"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Optional[int]			  =						"""ClapFeatureExtractor"""
				_lowerCAmelCase     :    Tuple			  =						("""RobertaTokenizer""", """RobertaTokenizerFast""")

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										super().__init__(lowerCAmelCase		,    lowerCAmelCase    )

				def __call__(				self		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			kwargs.pop('sampling_rate'		,    lowerCAmelCase    )

										if text is None and audios is None:
																raise ValueError('You have to specify either text or audios. Both cannot be none.'    )

										if text is not None:
																snake_case								=			self.tokenizer(lowerCAmelCase		,    return_tensors=lowerCAmelCase		,    **lowerCAmelCase    )

										if audios is not None:
																snake_case								=			self.feature_extractor(
																    lowerCAmelCase		,    sampling_rate=lowerCAmelCase		,    return_tensors=lowerCAmelCase		,    **lowerCAmelCase    )

										if text is not None and audios is not None:
																snake_case								=			audio_features.input_features
																return encoding
										elif text is not None:
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase    )		,    tensor_type=lowerCAmelCase    )

				def 		snake_case (				self		,    *lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return self.tokenizer.batch_decode(*lowerCAmelCase		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    *lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return self.tokenizer.decode(*lowerCAmelCase		,    **lowerCAmelCase    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.tokenizer.model_input_names
										snake_case								=			self.feature_extractor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )



"""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
SCREAMING_SNAKE_CASE__      						=  False
SCREAMING_SNAKE_CASE__      						=  False





def    lowerCAmelCase__    (			_UpperCamelCase       :				Namespace					)					->				List[str]:





						"""simple docstring"""






						return TrainCommand(_UpperCamelCase					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				@staticmethod
				def 		snake_case (				lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			parser.add_parser('train'		,    help='CLI tool to train a model on a task.'    )

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

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

				def __init__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			logging.get_logger('transformers-cli/training'    )

										snake_case								=			'tf' if is_tf_available() else 'torch'

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase    )
										snake_case								=			args.output

										snake_case								=			args.column_label
										snake_case								=			args.column_text
										snake_case								=			args.column_id

										self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""    )
										if args.task == "text_classification":
																snake_case								=			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}"""    )
										snake_case								=			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		,    )
										snake_case								=			None
										if args.validation_data:
																self.logger.info(F"""Loading validation dataset from {args.validation_data}"""    )
																snake_case								=			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		,    )

										snake_case								=			args.validation_split
										snake_case								=			args.train_batch_size
										snake_case								=			args.valid_batch_size
										snake_case								=			args.learning_rate
										snake_case								=			args.adam_epsilon

				def 		snake_case (				self    ):






										"""simple docstring"""

										if self.framework == "tf":
																return self.run_tf()
										return self.run_torch()

				def 		snake_case (				self    ):






										"""simple docstring"""

										raise NotImplementedError

				def 		snake_case (				self    ):






										"""simple docstring"""

										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    )


