


"""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
a__				:							Tuple						=     False
a__				:							Optional[int]						=     False




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					return TrainCommand(lowerCAmelCase_   )




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	def   UpperCAmelCase_      ( UpperCAmelCase__			:				ArgumentParser   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				parser.add_parser("train"			,			help="CLI tool to train a model on a task."   )

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

						train_parser.add_argument(
						    "--task"			,			type=UpperCAmelCase__			,			default="text_classification"			,			help="Task to train the model on."   )
						train_parser.add_argument(
						    "--model"			,			type=UpperCAmelCase__			,			default="bert-base-uncased"			,			help="Model's name or path to stored model."   )
						train_parser.add_argument("--train_batch_size"			,			type=UpperCAmelCase__			,			default=3_2			,			help="Batch size for training."   )
						train_parser.add_argument("--valid_batch_size"			,			type=UpperCAmelCase__			,			default=6_4			,			help="Batch size for validation."   )
						train_parser.add_argument("--learning_rate"			,			type=UpperCAmelCase__			,			default=3E-5			,			help="Learning rate."   )
						train_parser.add_argument("--adam_epsilon"			,			type=UpperCAmelCase__			,			default=1E-08			,			help="Epsilon for Adam optimizer."   )
						train_parser.set_defaults(func=UpperCAmelCase__   )




	def __init__( self			:				List[str]			,			UpperCAmelCase__			:				Namespace   )       ->	str:
						__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=UpperCAmelCase__   )
						__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   UpperCAmelCase_      ( self			:				Tuple   )       ->	Optional[Any]:
						if self.framework == "tf":
											return self.run_tf()
						return self.run_torch()




	def   UpperCAmelCase_      ( self			:				int   )       ->	str:
						raise NotImplementedError




	def   UpperCAmelCase_      ( self			:				Any   )       ->	Union[str, 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"""







def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if upper_limit < 0:
										raise ValueError("Limit for the Catalan sequence must be ≥ 0"   )

					__SCREAMING_SNAKE_CASE						   =				[0] * (upper_limit + 1)

					# Base case: C(0) = C(1) = 1
					__SCREAMING_SNAKE_CASE						   =				1
					if upper_limit > 0:
										__SCREAMING_SNAKE_CASE						   =				1

					# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
					for i in range(2  ,						upper_limit + 1   ):
										for j in range(lowerCAmelCase_   ):
															catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

					return catalan_list


if __name__ == "__main__":
		print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
		print('''\n*** Enter -1 at any time to quit ***''')
		print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
		try:
				while True:
						a__				:							List[str]						=     int(input().strip())
						if N < 0:
								print('''\n********* Goodbye!! ************''')
								break
						else:
								print(F"The Catalan numbers from 0 through {N} are:")
								print(catalan_numbers(N))
								print('''Try another upper limit for the sequence: ''', end='''''')
		except (NameError, ValueError):
				print('''\n********* Invalid input, goodbye! ************\n''')

		import doctest

		doctest.testmod()
