"""simple docstring"""




import random






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__   ,     A__   ,     A__		   =							[], [], []
							for element in data:
														if element < pivot:
																					less.append(UpperCamelCase__ )
														elif element > pivot:
																					greater.append(UpperCamelCase__ )
														else:
																					equal.append(UpperCamelCase__ )
							return less, equal, greater






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							if index >= len(UpperCamelCase__ ) or index < 0:
														return None

							A__		   =							items[random.randint(0				,			len(UpperCamelCase__ ) - 1 )]
							A__		   =							0
							A__   ,     A__   ,     A__		   =							_partition(UpperCamelCase__				,			UpperCamelCase__ )
							A__		   =							len(UpperCamelCase__ )
							A__		   =							len(UpperCamelCase__ )

							# index is the pivot
							if m <= index < m + count:
														return pivot
							# must be in smaller
							elif m > index:
														return quick_select(UpperCamelCase__				,			UpperCamelCase__ )
							# must be in larger
							else:
														return quick_select(UpperCamelCase__				,			index - (m + count) )






"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AlbertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							A__		   =							AlbertForPreTraining(UpperCamelCase__ )

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

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


if __name__ == "__main__":
				__lowerCamelCase							 =     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."
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





