

'''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							:				List[Any]     	=			False
SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			False




def 							_UpperCamelCase				(   lowerCAmelCase__:					Namespace   )      -> Optional[int]:
							return TrainCommand(lowerCAmelCase__   )






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@staticmethod
				def   a__					(    _lowercase       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				=				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-08,     help='Epsilon for Adam optimizer.'       )
											train_parser.set_defaults(func=_lowercase       )



				def __init__(    self,     _lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				logging.get_logger('transformers-cli/training'       )

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

											os.makedirs(args.output,     exist_ok=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				args.output

											SCREAMING_SNAKE_CASE_				=				args.column_label
											SCREAMING_SNAKE_CASE_				=				args.column_text
											SCREAMING_SNAKE_CASE_				=				args.column_id

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

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



				def   a__					(    self       )     ->							Union[str, Any]:
											if self.framework == "tf":
																		return self.run_tf()
											return self.run_torch()



				def   a__					(    self       )     ->							int:
											raise NotImplementedError





				def   a__					(    self       )     ->							Optional[Any]:
											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'''

SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			0  # The first color of the flag.
SCREAMING_SNAKE_CASE							:				List[str]     	=			1  # The second color of the flag.
SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			2  # The third color of the flag.
SCREAMING_SNAKE_CASE							:				Tuple     	=			(red, white, blue)




def 							_UpperCamelCase				(   lowerCAmelCase__:					list   )      -> list:
							if not sequence:
														return []
							if len(lowerCAmelCase__   ) == 1:
														return list(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				0
							SCREAMING_SNAKE_CASE_				=				len(lowerCAmelCase__   ) - 1
							SCREAMING_SNAKE_CASE_				=				0
							while mid <= high:
														if sequence[mid] == colors[0]:
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				sequence[mid], sequence[low]
																					low += 1
																					mid += 1
														elif sequence[mid] == colors[1]:
																					mid += 1
														elif sequence[mid] == colors[2]:
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				sequence[high], sequence[mid]
																					high -= 1
														else:
																					SCREAMING_SNAKE_CASE_				=				F"""The elements inside the sequence must contains only {colors} values"""
																					raise ValueError(lowerCAmelCase__   )
							return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE							:				str     	=			input("Enter numbers separated by commas:\n").strip()
							SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(item.strip()) for item in user_input.split(",")]
							print(f"{dutch_national_flag_sort(unsorted)}")





