




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
__A        =    False
__A        =    False







def 			lowerCamelCase_		(       UpperCamelCase__				:      Namespace      )     ->       Optional[int]:





				"""simple docstring"""


				return TrainCommand(UpperCamelCase__      )







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			@staticmethod
			def       lowercase_    (      lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    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__							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    logging.get_logger('transformers-cli/training'							)

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

							os.makedirs(args.output			,						exist_ok=lowerCamelCase__							)
							__lowerCamelCase		       =    args.output

							__lowerCamelCase		       =    args.column_label
							__lowerCamelCase		       =    args.column_text
							__lowerCamelCase		       =    args.column_id

							self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""							)
							if args.task == "text_classification":
											__lowerCamelCase		       =    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		       =    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		       =    None
							if args.validation_data:
											self.logger.info(f"""Loading validation dataset from {args.validation_data}"""							)
											__lowerCamelCase		       =    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		       =    args.validation_split
							__lowerCamelCase		       =    args.train_batch_size
							__lowerCamelCase		       =    args.valid_batch_size
							__lowerCamelCase		       =    args.learning_rate
							__lowerCamelCase		       =    args.adam_epsilon


			def       lowercase_    (      self							)    ->      int:



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


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							raise NotImplementedError






			def       lowercase_    (      self							)    ->      List[str]:



							'''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							)








from math import sqrt







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       bool:





				"""simple docstring"""


				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5 ,     int(sqrt(UpperCamelCase__      ) + 1      ) ,     6      ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True







def 			lowerCamelCase_		(       UpperCamelCase__				:      int = 1_0001      )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    0
				__lowerCamelCase		       =    1
				while count != nth and number < 3:
								number += 1
								if is_prime(UpperCamelCase__      ):
												count += 1
				while count != nth:
								number += 2
								if is_prime(UpperCamelCase__      ):
												count += 1
				return number


if __name__ == "__main__":
					print(f'''{solution() = }''')


