"""simple docstring"""





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


__A  =				logging.get_logger(__name__)

__A  =				{
    '''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  _snake_case     (					A_		):
      snake_case__			  =			'''vit_msn'''






      def __init__(       self      :			int							,					UpperCAmelCase      :			Tuple=768							,					UpperCAmelCase      :			List[Any]=12							,					UpperCAmelCase      :			Any=12							,					UpperCAmelCase      :			Any=3072							,					UpperCAmelCase      :			Tuple="gelu"							,					UpperCAmelCase      :			str=0.0							,					UpperCAmelCase      :			Dict=0.0							,					UpperCAmelCase      :			str=0.0_2							,					UpperCAmelCase      :			int=1E-06							,					UpperCAmelCase      :			List[str]=224							,					UpperCAmelCase      :			Dict=16							,					UpperCAmelCase      :			List[str]=3							,					UpperCAmelCase      :			List[Any]=True							,					**UpperCAmelCase      :			int							,					):
             super().__init__(**UpperCAmelCase    )

             __lowerCamelCase					:		Optional[int]		   =     hidden_size
             __lowerCamelCase					:		Any		   =     num_hidden_layers
             __lowerCamelCase					:		Dict		   =     num_attention_heads
             __lowerCamelCase					:		List[str]		   =     intermediate_size
             __lowerCamelCase					:		Tuple		   =     hidden_act
             __lowerCamelCase					:		Optional[int]		   =     hidden_dropout_prob
             __lowerCamelCase					:		List[Any]		   =     attention_probs_dropout_prob
             __lowerCamelCase					:		Dict		   =     initializer_range
             __lowerCamelCase					:		str		   =     layer_norm_eps
             __lowerCamelCase					:		Union[str, Any]		   =     image_size
             __lowerCamelCase					:		str		   =     patch_size
             __lowerCamelCase					:		List[str]		   =     num_channels
             __lowerCamelCase					:		List[Any]		   =     qkv_bias
"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     TaForConditionalGeneration(_lowerCamelCase      )

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
							model.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
				__A  =				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(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				__A  =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)