






import argparse

import torch

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


logging.set_verbosity_info()






def 							lowerCamelCase__ (       __lowerCamelCase     :	int    ,						__lowerCamelCase     :	Any    ,						__lowerCamelCase     :	Union[str, Any]						):
  # Initialise PyTorch model
  __UpperCAmelCase				:      List[str]	 =    AlbertConfig.from_json_file(__lowerCamelCase						)
  print(f"""Building PyTorch model from configuration: {config}"""						)
  __UpperCAmelCase				:      Any	 =    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__":
       a						:			Optional[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(
           "--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						:			Union[str, Any]     	=					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 torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Union[str, Any]:
     __A		:					int						=			RobertaPreLayerNormConfig.from_pretrained(
         __snake_case					,				architectures=['RobertaPreLayerNormForMaskedLM']     )

     # convert state_dict
     __A		:					Tuple						=			torch.load(hf_hub_download(repo_id=__snake_case					,				filename='pytorch_model.bin'     )     )
     __A		:					str						=			{}
     for tensor_key, tensor_value in original_state_dict.items():
          # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
          if tensor_key.startswith('roberta.'     ):
               __A		:					Dict						=			'roberta_prelayernorm.' + tensor_key[len('roberta.'     ) :]

          # The original implementation contains weights which are not used, remove them from the state_dict
          if tensor_key.endswith('.self.LayerNorm.weight'     ) or tensor_key.endswith('.self.LayerNorm.bias'     ):
               continue

          __A		:					str						=			tensor_value

     __A		:					Union[str, Any]						=			RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=__snake_case					,				config=__snake_case					,				state_dict=__snake_case     )
     model.save_pretrained(__snake_case     )

     # convert tokenizer
     __A		:					List[Any]						=			AutoTokenizer.from_pretrained(__snake_case     )
     tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint-repo''',
      default=None,
      type=str,
      required=True,
      help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)