



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__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		Optional[Any]			    =       LxmertConfig.from_json_file(lowercase__							)
						print(f'Building PyTorch model from configuration: {config}'							)
						_UpperCamelCase :		List[Any]			    =       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__":
					snake_case_			:     List[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.'
					)
					snake_case_			:     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 time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						if not arr:
												return None, None, 0
						if low == high:
												return low, high, arr[low]

						_UpperCamelCase :		str			    =       (low + high) // 2
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		Dict			    =       max_subarray(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		List[Any]			    =       max_subarray(UpperCAmelCase_  ,  mid + 1  ,  UpperCAmelCase_							)
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		Tuple			    =       max_cross_sum(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)
						if left_sum >= right_sum and left_sum >= cross_sum:
												return left_low, left_high, left_sum
						elif right_sum >= left_sum and right_sum >= cross_sum:
												return right_low, right_high, right_sum
						return cross_left, cross_right, cross_sum






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase    ,      _UpperCamelCase :		Optional[Any]			    =       float('-inf'							), -1
						_UpperCamelCase    ,      _UpperCamelCase :		int			    =       float('-inf'							), -1

						_UpperCamelCase :		int | float			    =       0
						for i in range(UpperCAmelCase_  ,  low - 1  ,  -1							):
												summ += arr[i]
												if summ > left_sum:
																		_UpperCamelCase :		Optional[int]			    =       summ
																		_UpperCamelCase :		Union[str, Any]			    =       i

						_UpperCamelCase :		List[Any]			    =       0
						for i in range(mid + 1  ,  high + 1							):
												summ += arr[i]
												if summ > right_sum:
																		_UpperCamelCase :		List[Any]			    =       summ
																		_UpperCamelCase :		List[str]			    =       i

						return max_left, max_right, (left_sum + right_sum)






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		List[str]			    =       [randint(1  ,  UpperCAmelCase_							) for _ in range(UpperCAmelCase_							)]
						_UpperCamelCase :		Optional[Any]			    =       time.time()
						max_subarray(UpperCAmelCase_  ,  0  ,  input_size - 1							)
						_UpperCamelCase :		str			    =       time.time()
						return end - start






def A__   (					):
						_UpperCamelCase :		Any			    =       [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
						_UpperCamelCase :		Dict			    =       [time_max_subarray(UpperCAmelCase_							) for input_size in input_sizes]
						print('No of Inputs\t\tTime Taken'							)
						for input_size, runtime in zip(UpperCAmelCase_  ,  UpperCAmelCase_							):
												print(UpperCAmelCase_  ,  '\t\t'  ,  UpperCAmelCase_							)
						plt.plot(UpperCAmelCase_  ,  UpperCAmelCase_							)
						plt.xlabel('Number of Inputs'							)
						plt.ylabel('Time taken in seconds'							)
						plt.show()


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

					testmod()


