

import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__a    :List[str]      =			logging.get_logger(__name__)


__a    :Dict      =			{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}

def       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: int				,__UpperCamelCase	: List[str]				,__UpperCamelCase	: Union[str, Any]    ):



      """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:
            A_						  =				TOKENIZER_CLASSES
      else:
            A_						  =				{tokenizer_name: getattr(__UpperCamelCase				,tokenizer_name + "Fast"    )}

      logger.info(f'''Loading tokenizer classes: {tokenizer_names}'''    )

      for tokenizer_name in tokenizer_names:
            A_						  =				TOKENIZER_CLASSES[tokenizer_name]

            A_						  =				True
            if checkpoint_name is None:
                  A_						  =				list(tokenizer_class.max_model_input_sizes.keys()    )
            else:
                  A_						  =				[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
                  A_						  =				tokenizer_class.from_pretrained(__UpperCamelCase				,force_download=__UpperCamelCase    )

                  # 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:
                        A_  ,     A_						  =				checkpoint.split("/"    )
                        A_						  =				os.path.join(__UpperCamelCase				,__UpperCamelCase    )
                  elif add_prefix:
                        A_						  =				checkpoint
                        A_						  =				dump_path
                  else:
                        A_						  =				None
                        A_						  =				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]:
                        A_						  =				list(tokenizer.pretrained_vocab_files_map.values()    )[0][checkpoint]
                        A_						  =				file_path.split(__UpperCamelCase    )[-1][0]
                        if next_char == "/":
                              A_						  =				os.path.join(__UpperCamelCase				,__UpperCamelCase    )
                              A_						  =				None

                        logger.info(f'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''    )

                  A_						  =				tokenizer.save_pretrained(
                      __UpperCamelCase				,legacy_format=__UpperCamelCase				,filename_prefix=__UpperCamelCase    )
                  logger.info(f'''=> File names {file_names}'''    )

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


if __name__ == "__main__":
      __a    :Optional[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.',
      )
      __a    :List[str]      =			parser.parse_args()

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







from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)



