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


lowercase_  : List[Any]								=					logging.get_logger(__name__)

lowercase_  : Dict								=					{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   "vit_msn"
    def __init__(	self			,			lowerCAmelCase=768			,			lowerCAmelCase=12			,			lowerCAmelCase=12			,			lowerCAmelCase=3072			,			lowerCAmelCase="gelu"			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.02			,			lowerCAmelCase=1e-06			,			lowerCAmelCase=224			,			lowerCAmelCase=16			,			lowerCAmelCase=3			,			lowerCAmelCase=True			,			**lowerCAmelCase			,			)							->       int:
     super().__init__(**lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: int=   hidden_size
     SCREAMING_SNAKE_CASE__: List[Any]=   num_hidden_layers
     SCREAMING_SNAKE_CASE__: int=   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__: Tuple=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__: int=   initializer_range
     SCREAMING_SNAKE_CASE__: str=   layer_norm_eps
     SCREAMING_SNAKE_CASE__: Any=   image_size
     SCREAMING_SNAKE_CASE__: List[Any]=   patch_size
     SCREAMING_SNAKE_CASE__: Optional[Any]=   num_channels
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   qkv_bias




import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       BertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case_     							=       BertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

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


if __name__ == "__main__":
					_UpperCAmelCase						:  Union[str, 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(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT 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						:  Any									=			parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




