



'''simple docstring'''


import argparse

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


logging.set_verbosity_info()






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							,						lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									=BertConfig.from_pretrained(
         "bert-large-cased"							,						vocab_size=lowercase__							,						max_position_embeddings=5_1_2							,						is_decoder=lowercase__							,						add_cross_attention=lowercase__							,						)
     a_									=bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     a_									=BertGenerationConfig(**lowercase__							)
     if is_encoder:
          a_									=BertGenerationEncoder(lowercase__							)
     else:
          a_									=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__":
  lowercase											=      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=50_358, type=int, help='''Vocab size of model''')
  lowercase											=      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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowercase											=      {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase											=      ['''BarthezTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase											=      ['''BarthezTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_barthez import BarthezTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_barthez_fast import BarthezTokenizerFast

else:
  import sys

  lowercase											=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


