




import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

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


snake_case_ : Optional[int]    		=       {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}


def    A						(__A   :    str   ,				__A   :    Optional[int]   ,				__A   :    Optional[Any]   ,				__A   :    Union[str, Any]       )   ->      Optional[int]:







     """simple docstring"""


     if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
          raise ValueError(F"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()       )}."""       )

     if tokenizer_name is None:
          UpperCAmelCase_          =							TOKENIZER_CLASSES
     else:
          UpperCAmelCase_          =							{tokenizer_name: getattr(__A   ,				tokenizer_name + '''Fast'''       )}

     logger.info(F"""Loading tokenizer classes: {tokenizer_names}"""       )

     for tokenizer_name in tokenizer_names:
          UpperCAmelCase_          =							TOKENIZER_CLASSES[tokenizer_name]

          UpperCAmelCase_          =							True
          if checkpoint_name is None:
               UpperCAmelCase_          =							list(tokenizer_class.max_model_input_sizes.keys()       )
          else:
               UpperCAmelCase_          =							[checkpoint_name]

          logger.info(F"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""       )

          for checkpoint in checkpoint_names:
               logger.info(F"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""       )

               # Load tokenizer
               UpperCAmelCase_          =							tokenizer_class.from_pretrained(__A   ,				force_download=__A       )

               # Save fast tokenizer
               logger.info(F"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""       )

               # For organization names we create sub-directories
               if "/" in checkpoint:
                    UpperCAmelCase_     ,       UpperCAmelCase_          =							checkpoint.split('''/'''       )
                    UpperCAmelCase_          =							os.path.join(__A   ,				__A       )
               elif add_prefix:
                    UpperCAmelCase_          =							checkpoint
                    UpperCAmelCase_          =							dump_path
               else:
                    UpperCAmelCase_          =							None
                    UpperCAmelCase_          =							dump_path

               logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""       )

               if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()       )[0]:
                    UpperCAmelCase_          =							list(tokenizer.pretrained_vocab_files_map.values()       )[0][checkpoint]
                    UpperCAmelCase_          =							file_path.split(__A       )[-1][0]
                    if next_char == "/":
                         UpperCAmelCase_          =							os.path.join(__A   ,				__A       )
                         UpperCAmelCase_          =							None

                    logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""       )

               UpperCAmelCase_          =							tokenizer.save_pretrained(
                   __A   ,				legacy_format=__A   ,				filename_prefix=__A       )
               logger.info(F"""=> File names {file_names}"""       )

               for file_name in file_names:
                    if not file_name.endswith('''tokenizer.json'''       ):
                         os.remove(__A       )
                         logger.info(F"""=> removing {file_name}"""       )


if __name__ == "__main__":
    snake_case_ : int    		=       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
    )
    parser.add_argument(
        "--tokenizer_name",
        default=None,
        type=str,
        help=(
            f"Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will "
            "download and convert all the checkpoints from AWS."
        ),
    )
    parser.add_argument(
        "--checkpoint_name",
        default=None,
        type=str,
        help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
    )
    parser.add_argument(
        "--force_download",
        action="store_true",
        help="Re-download checkpoints.",
    )
    snake_case_ : Union[str, Any]    		=       parser.parse_args()

    convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)











'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__a          =		logging.get_logger(__name__)

__a          =		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__a          =		{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

__a          =		{
    "junnyu/roformer_chinese_small": 1536,
    "junnyu/roformer_chinese_base": 1536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


__a          =		{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		VOCAB_FILES_NAMES
    lowercase					=		PRETRAINED_VOCAB_FILES_MAP
    lowercase					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase					=		PRETRAINED_INIT_CONFIGURATION
    lowercase					=		RoFormerTokenizer




    def __init__(    self					:     List[Any] ,     snake_case_					:     List[str]=None ,     snake_case_					:     Dict=None ,     snake_case_					:     Any=True ,     snake_case_					:     str="[UNK]" ,     snake_case_					:     List[str]="[SEP]" ,     snake_case_					:     Optional[Any]="[PAD]" ,     snake_case_					:     Union[str, Any]="[CLS]" ,     snake_case_					:     Union[str, Any]="[MASK]" ,     snake_case_					:     List[Any]=True ,     snake_case_					:     Optional[Any]=None ,     **snake_case_					:     Tuple ,     ):
          super().__init__(
              snake_case_ ,     tokenizer_file=snake_case_ ,     do_lower_case=snake_case_ ,     unk_token=snake_case_ ,     sep_token=snake_case_ ,     pad_token=snake_case_ ,     cls_token=snake_case_ ,     mask_token=snake_case_ ,     tokenize_chinese_chars=snake_case_ ,     strip_accents=snake_case_ ,     **snake_case_ ,     )

          snake_case__  :    str    						=      json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
          if (
              pre_tok_state.get("""lowercase""" ,     snake_case_						) != do_lower_case
              or pre_tok_state.get("""strip_accents""" ,     snake_case_						) != strip_accents
          ):
                snake_case__  :    str    						=      getattr(snake_case_ ,     pre_tok_state.pop("""type"""						)						)
                snake_case__  :    Optional[int]    						=      do_lower_case
                snake_case__  :    Union[str, Any]    						=      strip_accents
                snake_case__  :    Union[str, Any]    						=      pre_tok_class(**snake_case_						)

          snake_case__  :    str    						=      do_lower_case




    def __getstate__(    self					:     int						):
          snake_case__  :    List[Any]    						=      self.__dict__.copy()
          snake_case__  :    str    						=      BertPreTokenizer()
          return state




    def __setstate__(    self					:     Dict ,     snake_case_					:     Dict						):
          snake_case__  :    List[Any]    						=      d
          snake_case__  :    Union[str, Any]    						=      self.__dict__["""_tokenizer"""].get_vocab()
          snake_case__  :    List[Any]    						=      PreTokenizer.custom(JiebaPreTokenizer(snake_case_						)						)




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     Optional[Any] ,     snake_case_					:     List[str]=None						):
          snake_case__  :    str    						=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                output += token_ids_a + [self.sep_token_id]

          return output




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     List[int] ,     snake_case_					:     Optional[List[int]] = None						):
          snake_case__  :    int    						=      [self.sep_token_id]
          snake_case__  :    str    						=      [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					:     Dict ,     snake_case_					:     str ,     snake_case_					:     Optional[str] = None						):
          snake_case__  :    Union[str, Any]    						=      self._tokenizer.model.save(snake_case_ ,     name=snake_case_						)
          return tuple(snake_case_						)




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     List[str] ,     snake_case_					:     Tuple=None ,     snake_case_					:     List[str]=None ,     snake_case_					:     Union[str, Any]=False ,     **snake_case_					:     Tuple ,     ):
          snake_case__  :    Optional[Any]    						=      BertPreTokenizer()
          return super().save_pretrained(snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     **snake_case_						)
