






from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__   :						Dict													=   logging.get_logger(__name__)

snake_case__   :						Optional[Any]													=   {
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """mra"""




  def __init__(  self		,		_UpperCAmelCase=50265		,		_UpperCAmelCase=768		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=512		,		_UpperCAmelCase=1		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-5		,		_UpperCAmelCase="absolute"		,		_UpperCAmelCase=4		,		_UpperCAmelCase="full"		,		_UpperCAmelCase=0		,		_UpperCAmelCase=0		,		_UpperCAmelCase=1		,		_UpperCAmelCase=0		,		_UpperCAmelCase=2		,		**_UpperCAmelCase		,		)      ->			Dict:
     super().__init__(pad_token_id=_UpperCAmelCase		,		bos_token_id=_UpperCAmelCase		,		eos_token_id=_UpperCAmelCase		,		**_UpperCAmelCase			)

     UpperCamelCase_						=  vocab_size
     UpperCamelCase_						=  max_position_embeddings
     UpperCamelCase_						=  hidden_size
     UpperCamelCase_						=  num_hidden_layers
     UpperCamelCase_						=  num_attention_heads
     UpperCamelCase_						=  intermediate_size
     UpperCamelCase_						=  hidden_act
     UpperCamelCase_						=  hidden_dropout_prob
     UpperCamelCase_						=  attention_probs_dropout_prob
     UpperCamelCase_						=  initializer_range
     UpperCamelCase_						=  type_vocab_size
     UpperCamelCase_						=  layer_norm_eps
     UpperCamelCase_						=  position_embedding_type
     UpperCamelCase_						=  block_per_row
     UpperCamelCase_						=  approx_mode
     UpperCamelCase_						=  initial_prior_first_n_blocks
     UpperCamelCase_						=  initial_prior_diagonal_n_blocks













import argparse

import torch

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


logging.set_verbosity_info()






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  AlbertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  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__":
 snake_case__   :						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."""
 )
 snake_case__   :						str													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





