



from ... import PretrainedConfig


_lowercase     							=						{
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class 				__A					( A_      ):
			UpperCamelCase					:Optional[Any]     =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			UpperCamelCase					:int     =			'''nezha'''





			def __init__(self							,    __magic_name__=21128							,    __magic_name__=768							,    __magic_name__=12							,    __magic_name__=12							,    __magic_name__=3072							,    __magic_name__="gelu"							,    __magic_name__=0.1							,    __magic_name__=0.1							,    __magic_name__=512							,    __magic_name__=64							,    __magic_name__=2							,    __magic_name__=0.02							,    __magic_name__=1E-12							,    __magic_name__=0.1							,    __magic_name__=0							,    __magic_name__=2							,    __magic_name__=3							,    __magic_name__=True							,    **__magic_name__							,    ):
						super().__init__(pad_token_id=__magic_name__							,    bos_token_id=__magic_name__							,    eos_token_id=__magic_name__							,    **__magic_name__				)

						lowerCamelCase__ :      List[str]								=		vocab_size
						lowerCamelCase__ :      int								=		hidden_size
						lowerCamelCase__ :      int								=		num_hidden_layers
						lowerCamelCase__ :      Optional[int]								=		num_attention_heads
						lowerCamelCase__ :      Dict								=		hidden_act
						lowerCamelCase__ :      str								=		intermediate_size
						lowerCamelCase__ :      Optional[Any]								=		hidden_dropout_prob
						lowerCamelCase__ :      Dict								=		attention_probs_dropout_prob
						lowerCamelCase__ :      str								=		max_position_embeddings
						lowerCamelCase__ :      Optional[Any]								=		max_relative_position
						lowerCamelCase__ :      Optional[int]								=		type_vocab_size
						lowerCamelCase__ :      Optional[Any]								=		initializer_range
						lowerCamelCase__ :      Any								=		layer_norm_eps
						lowerCamelCase__ :      Union[str, Any]								=		classifier_dropout
						lowerCamelCase__ :      Union[str, Any]								=		use_cache








import argparse

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


logging.set_verbosity_info()






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[Any] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		TaConfig.from_json_file(UpperCamelCase )
			print(f"Building PyTorch model from configuration: {config}" )
			lowerCamelCase__ :      Optional[int]								=		TaForConditionalGeneration(UpperCamelCase )

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

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


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



