

'''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 			_lowercase    (      __UpperCamelCase     :		Dict   ,       __UpperCamelCase     :		List[str] ):
      # Initialize PyTorch model
      snake_case__					=   CanineConfig()
      snake_case__					=   CanineModel(__UpperCamelCase )
      model.eval()

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

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

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

      # Save tokenizer files
      snake_case__					=   CanineTokenizer()
      print(F'''Save tokenizer files to {pytorch_dump_path}''' )
      tokenizer.save_pretrained(__UpperCamelCase )


if __name__ == "__main__":
       lowerCAmelCase						:   List[str]										=    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.''',
       )
       lowerCAmelCase						:   Tuple										=    parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)









import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_reformer import ReformerTokenizer
else:
       lowerCAmelCase						:   Union[str, Any]										=    None


lowerCAmelCase						:   str										=    logging.get_logger(__name__)


lowerCAmelCase						:   Any										=    '''▁'''

lowerCAmelCase						:   Tuple										=    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase						:   Optional[Any]										=    {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

lowerCAmelCase						:   Union[str, Any]										=    {
    '''google/reformer-crime-and-punishment''': 52_4288,
}
class     SCREAMING_SNAKE_CASE__     (						__a		):





       '''simple docstring'''




       UpperCamelCase__ :     Union[str, Any]				    =       VOCAB_FILES_NAMES
       UpperCamelCase__ :     str				    =       PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__ :     str				    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__ :     str				    =       ['''input_ids''', '''attention_mask''']
       UpperCamelCase__ :     Optional[Any]				    =       ReformerTokenizer

       def __init__(						self       :						List[str]    ,		lowerCAmelCase__       :						Tuple=None    ,		lowerCAmelCase__       :						Optional[Any]=None    ,		lowerCAmelCase__       :						Dict="</s>"    ,		lowerCAmelCase__       :						Tuple="<unk>"    ,		lowerCAmelCase__       :						Dict=[]    ,		**lowerCAmelCase__       :						Optional[Any]    ,		)			->    Dict:
             super().__init__(
                 lowerCAmelCase__    ,		tokenizer_file=lowerCAmelCase__    ,		eos_token=lowerCAmelCase__    ,		unk_token=lowerCAmelCase__    ,		additional_special_tokens=lowerCAmelCase__    ,		**lowerCAmelCase__    ,		)

             snake_case__					=   vocab_file
             snake_case__					=   False if not self.vocab_file else True

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						str    ,		lowerCAmelCase__       :						Optional[str] = None							)			->    Tuple[str]:
             if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                       """tokenizer."""							)

             if not os.path.isdir(lowerCAmelCase__							):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                   return
             snake_case__					=   os.path.join(
                 lowerCAmelCase__    ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

             if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCAmelCase__							):
                   copyfile(self.vocab_file    ,		lowerCAmelCase__							)

             return (out_vocab_file,)






