
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
lowercase__          =False
lowercase__          =False




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ):
 return TrainCommand(_UpperCAmelCase  )



class 		UpperCamelCase__  (	SCREAMING_SNAKE_CASE__   ):
   @staticmethod
   def 				lowerCAmelCase      (snake_case_			: ArgumentParser       ):
    __a			:  Optional[int]		    =  parser.add_parser('''train''' ,    help='''CLI tool to train a model on a task.'''       )

    train_parser.add_argument(
        '''--train_data''' ,    type=snake_case_ ,    required=snake_case_ ,    help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.''' ,    )
    train_parser.add_argument(
        '''--column_label''' ,    type=snake_case_ ,    default=0 ,    help='''Column of the dataset csv file with example labels.'''       )
    train_parser.add_argument(
        '''--column_text''' ,    type=snake_case_ ,    default=1 ,    help='''Column of the dataset csv file with example texts.'''       )
    train_parser.add_argument(
        '''--column_id''' ,    type=snake_case_ ,    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=snake_case_ ,    default='''''' ,    help='''path to validation dataset.'''       )
    train_parser.add_argument(
        '''--validation_split''' ,    type=snake_case_ ,    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=snake_case_ ,    default='''./''' ,    help='''path to saved the trained model.'''       )

    train_parser.add_argument(
        '''--task''' ,    type=snake_case_ ,    default='''text_classification''' ,    help='''Task to train the model on.'''       )
    train_parser.add_argument(
        '''--model''' ,    type=snake_case_ ,    default='''bert-base-uncased''' ,    help='''Model\'s name or path to stored model.'''       )
    train_parser.add_argument('''--train_batch_size''' ,    type=snake_case_ ,    default=3_2 ,    help='''Batch size for training.'''       )
    train_parser.add_argument('''--valid_batch_size''' ,    type=snake_case_ ,    default=6_4 ,    help='''Batch size for validation.'''       )
    train_parser.add_argument('''--learning_rate''' ,    type=snake_case_ ,    default=3E-5 ,    help='''Learning rate.'''       )
    train_parser.add_argument('''--adam_epsilon''' ,    type=snake_case_ ,    default=1E-08 ,    help='''Epsilon for Adam optimizer.'''       )
    train_parser.set_defaults(func=snake_case_       )
   def __init__(self			: str ,    snake_case_			: Namespace       ):
    __a			:  str		    =  logging.get_logger('''transformers-cli/training'''       )

    __a			:  Dict		    =  'tf' if is_tf_available() else 'torch'

    os.makedirs(args.output ,    exist_ok=snake_case_       )
    __a			:  int		    =  args.output

    __a			:  Optional[int]		    =  args.column_label
    __a			:  Union[str, Any]		    =  args.column_text
    __a			:  List[str]		    =  args.column_id

    self.logger.info(f"Loading {args.task} pipeline for {args.model}"       )
    if args.task == "text_classification":
     __a			:  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}"       )
    __a			:  Optional[int]		    =  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 ,    )
    __a			:  Dict		    =  None
    if args.validation_data:
     self.logger.info(f"Loading validation dataset from {args.validation_data}"       )
     __a			:  Optional[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 ,    )

    __a			:  Tuple		    =  args.validation_split
    __a			:  Union[str, Any]		    =  args.train_batch_size
    __a			:  str		    =  args.valid_batch_size
    __a			:  str		    =  args.learning_rate
    __a			:  Optional[int]		    =  args.adam_epsilon
   def 				lowerCAmelCase      (self			: List[Any]       ):
    if self.framework == "tf":
     return self.run_tf()
    return self.run_torch()
   def 				lowerCAmelCase      (self			: int       ):
    raise NotImplementedError







   def 				lowerCAmelCase      (self			: Dict       ):
    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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase__          ={
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecAudioForAudioFrameClassification',
          'Data2VecAudioForCTC',
          'Data2VecAudioForSequenceClassification',
          'Data2VecAudioForXVector',
          'Data2VecAudioModel',
          'Data2VecAudioPreTrainedModel',
      ]
      lowercase__          =[
          'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecTextForCausalLM',
          'Data2VecTextForMaskedLM',
          'Data2VecTextForMultipleChoice',
          'Data2VecTextForQuestionAnswering',
          'Data2VecTextForSequenceClassification',
          'Data2VecTextForTokenClassification',
          'Data2VecTextModel',
          'Data2VecTextPreTrainedModel',
      ]
      lowercase__          =[
          'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecVisionForImageClassification',
          'Data2VecVisionForMaskedImageModeling',
          'Data2VecVisionForSemanticSegmentation',
          'Data2VecVisionModel',
          'Data2VecVisionPreTrainedModel',
      ]

if is_tf_available():
      lowercase__          =[
          'TFData2VecVisionForImageClassification',
          'TFData2VecVisionForSemanticSegmentation',
          'TFData2VecVisionModel',
          'TFData2VecVisionPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
      from .configuration_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecTextConfig,
          DataaVecTextOnnxConfig,
      )
      from .configuration_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecVisionConfig,
          DataaVecVisionOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dataavec_audio import (
                DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecAudioForAudioFrameClassification,
                DataaVecAudioForCTC,
                DataaVecAudioForSequenceClassification,
                DataaVecAudioForXVector,
                DataaVecAudioModel,
                DataaVecAudioPreTrainedModel,
            )
            from .modeling_dataavec_text import (
                DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecTextForCausalLM,
                DataaVecTextForMaskedLM,
                DataaVecTextForMultipleChoice,
                DataaVecTextForQuestionAnswering,
                DataaVecTextForSequenceClassification,
                DataaVecTextForTokenClassification,
                DataaVecTextModel,
                DataaVecTextPreTrainedModel,
            )
            from .modeling_dataavec_vision import (
                DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecVisionForImageClassification,
                DataaVecVisionForMaskedImageModeling,
                DataaVecVisionForSemanticSegmentation,
                DataaVecVisionModel,
                DataaVecVisionPreTrainedModel,
            )
      if is_tf_available():
            from .modeling_tf_dataavec_vision import (
                TFDataaVecVisionForImageClassification,
                TFDataaVecVisionForSemanticSegmentation,
                TFDataaVecVisionModel,
                TFDataaVecVisionPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
