


'''simple docstring'''







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,
)





'''simple docstring'''







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def       lowercase					(_A       ):







       """simple docstring"""







       return ConvertCommand(
           args.model_type ,    args.tf_checkpoint ,    args.pytorch_dump_output ,    args.config ,    args.finetuning_task_name       )


lowerCAmelCase     :  Optional[int]											=    """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     @staticmethod
     def a						(			snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							parser.add_parser(
                'convert'   ,						help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'   ,						)
            train_parser.add_argument('--model_type'   ,						type=snake_case__   ,						required=snake_case__   ,						help='Model\'s type.' )
            train_parser.add_argument(
                '--tf_checkpoint'   ,						type=snake_case__   ,						required=snake_case__   ,						help='TensorFlow checkpoint path or folder.' )
            train_parser.add_argument(
                '--pytorch_dump_output'   ,						type=snake_case__   ,						required=snake_case__   ,						help='Path to the PyTorch saved model output.' )
            train_parser.add_argument('--config'   ,						type=snake_case__   ,						default=''   ,						help='Configuration file path or folder.' )
            train_parser.add_argument(
                '--finetuning_task_name'   ,						type=snake_case__   ,						default=snake_case__   ,						help='Optional fine-tuning task name if the TF model was a finetuned model.'   ,						)
            train_parser.set_defaults(func=snake_case__ )







     def __init__(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						*snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							logging.get_logger('transformers-cli/converting' )

            self._logger.info(F'Loading model {model_type}' )
            _lowerCAmelCase     :				Any       =							model_type
            _lowerCAmelCase     :				Any       =							tf_checkpoint
            _lowerCAmelCase     :				List[str]       =							pytorch_dump_output
            _lowerCAmelCase     :				int       =							config
            _lowerCAmelCase     :				str       =							finetuning_task_name







     def a						(			self ):

            '''simple docstring'''

            if self._model_type == "albert":
                   try:
                          from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "bert":
                   try:
                          from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "funnel":
                   try:
                          from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "t5":
                   try:
                          from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "gpt":
                   from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                       convert_openai_checkpoint_to_pytorch,
                   )

                   convert_openai_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "transfo_xl":
                   try:
                          from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                              convert_transfo_xl_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   if "ckpt" in self._tf_checkpoint.lower():
                          _lowerCAmelCase     :				List[Any]       =							self._tf_checkpoint
                          _lowerCAmelCase     :				Optional[int]       =							''
                   else:
                          _lowerCAmelCase     :				int       =							self._tf_checkpoint
                          _lowerCAmelCase     :				Dict       =							''
                   convert_transfo_xl_checkpoint_to_pytorch(
                       snake_case__   ,						self._config   ,						self._pytorch_dump_output   ,						snake_case__ )
            elif self._model_type == "gpt2":
                   try:
                          from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                              convert_gpta_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "xlnet":
                   try:
                          from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                              convert_xlnet_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_xlnet_checkpoint_to_pytorch(
                       self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output   ,						self._finetuning_task_name )
            elif self._model_type == "xlm":
                   from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                       convert_xlm_checkpoint_to_pytorch,
                   )

                   convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._pytorch_dump_output )
            elif self._model_type == "lxmert":
                   from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                       convert_lxmert_checkpoint_to_pytorch,
                   )

                   convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._pytorch_dump_output )
            elif self._model_type == "rembert":
                   from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                       convert_rembert_tf_checkpoint_to_pytorch,
                   )

                   convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            else:
                   raise ValueError(
                       '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]' )

