






'''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 						A__			(				__lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			Dict						):
      lowerCamelCase__         =  LxmertConfig.from_json_file(lowercase__						)
      print(F'''Building PyTorch model from configuration: {config}'''						)
      lowerCamelCase__         =  LxmertForPreTraining(lowercase__						)

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

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


if __name__ == "__main__":
       UpperCamelCase    :      Optional[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.'
       )
       UpperCamelCase    :      Optional[int]  			=		parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''







from __future__ import annotations

import math
def 						A__			(				__lowerCAmelCase		:			int						):

						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


UpperCamelCase    :      str  			=		[num for num in range(3, 10_00_01, 2) if not is_prime(num)]
def 						A__			(				__lowerCAmelCase		:			int						):
						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												raise ValueError("""n must be an integer"""						)
						if n <= 0:
												raise ValueError("""n must be >= 0"""						)

						lowerCamelCase__         =  []
						for num in range(len(__lowerCAmelCase						)						):
												lowerCamelCase__         =  0
												while 2 * i * i <= odd_composites[num]:
																		lowerCamelCase__         =  odd_composites[num] - 2 * i * i
																		if is_prime(__lowerCAmelCase						):
																								break
																		i += 1
												else:
																		list_nums.append(odd_composites[num]						)
																		if len(__lowerCAmelCase						) == n:
																								return list_nums

						return []
def 						A__			(				):
						return compute_nums(1						)[0]


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

