


'''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:
    lowerCAmelCase  =				None

lowerCAmelCase  =				logging.get_logger(__name__)

lowerCAmelCase  =				{"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase  =				{
    """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"""
        ),
    }
}

lowerCAmelCase  =				{
    """microsoft/deberta-v2-xlarge""": 512,
    """microsoft/deberta-v2-xxlarge""": 512,
    """microsoft/deberta-v2-xlarge-mnli""": 512,
    """microsoft/deberta-v2-xxlarge-mnli""": 512,
}

lowerCAmelCase  =				{
    """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 					lowerCAmelCase_     (    UpperCAmelCase  ):
       __UpperCAmelCase				=VOCAB_FILES_NAMES
       __UpperCAmelCase				=PRETRAINED_VOCAB_FILES_MAP
       __UpperCAmelCase				=PRETRAINED_INIT_CONFIGURATION
       __UpperCAmelCase				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCAmelCase				=DebertaVaTokenizer






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

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






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase=None      )->			Optional[Any]:

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






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase=None ,    _UpperCamelCase=False      )->			str:

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

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






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase=None      )->			Union[str, Any]:
            _A				=       [self.sep_token_id]
            _A				=       [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      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

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


logging.set_verbosity_info()

def 				lowerCamelCase_   (   __UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					str		)				->  List[str]:







     """simple docstring"""
     # Initialise PyTorch model
     _A				=       BertConfig.from_pretrained(
         'bert-large-cased'       ,						vocab_size=__UpperCamelCase       ,						max_position_embeddings=5_1_2       ,						is_decoder=__UpperCamelCase       ,						add_cross_attention=__UpperCamelCase       ,						)
     _A				=       bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     _A				=       BertGenerationConfig(**__UpperCamelCase		)
     if is_encoder:
          _A				=       BertGenerationEncoder(__UpperCamelCase		)
     else:
          _A				=       BertGenerationDecoder(__UpperCamelCase		)
     print(F'Building PyTorch model from configuration: {config}'		)

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

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


if __name__ == "__main__":
    lowerCAmelCase  =				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=50_358, type=int, help="""Vocab size of model""")
    lowerCAmelCase  =				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,
    )





