






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
a						:			Dict     	=					False
a						:			str     	=					False






def 							lowerCamelCase__ (       __lowerCamelCase     :	Namespace						):
  return TrainCommand(__lowerCamelCase						)







class        a       (					lowercase__  ):



      """simple docstring"""


      @staticmethod
      def       UpperCAmelCase ( __lowercase   :     ArgumentParser   )				-> Dict:
        __UpperCAmelCase				:      Optional[int]	 =    parser.add_parser("""train"""						, help="""CLI tool to train a model on a task."""   )

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

        train_parser.add_argument(
            """--task"""						, type=__lowercase						, default="""text_classification"""						, help="""Task to train the model on."""   )
        train_parser.add_argument(
            """--model"""						, type=__lowercase						, default="""bert-base-uncased"""						, help="""Model's name or path to stored model."""   )
        train_parser.add_argument("""--train_batch_size"""						, type=__lowercase						, default=32						, help="""Batch size for training."""   )
        train_parser.add_argument("""--valid_batch_size"""						, type=__lowercase						, default=64						, help="""Batch size for validation."""   )
        train_parser.add_argument("""--learning_rate"""						, type=__lowercase						, default=3e-5						, help="""Learning rate."""   )
        train_parser.add_argument("""--adam_epsilon"""						, type=__lowercase						, default=1e-0_8						, help="""Epsilon for Adam optimizer."""   )
        train_parser.set_defaults(func=__lowercase   )
      def __init__( self   :     Dict						, __lowercase   :     Namespace   )				-> Dict:
        __UpperCAmelCase				:      Union[str, Any]	 =    logging.get_logger("""transformers-cli/training"""   )

        __UpperCAmelCase				:      Any	 =    """tf""" if is_tf_available() else """torch"""

        os.makedirs(args.output						, exist_ok=__lowercase   )
        __UpperCAmelCase				:      List[Any]	 =    args.output

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

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

        __UpperCAmelCase				:      Optional[Any]	 =    args.validation_split
        __UpperCAmelCase				:      Dict	 =    args.train_batch_size
        __UpperCAmelCase				:      List[Any]	 =    args.valid_batch_size
        __UpperCAmelCase				:      List[str]	 =    args.learning_rate
        __UpperCAmelCase				:      Any	 =    args.adam_epsilon
      def       UpperCAmelCase ( self   :     List[Any]   )				-> List[Any]:
        if self.framework == "tf":
          return self.run_tf()
        return self.run_torch()
      def       UpperCAmelCase ( self   :     Dict   )				-> Optional[Any]:
        raise NotImplementedError




      def       UpperCAmelCase ( self   :     str   )				-> Optional[int]:
        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   )






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_UpperCamelCase 							=  {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  ["""ViTFeatureExtractor"""]
      _UpperCamelCase 							=  ["""ViTImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ViTForImageClassification""",
          """ViTForMaskedImageModeling""",
          """ViTModel""",
          """ViTPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """TFViTForImageClassification""",
          """TFViTModel""",
          """TFViTPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """FlaxViTForImageClassification""",
          """FlaxViTModel""",
          """FlaxViTPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_vit import ViTFeatureExtractor
            from .image_processing_vit import ViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_vit import (
                VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                ViTForImageClassification,
                ViTForMaskedImageModeling,
                ViTModel,
                ViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
      import sys

      _UpperCamelCase 							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

