






import argparse

import torch

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


logging.set_verbosity_info()





def 	lowerCAmelCase_		(lowercase__						:       Any     ,					lowercase__						:       Optional[Any]     ,					lowercase__						:       List[str]       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						AlbertConfig.from_json_file(lowercase__       )
				print(f'Building PyTorch model from configuration: {config}'       )
				lowerCAmelCase__      			=						AlbertForPreTraining(lowercase__       )

				# Load weights from tf checkpoint
				load_tf_weights_in_albert(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			:		Tuple   				=    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			:		Any   				=    parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)













from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





