


import argparse

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


logging.set_verbosity_info()
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase							):
   # Initialise PyTorch model
   UpperCAmelCase_				:   int							= BertConfig.from_pretrained(
       '''bert-large-cased'''      ,							vocab_size=__lowercase      ,							max_position_embeddings=5_1_2      ,							is_decoder=__lowercase      ,							add_cross_attention=__lowercase      ,							)
   UpperCAmelCase_				:   Tuple							= bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   UpperCAmelCase_				:   Union[str, Any]							= BertGenerationConfig(**__lowercase							)
   if is_encoder:
      UpperCAmelCase_				:   List[str]							= BertGenerationEncoder(__lowercase							)
   else:
      UpperCAmelCase_				:   str							= BertGenerationDecoder(__lowercase							)
   print(F'''Building PyTorch model from configuration: {config}'''							)

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       __lowercase      ,							__lowercase      ,							model_class='''bert'''      ,							is_encoder_named_decoder=__lowercase      ,							is_encoder=__lowercase      ,							)

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


if __name__ == "__main__":
       __UpperCamelCase					:	List[Any]          =				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=5_0358, type=int, help='Vocab size of model')
       __UpperCamelCase					:	str          =				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,
       )



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()
def   snake_case_    (			__lowercase      ,							__lowercase							):
   # Initialize PyTorch model
   UpperCAmelCase_				:   Optional[Any]							= CanineConfig()
   UpperCAmelCase_				:   Any							= CanineModel(__lowercase							)
   model.eval()

   print(F'''Building PyTorch model from configuration: {config}'''							)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(__lowercase      ,							__lowercase      ,							__lowercase							)

   # Save pytorch-model (weights and configuration)
   print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
   model.save_pretrained(__lowercase							)

   # Save tokenizer files
   UpperCAmelCase_				:   List[Any]							= CanineTokenizer()
   print(F'''Save tokenizer files to {pytorch_dump_path}'''							)
   tokenizer.save_pretrained(__lowercase							)


if __name__ == "__main__":
       __UpperCamelCase					:	int          =				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path',
           default=None,
           type=str,
           required=True,
           help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
       )
       parser.add_argument(
           '--pytorch_dump_path',
           default=None,
           type=str,
           required=True,
           help='Path to a folder where the PyTorch model will be placed.',
       )
       __UpperCamelCase					:	str          =				parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)