1       )      ,					6       ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True





def      lowerCamelCase_					(							lowerCamelCase__ = 1_0_0_0_1       ):

				try:
								lowerCamelCase_         =     int(lowerCamelCase__       )
				except (TypeError, ValueError):
								raise TypeError("Parameter nth must be int or castable to int."       ) from None
				if nth <= 0:
								raise ValueError("Parameter nth must be greater than or equal to one."       )
				lowerCamelCase_         =     []
				lowerCamelCase_         =     2
				while len(lowerCamelCase__       ) < nth:
								if is_prime(lowerCamelCase__       ):
												primes.append(lowerCamelCase__       )
												num += 1
								else:
												num += 1
				return primes[len(lowerCamelCase__       ) - 1]


if __name__ == "__main__":
			print(F"""{solution() = }""")


