
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


lowerCamelCase_				:							Union[str, Any]       =    logging.get_logger(__name__)

lowerCamelCase_				:							Dict       =    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase_				:							List[Any]       =    {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

lowerCamelCase_				:							List[Any]       =    {
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

lowerCamelCase_				:							Any       =    {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			VOCAB_FILES_NAMES
     lowercase__				  =			PRETRAINED_VOCAB_FILES_MAP
     lowercase__				  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase__				  =			["""input_ids""", """attention_mask""", """token_type_ids"""]
     lowercase__				  =			DebertaTokenizer

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_="replace"							,       SCREAMING_SNAKE_CASE_="[CLS]"							,       SCREAMING_SNAKE_CASE_="[SEP]"							,       SCREAMING_SNAKE_CASE_="[SEP]"							,       SCREAMING_SNAKE_CASE_="[CLS]"							,       SCREAMING_SNAKE_CASE_="[UNK]"							,       SCREAMING_SNAKE_CASE_="[PAD]"							,       SCREAMING_SNAKE_CASE_="[MASK]"							,       SCREAMING_SNAKE_CASE_=False							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(
                SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       tokenizer_file=SCREAMING_SNAKE_CASE_							,       errors=SCREAMING_SNAKE_CASE_							,       bos_token=SCREAMING_SNAKE_CASE_							,       eos_token=SCREAMING_SNAKE_CASE_							,       unk_token=SCREAMING_SNAKE_CASE_							,       sep_token=SCREAMING_SNAKE_CASE_							,       cls_token=SCREAMING_SNAKE_CASE_							,       pad_token=SCREAMING_SNAKE_CASE_							,       mask_token=SCREAMING_SNAKE_CASE_							,       add_prefix_space=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_							,       )
            __snake_case						       =   kwargs.pop('add_bos_token'							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
            if pre_tok_state.get('add_prefix_space'							,       SCREAMING_SNAKE_CASE_    ) != add_prefix_space:
                   __snake_case						       =   getattr(SCREAMING_SNAKE_CASE_							,       pre_tok_state.pop('type'    )    )
                   __snake_case						       =   add_prefix_space
                   __snake_case						       =   pre_tok_class(**SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   add_prefix_space

     @property
     def 						__lowerCamelCase				(  self    ):
            if self._mask_token is None:
                   if self.verbose:
                          logger.error('Using mask_token, but it is not set yet.'    )
                   return None
            return str(self._mask_token    )

     @mask_token.setter
     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   AddedToken(SCREAMING_SNAKE_CASE_							,       lstrip=SCREAMING_SNAKE_CASE_							,       rstrip=SCREAMING_SNAKE_CASE_    ) if isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ) else value
            __snake_case						       =   value

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None    ):
            if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            __snake_case						       =   [self.cls_token_id]
            __snake_case						       =   [self.sep_token_id]
            return cls + token_ids_a + sep + token_ids_a + sep

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None    ):
            __snake_case						       =   [self.sep_token_id]
            __snake_case						       =   [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 						__lowerCamelCase				(  self							,       *SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   kwargs.get('is_split_into_words'							,       SCREAMING_SNAKE_CASE_    )
            assert self.add_prefix_space or not is_split_into_words, (
                f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       *SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   kwargs.get('is_split_into_words'							,       SCREAMING_SNAKE_CASE_    )

            assert self.add_prefix_space or not is_split_into_words, (
                f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None    ):
            __snake_case						       =   self._tokenizer.model.save(SCREAMING_SNAKE_CASE_							,       name=SCREAMING_SNAKE_CASE_    )
            return tuple(SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   []
            for is_user, text in conversation.iter_texts():
                   input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_							,       add_special_tokens=SCREAMING_SNAKE_CASE_    ) + [self.eos_token_id]    )

            if len(SCREAMING_SNAKE_CASE_    ) > self.model_max_length:
                   __snake_case						       =   input_ids[-self.model_max_length :]
            return input_ids


import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[Any]       =    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(
     "--convbert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ConvBERT 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."
 )
 lowerCamelCase_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
