



import sys

from transformers import AutoTokenizer


_UpperCamelCase							    =						sys.argv[1]
_UpperCamelCase							    =						sys.argv[2]
_UpperCamelCase							    =						int(sys.argv[3])

_UpperCamelCase							    =						0

_UpperCamelCase							    =						AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
   for line in f_p:
      _UpperCamelCase							    =						line.rstrip()

      if not line:
         print(line)
         _UpperCamelCase							    =						0
         continue

      _UpperCamelCase							    =						line.split()[0]

      _UpperCamelCase							    =						len(tokenizer.tokenize(token))

      # Token contains strange control characters like \x96 or \x95
      # Just filter out the complete line
      if current_subwords_len == 0:
         continue

      if (subword_len_counter + current_subwords_len) > max_len:
         print('')
         print(line)
         _UpperCamelCase							    =						current_subwords_len
         continue

      subword_len_counter += current_subwords_len

      print(line)











'''simple docstring'''





import argparse

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


logging.set_verbosity_info()

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
  # Initialize PyTorch model
  __lowerCamelCase	:	Union[str, Any]   	=			CanineConfig()
  __lowerCamelCase	:	Optional[Any]   	=			CanineModel(_lowerCAmelCase	)
  model.eval()

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

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

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

  # Save tokenizer files
  __lowerCamelCase	:	str   	=			CanineTokenizer()
  print(F'Save tokenizer files to {pytorch_dump_path}'	)
  tokenizer.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
   _UpperCamelCase							    =						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							    =						parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






