


import math
def     UpperCAmelCase_ (							__snake_case						)					->      str:







 """simple docstring"""


 if number == 0:
  _lowercase											='''Zero'''
  return words
 else:
  _lowercase											=math.logaa(__snake_case						)
  _lowercase											=digits + 1
  _lowercase											={}
  _lowercase											=''''''
  _lowercase											='''One'''
  _lowercase											='''Two'''
  _lowercase											='''Three'''
  _lowercase											='''Four'''
  _lowercase											='''Five'''
  _lowercase											='''Six'''
  _lowercase											='''Seven'''
  _lowercase											='''Eight'''
  _lowercase											='''Nine'''

  _lowercase											={}
  _lowercase											=''''''
  _lowercase											='''Twenty'''
  _lowercase											='''Thirty'''
  _lowercase											='''Forty'''
  _lowercase											='''Fifty'''
  _lowercase											='''Sixty'''
  _lowercase											='''Seventy'''
  _lowercase											='''Eighty'''
  _lowercase											='''Ninety'''

  _lowercase											={}
  _lowercase											='''Ten'''
  _lowercase											='''Eleven'''
  _lowercase											='''Twelve'''
  _lowercase											='''Thirteen'''
  _lowercase											='''Fourteen'''
  _lowercase											='''Fifteen'''
  _lowercase											='''Sixteen'''
  _lowercase											='''Seventeen'''
  _lowercase											='''Eighteen'''
  _lowercase											='''Nineteen'''

  _lowercase											={}
  _lowercase											='''Hundred,'''
  _lowercase											='''Thousand,'''
  _lowercase											='''Lakh,'''
  _lowercase											='''Crore,'''

  _lowercase											=number
  _lowercase											=''''''
  _lowercase											=0
  _lowercase											=int(__snake_case						)
  while counter < digits:
   _lowercase											=temp_num % 10
   if counter % 2 == 0:
    _lowercase											=''''''
    if counter in placevalue and current != 0:
     _lowercase											=placevalue[counter]
    if counter == 2:
     _lowercase											=singles[current] + addition + words
    elif counter == 0:
     if ((temp_num % 100) // 10) == 1:
      _lowercase											=teens[current] + addition + words
      _lowercase											=temp_num // 10
      counter += 1
     else:
      _lowercase											=singles[current] + addition + words

    else:
     _lowercase											=doubles[current] + addition + words

   else:
    if counter == 1:
     if current == 1:
      _lowercase											=teens[number % 10] + words
     else:
      _lowercase											=''''''
      if counter in placevalue:
       _lowercase											=placevalue[counter]
      _lowercase											=doubles[current] + addition + words
    else:
     _lowercase											=''''''
     if counter in placevalue:
      if current == 0 and ((temp_num % 100) // 10) == 0:
       _lowercase											=''''''
      else:
       _lowercase											=placevalue[counter]
     if ((temp_num % 100) // 10) == 1:
      _lowercase											=teens[current] + addition + words
      _lowercase											=temp_num // 10
      counter += 1
     else:
      _lowercase											=singles[current] + addition + words
   counter += 1
   _lowercase											=temp_num // 10
 return words


if __name__ == "__main__":
  import doctest

  doctest.testmod()





import argparse

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


logging.set_verbosity_info()
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      Tuple:







 """simple docstring"""


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

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

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


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=5_0358, 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,
  )

