


'''simple docstring'''







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

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


if is_sentencepiece_available():
  from .tokenization_deberta_va import DebertaVaTokenizer
else:
  a__    :   List[Any]								=			None

a__    :   Optional[Any]								=			logging.get_logger(__name__)

a__    :   List[str]								=			{'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

a__    :   List[Any]								=			{
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

a__    :   int								=			{
    'microsoft/deberta-v2-xlarge': 5_1_2,
    'microsoft/deberta-v2-xxlarge': 5_1_2,
    'microsoft/deberta-v2-xlarge-mnli': 5_1_2,
    'microsoft/deberta-v2-xxlarge-mnli': 5_1_2,
}

a__    :   List[str]								=			{
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      VOCAB_FILES_NAMES
    __UpperCAmelCase       =      PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase       =      PRETRAINED_INIT_CONFIGURATION
    __UpperCAmelCase       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase       =      DebertaVaTokenizer


    def __init__(	self    ,   a=None    ,   a=None    ,   a=False    ,   a=False    ,   a="[CLS]"    ,   a="[SEP]"    ,   a="[UNK]"    ,   a="[SEP]"    ,   a="[PAD]"    ,   a="[CLS]"    ,   a="[MASK]"    ,   **a    ,   ):
          super().__init__(
              a    ,   tokenizer_file=a    ,   do_lower_case=a    ,   bos_token=a    ,   eos_token=a    ,   unk_token=a    ,   sep_token=a    ,   pad_token=a    ,   cls_token=a    ,   mask_token=a    ,   split_by_punct=a    ,   **a    ,   )

          UpperCamelCase__      =							do_lower_case
          UpperCamelCase__      =							split_by_punct
          UpperCamelCase__      =							vocab_file
          UpperCamelCase__      =							False if not self.vocab_file else True


    def   __a				(	self    ,   a    ,   a=None       ):

          if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCamelCase__      =							[self.cls_token_id]
          UpperCamelCase__      =							[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep


    def   __a				(	self    ,   a    ,   a=None    ,   a=False       ):

          if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=a    ,   token_ids_a=a    ,   already_has_special_tokens=a       )

          if token_ids_a is not None:
                return [1] + ([0] * len(a       )) + [1] + ([0] * len(a       )) + [1]
          return [1] + ([0] * len(a       )) + [1]


    def   __a				(	self    ,   a    ,   a=None       ):
          UpperCamelCase__      =							[self.sep_token_id]
          UpperCamelCase__      =							[self.cls_token_id]
          if token_ids_a is None:
                return len(cls + token_ids_a + sep       ) * [0]
          return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]


    def   __a				(	self    ,   a    ,   a = None       ):
          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(a       ):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
                return
          UpperCamelCase__      =							os.path.join(
              a    ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

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

          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			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



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

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

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


if __name__ == "__main__":
  a__    :   str								=			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.'
  )
  a__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





