



import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()







def       lowerCamelCase_  (    _a       :						Union[str, Any]   ,		_a       :						Union[str, Any]   ,		_a       :						str   ,		_a       :						Tuple   ,		_a       :						Any    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					BertConfig.from_pretrained(
        """bert-large-cased"""   ,		vocab_size=_a   ,		max_position_embeddings=512   ,		is_decoder=_a   ,		add_cross_attention=_a   ,		)
    UpperCAmelCase_     :       Any  		=					bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCAmelCase_     :       str  		=					BertGenerationConfig(**_a    )
    if is_encoder:
        UpperCAmelCase_     :       List[str]  		=					BertGenerationEncoder(_a    )
    else:
        UpperCAmelCase_     :       Any  		=					BertGenerationDecoder(_a    )
    print(F'''Building PyTorch model from configuration: {config}'''    )

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        _a   ,		_a   ,		model_class="""bert"""   ,		is_encoder_named_decoder=_a   ,		is_encoder=_a   ,		)

    # Save pytorch-model
    print(F'''Save PyTorch model and config to {pytorch_dump_path}'''    )
    model.save_pretrained(_a    )


if __name__ == "__main__":
      UpperCamelCase_									=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      parser.add_argument(
          '''--is_encoder_named_decoder''',
          action='''store_true''',
          help='''If decoder has to be renamed to encoder in PyTorch model.''',
      )
      parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
      parser.add_argument('''--vocab_size''', default=50358, type=int, help='''Vocab size of model''')
      UpperCamelCase_									=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )





"""simple docstring"""




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


_UpperCamelCase:					Tuple     				=	logging.get_logger(__name__)

_UpperCamelCase:					List[str]     				=	{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					'regnet'
   _lowerCamelCase       				=					['x', 'y']


   def __init__(				self   :				Union[str, Any],  lowerCAmelCase   :				Union[str, Any]=3,  lowerCAmelCase   :				Tuple=32,  lowerCAmelCase   :				Optional[int]=[128, 192, 512, 1088],  lowerCAmelCase   :				Optional[int]=[2, 6, 12, 2],  lowerCAmelCase   :				str=64,  lowerCAmelCase   :				List[str]="y",  lowerCAmelCase   :				Optional[int]="relu",  **lowerCAmelCase   :				List[Any],  )     ->							Union[str, Any]:
         super().__init__(**lowerCAmelCase      )
         if layer_type not in self.layer_types:
               raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types      )}'''      )
         lowercase	:   str											= num_channels
         lowercase	:   str											= embedding_size
         lowercase	:   int											= hidden_sizes
         lowercase	:   Tuple											= depths
         lowercase	:   str											= groups_width
         lowercase	:   List[str]											= layer_type
         lowercase	:   Optional[int]											= hidden_act
         # always downsample in the first stage
         lowercase	:   Optional[Any]											= True
