





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
__lowerCAmelCase :Tuple     =    False
__lowerCAmelCase :Any     =    False







def    A				(							UpperCAmelCase				):
					return TrainCommand(UpperCAmelCase				)






class        _a(    __A    ):







		@staticmethod
		def        lowercase       (    __snake_case				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   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 ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   logging.get_logger("transformers-cli/training"				)

							_snake_case       :					List[str]				       =   "tf" if is_tf_available() else "torch"

							os.makedirs(args.output ,						exist_ok=__snake_case				)
							_snake_case       :					List[str]				       =   args.output

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

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

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







		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

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







		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							raise NotImplementedError







		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''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				)






import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


__lowerCAmelCase :str     =    logging.get_logger(__name__)






class        _a(    __A    ):







		def __init__(    self ,						*__snake_case ,						**__snake_case				)      ->      None:


							'''simple docstring'''

							warnings.warn(
							    "The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
							    " Please use VideoMAEImageProcessor instead." ,						__snake_case ,						)
							super().__init__(*__snake_case ,						**__snake_case				)