
"""simple docstring"""







import argparse

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


logging.set_verbosity_info()
def       __UpperCAmelCase		(		a_      :		Optional[Any]     ,	a_      :		str     ,	a_      :		Union[str, Any]     ,	a_      :		List[str]     ,	a_      :		List[str]					)  ->							Union[str, Any]:
   # Initialise PyTorch model
   _UpperCAmelCase 						=       BertConfig.from_pretrained(
       "bert-large-cased"     ,	vocab_size=a_     ,	max_position_embeddings=5_12     ,	is_decoder=a_     ,	add_cross_attention=a_     ,	)
   _UpperCAmelCase 						=       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   _UpperCAmelCase 						=       BertGenerationConfig(**a_					)
   if is_encoder:
      _UpperCAmelCase 						=       BertGenerationEncoder(a_					)
   else:
      _UpperCAmelCase 						=       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__":
  SCREAMING_SNAKE_CASE_  					= 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')
  SCREAMING_SNAKE_CASE_  					= 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"""







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,*lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          warnings.warn(
              "The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV1ImageProcessor instead."				,lowerCamelCase_				,)
          super().__init__(*lowerCamelCase_				,**lowerCamelCase_		)

