


import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
      raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
snake_case__				:	Dict					       =					False
snake_case__				:	Optional[int]					       =					False


def    __lowerCamelCase   (      A__						: Namespace					)     ->    int:
   return TrainCommand(A__					)





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       @staticmethod
       def 				_lowerCAmelCase							(						__a				:				ArgumentParser							)					->str:
          lowerCamelCase_						:   Dict		= parser.add_parser("""train"""		,				help="""CLI tool to train a model on a task."""							)

          train_parser.add_argument(
              """--train_data"""		,				type=__a		,				required=__a		,				help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""		,				)
          train_parser.add_argument(
              """--column_label"""		,				type=__a		,				default=0		,				help="""Column of the dataset csv file with example labels."""							)
          train_parser.add_argument(
              """--column_text"""		,				type=__a		,				default=1		,				help="""Column of the dataset csv file with example texts."""							)
          train_parser.add_argument(
              """--column_id"""		,				type=__a		,				default=2		,				help="""Column of the dataset csv file with example ids."""							)
          train_parser.add_argument(
              """--skip_first_row"""		,				action="""store_true"""		,				help="""Skip the first row of the csv file (headers)."""							)

          train_parser.add_argument("""--validation_data"""		,				type=__a		,				default=""""""		,				help="""path to validation dataset."""							)
          train_parser.add_argument(
              """--validation_split"""		,				type=__a		,				default=0.1		,				help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""		,				)

          train_parser.add_argument("""--output"""		,				type=__a		,				default="""./"""		,				help="""path to saved the trained model."""							)

          train_parser.add_argument(
              """--task"""		,				type=__a		,				default="""text_classification"""		,				help="""Task to train the model on."""							)
          train_parser.add_argument(
              """--model"""		,				type=__a		,				default="""bert-base-uncased"""		,				help="""Model's name or path to stored model."""							)
          train_parser.add_argument("""--train_batch_size"""		,				type=__a		,				default=32		,				help="""Batch size for training."""							)
          train_parser.add_argument("""--valid_batch_size"""		,				type=__a		,				default=64		,				help="""Batch size for validation."""							)
          train_parser.add_argument("""--learning_rate"""		,				type=__a		,				default=3e-5		,				help="""Learning rate."""							)
          train_parser.add_argument("""--adam_epsilon"""		,				type=__a		,				default=1e-08		,				help="""Epsilon for Adam optimizer."""							)
          train_parser.set_defaults(func=__a							)



       def __init__(						self				:				Dict		,				__a				:				Namespace							)					->Optional[Any]:
          lowerCamelCase_						:   Dict		= logging.get_logger("""transformers-cli/training"""							)

          lowerCamelCase_						:   Optional[Any]		= """tf""" if is_tf_available() else """torch"""

          os.makedirs(args.output		,				exist_ok=__a							)
          lowerCamelCase_						:   Optional[Any]		= args.output

          lowerCamelCase_						:   Tuple		= args.column_label
          lowerCamelCase_						:   Tuple		= args.column_text
          lowerCamelCase_						:   Tuple		= args.column_id

          self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''							)
          if args.task == "text_classification":
             lowerCamelCase_						:   List[Any]		= TextClassificationPipeline.from_pretrained(args.model							)
          elif args.task == "token_classification":
             raise NotImplementedError
          elif args.task == "question_answering":
             raise NotImplementedError

          self.logger.info(F'''Loading dataset from {args.train_data}'''							)
          lowerCamelCase_						:   Optional[Any]		= Processor.create_from_csv(
              args.train_data		,				column_label=args.column_label		,				column_text=args.column_text		,				column_id=args.column_id		,				skip_first_row=args.skip_first_row		,				)
          lowerCamelCase_						:   Any		= None
          if args.validation_data:
             self.logger.info(F'''Loading validation dataset from {args.validation_data}'''							)
             lowerCamelCase_						:   Union[str, Any]		= Processor.create_from_csv(
                 args.validation_data		,				column_label=args.column_label		,				column_text=args.column_text		,				column_id=args.column_id		,				skip_first_row=args.skip_first_row		,				)

          lowerCamelCase_						:   Dict		= args.validation_split
          lowerCamelCase_						:   Optional[Any]		= args.train_batch_size
          lowerCamelCase_						:   Union[str, Any]		= args.valid_batch_size
          lowerCamelCase_						:   Union[str, Any]		= args.learning_rate
          lowerCamelCase_						:   List[Any]		= args.adam_epsilon



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Dict:
          if self.framework == "tf":
             return self.run_tf()
          return self.run_torch()



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Dict:
          raise NotImplementedError




       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Union[str, Any]:
          self.pipeline.fit(
              self.train_dataset		,				validation_data=self.valid_dataset		,				validation_split=self.validation_split		,				learning_rate=self.learning_rate		,				adam_epsilon=self.adam_epsilon		,				train_batch_size=self.train_batch_size		,				valid_batch_size=self.valid_batch_size		,				)

          # Save trained pipeline
          self.pipeline.save_pretrained(self.output							)









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,
)





