
"""simple docstring"""






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

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


if is_sentencepiece_available():
      from .tokenization_xglm import XGLMTokenizer
else:
      UpperCamelCase_						   =   None


UpperCamelCase_						   =   logging.get_logger(__name__)

UpperCamelCase_						   =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase_						   =   {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

UpperCamelCase_						   =   {
    'facebook/xglm-564M': 2048,
}





class 							snake_case       (     SCREAMING_SNAKE_CASE_		):
   a_					:		Any     					=     VOCAB_FILES_NAMES
   a_					:		Any     					=     PRETRAINED_VOCAB_FILES_MAP
   a_					:		List[str]     					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a_					:		int     					=     ["""input_ids""", """attention_mask"""]
   a_					:		Union[str, Any]     					=     XGLMTokenizer







   def __init__( self       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase="<s>"       ,							__UpperCAmelCase="</s>"       ,							__UpperCAmelCase="</s>"       ,							__UpperCAmelCase="<s>"       ,							__UpperCAmelCase="<unk>"       ,							__UpperCAmelCase="<pad>"       ,							**__UpperCAmelCase       ,							)   ->Optional[int]:
      # Compatibility with the original tokenizer
      a_						   =     7
      a_						   =     [F'''<madeupword{i}>''' for i in range(self.num_madeup_words)]

      a_						   =     kwargs.get("additional_special_tokens"       ,							[])
      kwargs["additional_special_tokens"] += [
          word for word in madeup_words if word not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          __UpperCAmelCase       ,							tokenizer_file=__UpperCAmelCase       ,							bos_token=__UpperCAmelCase       ,							eos_token=__UpperCAmelCase       ,							sep_token=__UpperCAmelCase       ,							cls_token=__UpperCAmelCase       ,							unk_token=__UpperCAmelCase       ,							pad_token=__UpperCAmelCase       ,							**__UpperCAmelCase       ,							)

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







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase = None)   ->List[int]:

      if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
      a_						   =     [self.sep_token_id]
      return sep + token_ids_a + sep + sep + token_ids_a







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase = None)   ->List[int]:
      a_						   =     [self.sep_token_id]

      if token_ids_a is None:
         return len(sep + token_ids_a) * [0]
      return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase = 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(__UpperCAmelCase):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory.''')
         return
      a_						   =     os.path.join(
          __UpperCAmelCase       ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

      return (out_vocab_file,)

"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """simple docstring"""



   a_						   =     RoFormerConfig.from_json_file(UpperCAmelCase     )
   print(F'''Building PyTorch model from configuration: {config}'''     )
   a_						   =     RoFormerForMaskedLM(UpperCAmelCase     )

   # Load weights from tf checkpoint
   load_tf_weights_in_roformer(UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )

   # Save pytorch-model
   print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
   torch.save(model.state_dict() ,   UpperCAmelCase ,   _use_new_zipfile_serialization=UpperCAmelCase     )


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 path.'
      )
      parser.add_argument(
          '--bert_config_file',
          default=None,
          type=str,
          required=True,
          help=(
              'The config json file corresponding to the pre-trained BERT model. \n'
              'This specifies the model architecture.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      UpperCamelCase_						   =   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)