
'''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
lowercase			:				Tuple    		=			False
lowercase			:				str    		=			False







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    return TrainCommand(snake_case__						)







class  A				(							__snake_case						):





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         A       :   List[Any]					=						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=32						,			help='''Batch size for training.'''	)
         train_parser.add_argument('''--valid_batch_size'''						,			type=SCREAMING_SNAKE_CASE						,			default=64						,			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-08						,			help='''Epsilon for Adam optimizer.'''	)
         train_parser.set_defaults(func=SCREAMING_SNAKE_CASE	)





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Any					=						logging.get_logger('''transformers-cli/training'''	)

         A       :   List[Any]					=						'''tf''' if is_tf_available() else '''torch'''

         os.makedirs(args.output						,			exist_ok=SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						args.output

         A       :   Any					=						args.column_label
         A       :   Optional[Any]					=						args.column_text
         A       :   Optional[Any]					=						args.column_id

         self.logger.info(F'Loading {args.task} pipeline for {args.model}'	)
         if args.task == "text_classification":
             A       :   int					=						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       :   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						,			)
         A       :   Any					=						None
         if args.validation_data:
             self.logger.info(F'Loading validation dataset from {args.validation_data}'	)
             A       :   Tuple					=						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       :   Optional[int]					=						args.train_batch_size
         A       :   Dict					=						args.valid_batch_size
         A       :   Any					=						args.learning_rate
         A       :   Any					=						args.adam_epsilon





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         raise NotImplementedError





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """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	)











"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def      __lowerCamelCase   (							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Tuple						=  ArgumentParser('''Accelerate CLI tool''' ,   usage='''accelerate <command> [<args>]''' ,   allow_abbrev=a_							)
      __SCREAMING_SNAKE_CASE   :str						=  parser.add_subparsers(help='''accelerate command helpers'''							)

      # Register commands
      get_config_parser(subparsers=a_							)
      env_command_parser(subparsers=a_							)
      launch_command_parser(subparsers=a_							)
      tpu_command_parser(subparsers=a_							)
      test_command_parser(subparsers=a_							)

      # Let's go
      __SCREAMING_SNAKE_CASE   :int						=  parser.parse_args()

      if not hasattr(a_ ,   '''func'''							):
            parser.print_help()
            exit(1							)

      # Run
      args.func(a_							)


if __name__ == "__main__":
  main()