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


SCREAMING_SNAKE_CASE							:str							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Any							 =				{
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "luke"






	def __init__(       self					: Dict							,A					: Optional[int]=5_02_67							,A					: List[Any]=50_00_00							,A					: Any=7_68							,A					: Tuple=2_56							,A					: List[Any]=12							,A					: Tuple=12							,A					: List[Any]=30_72							,A					: Any="gelu"							,A					: Any=0.1							,A					: Optional[int]=0.1							,A					: Dict=5_12							,A					: str=2							,A					: List[str]=0.02							,A					: List[str]=1E-12							,A					: List[Any]=True							,A					: Dict=None							,A					: Optional[Any]=1							,A					: Optional[int]=0							,A					: Any=2							,**A					: Tuple							,):
					super().__init__(pad_token_id=A							,bos_token_id=A							,eos_token_id=A							,**A )

					__A   			= vocab_size
					__A   			= entity_vocab_size
					__A   			= hidden_size
					__A   			= entity_emb_size
					__A   			= num_hidden_layers
					__A   			= num_attention_heads
					__A   			= hidden_act
					__A   			= intermediate_size
					__A   			= hidden_dropout_prob
					__A   			= attention_probs_dropout_prob
					__A   			= max_position_embeddings
					__A   			= type_vocab_size
					__A   			= initializer_range
					__A   			= layer_norm_eps
					__A   			= use_entity_aware_attention
					__A   			= classifier_dropout

import argparse

import torch

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


logging.set_verbosity_info()






def  snake_case  (       snake_case__			:int   ,	snake_case__			:List[str]   ,	snake_case__			:Union[str, Any])						->   str:
       # Initialise PyTorch model
       _A        =  AlbertConfig.from_json_file(snake_case__)
       print(F'''Building PyTorch model from configuration: {config}''')
       _A        =  AlbertForPreTraining(snake_case__)

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

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


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




