"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				# Initialise PyTorch model
				SCREAMING_SNAKE_CASE__		  =				AlbertConfig.from_json_file(__UpperCAmelCase       )
				print(F"""Building PyTorch model from configuration: {config}"""       )
				SCREAMING_SNAKE_CASE__		  =				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__":
				_A   					=    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.'
				)
				_A   					=    parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





"""simple docstring"""






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_A   					=    ['small', 'medium', 'large']

_A   					=    'lm_head.decoder.weight'
_A   					=    'lm_head.weight'




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				d.pop(__UpperCAmelCase       )
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )
				torch.save(__UpperCAmelCase  ,					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						parser.add_argument('--dialogpt_path', default='.', type=str)
						_A   					=    parser.parse_args()
						for MODEL in DIALOGPT_MODELS:
												_A   					=    os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
												_A   					=    F'./DialoGPT-{MODEL}'
												convert_dialogpt_checkpoint(
												    checkpoint_path,
												    pytorch_dump_folder_path,
												)




