



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase):
					# Initialise PyTorch model
					UpperCamelCase_		  =   AlbertConfig.from_json_file(_lowerCAmelCase)
					print(f"""Building PyTorch model from configuration: {config}""")
					UpperCamelCase_		  =   AlbertForPreTraining(_lowerCAmelCase)

					# Load weights from tf checkpoint
					load_tf_weights_in_albert(_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase)

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


if __name__ == "__main__":
	UpperCAmelCase   :							Optional[int]							 =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(
	    """--albert_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained ALBERT 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."""
	)
	UpperCAmelCase   :							List[Any]							 =parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





UpperCAmelCase   :							Any							 =0  # The first color of the flag.
UpperCAmelCase   :							Optional[int]							 =1  # The second color of the flag.
UpperCAmelCase   :							Optional[Any]							 =2  # The third color of the flag.
UpperCAmelCase   :							Union[str, Any]							 =(red, white, blue)



def    _lowerCAmelCase    (_lowerCAmelCase):
					if not sequence:
										return []
					if len(_lowerCAmelCase) == 1:
										return list(_lowerCAmelCase)
					UpperCamelCase_		  =   0
					UpperCamelCase_		  =   len(_lowerCAmelCase) - 1
					UpperCamelCase_		  =   0
					while mid <= high:
										if sequence[mid] == colors[0]:
															UpperCamelCase_  ,    UpperCamelCase_		  =   sequence[mid], sequence[low]
															low += 1
															mid += 1
										elif sequence[mid] == colors[1]:
															mid += 1
										elif sequence[mid] == colors[2]:
															UpperCamelCase_  ,    UpperCamelCase_		  =   sequence[high], sequence[mid]
															high -= 1
										else:
															UpperCamelCase_		  =   f"""The elements inside the sequence must contains only {colors} values"""
															raise ValueError(_lowerCAmelCase)
					return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	UpperCAmelCase   :							Tuple							 =input("""Enter numbers separated by commas:\n""").strip()
	UpperCAmelCase   :							Any							 =[int(item.strip()) for item in user_input.split(""",""")]
	print(F"{dutch_national_flag_sort(unsorted)}")
