


"""simple docstring"""





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
__UpperCAmelCase     					=   False
__UpperCAmelCase     					=   False





def      lowercase__						(	lowerCamelCase  :	Namespace					)       ->  str:
  return TrainCommand(UpperCAmelCase_					)







class 			lowercase_	(			_lowerCAmelCase    ):







      @staticmethod
      def 					_lowerCAmelCase							(   _lowercase			:				List[Any]     ):
        lowerCAmelCase__     :     Tuple										=       parser.add_parser("train"		,	help="CLI tool to train a model on a task."     )

        train_parser.add_argument(
            "--train_data"		,	type=SCREAMING_SNAKE_CASE__		,	required=SCREAMING_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=SCREAMING_SNAKE_CASE__		,	default=0		,	help="Column of the dataset csv file with example labels."     )
        train_parser.add_argument(
            "--column_text"		,	type=SCREAMING_SNAKE_CASE__		,	default=1		,	help="Column of the dataset csv file with example texts."     )
        train_parser.add_argument(
            "--column_id"		,	type=SCREAMING_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=SCREAMING_SNAKE_CASE__		,	default=""		,	help="path to validation dataset."     )
        train_parser.add_argument(
            "--validation_split"		,	type=SCREAMING_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=SCREAMING_SNAKE_CASE__		,	default="./"		,	help="path to saved the trained model."     )

        train_parser.add_argument(
            "--task"		,	type=SCREAMING_SNAKE_CASE__		,	default="text_classification"		,	help="Task to train the model on."     )
        train_parser.add_argument(
            "--model"		,	type=SCREAMING_SNAKE_CASE__		,	default="bert-base-uncased"		,	help="Model's name or path to stored model."     )
        train_parser.add_argument("--train_batch_size"		,	type=SCREAMING_SNAKE_CASE__		,	default=3_2		,	help="Batch size for training."     )
        train_parser.add_argument("--valid_batch_size"		,	type=SCREAMING_SNAKE_CASE__		,	default=6_4		,	help="Batch size for validation."     )
        train_parser.add_argument("--learning_rate"		,	type=SCREAMING_SNAKE_CASE__		,	default=3e-5		,	help="Learning rate."     )
        train_parser.add_argument("--adam_epsilon"		,	type=SCREAMING_SNAKE_CASE__		,	default=1e-0_8		,	help="Epsilon for Adam optimizer."     )
        train_parser.set_defaults(func=SCREAMING_SNAKE_CASE__     )







      def __init__(   self			:				Optional[Any]		,	_lowercase			:				int     ):
        lowerCAmelCase__     :     List[Any]										=       logging.get_logger("transformers-cli/training"     )

        lowerCAmelCase__     :     List[Any]										=       "tf" if is_tf_available() else "torch"

        os.makedirs(args.output		,	exist_ok=SCREAMING_SNAKE_CASE__     )
        lowerCAmelCase__     :     Union[str, Any]										=       args.output

        lowerCAmelCase__     :     Tuple										=       args.column_label
        lowerCAmelCase__     :     int										=       args.column_text
        lowerCAmelCase__     :     List[Any]										=       args.column_id

        self.logger.info(f"Loading {args.task} pipeline for {args.model}"     )
        if args.task == "text_classification":
          lowerCAmelCase__     :     List[str]										=       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__     :     Tuple										=       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__     :     int										=       None
        if args.validation_data:
          self.logger.info(f"Loading validation dataset from {args.validation_data}"     )
          lowerCAmelCase__     :     Dict										=       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__     :     Any										=       args.validation_split
        lowerCAmelCase__     :     List[str]										=       args.train_batch_size
        lowerCAmelCase__     :     Optional[Any]										=       args.valid_batch_size
        lowerCAmelCase__     :     Tuple										=       args.learning_rate
        lowerCAmelCase__     :     List[str]										=       args.adam_epsilon







      def 					_lowerCAmelCase							(   self			:				Optional[int]     ):
        if self.framework == "tf":
          return self.run_tf()
        return self.run_torch()







      def 					_lowerCAmelCase							(   self			:				Union[str, Any]     ):
        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     )





"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase     =							{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """WavLMForAudioFrameClassification""",
			    """WavLMForCTC""",
			    """WavLMForSequenceClassification""",
			    """WavLMForXVector""",
			    """WavLMModel""",
			    """WavLMPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

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




