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


lowercase_  : Optional[int]								=					logging.get_logger(__name__)

lowercase_  : Optional[Any]								=					{
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   "vit_mae"
    def __init__(	self			,			lowerCAmelCase=768			,			lowerCAmelCase=12			,			lowerCAmelCase=12			,			lowerCAmelCase=3072			,			lowerCAmelCase="gelu"			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.02			,			lowerCAmelCase=1e-12			,			lowerCAmelCase=224			,			lowerCAmelCase=16			,			lowerCAmelCase=3			,			lowerCAmelCase=True			,			lowerCAmelCase=16			,			lowerCAmelCase=512			,			lowerCAmelCase=8			,			lowerCAmelCase=2048			,			lowerCAmelCase=0.75			,			lowerCAmelCase=False			,			**lowerCAmelCase			,			)							->       Optional[int]:
     super().__init__(**lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   hidden_size
     SCREAMING_SNAKE_CASE__: List[str]=   num_hidden_layers
     SCREAMING_SNAKE_CASE__: Dict=   num_attention_heads
     SCREAMING_SNAKE_CASE__: Any=   intermediate_size
     SCREAMING_SNAKE_CASE__: Tuple=   hidden_act
     SCREAMING_SNAKE_CASE__: Optional[int]=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__: Dict=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__: Dict=   initializer_range
     SCREAMING_SNAKE_CASE__: List[Any]=   layer_norm_eps
     SCREAMING_SNAKE_CASE__: int=   image_size
     SCREAMING_SNAKE_CASE__: str=   patch_size
     SCREAMING_SNAKE_CASE__: str=   num_channels
     SCREAMING_SNAKE_CASE__: Tuple=   qkv_bias
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   decoder_num_attention_heads
     SCREAMING_SNAKE_CASE__: Tuple=   decoder_hidden_size
     SCREAMING_SNAKE_CASE__: List[Any]=   decoder_num_hidden_layers
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   decoder_intermediate_size
     SCREAMING_SNAKE_CASE__: List[Any]=   mask_ratio
     SCREAMING_SNAKE_CASE__: List[str]=   norm_pix_loss




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				List[Any]:
	# Initialise PyTorch model
	__A :			Dict		    =			MobileBertConfig.from_json_file(a__       )
	print(f"""Building PyTorch model from configuration: {config}"""       )
	__A :			Tuple		    =			MobileBertForPreTraining(a__       )
	# Load weights from tf checkpoint
	__A :			Dict		    =			load_tf_weights_in_mobilebert(a__			,a__			,a__       )
	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					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(
			    '''--mobilebert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained MobileBERT 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_							:					Tuple		 =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





