




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__				:     int 		=						False
lowercase__				:     Optional[Any] 		=						False


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return TrainCommand(_A							)






class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						@staticmethod
						def        snake_case__						(						__lowercase							:     ArgumentParser						):






							"""simple docstring"""






							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							:     int       ,	__lowercase							:     Namespace						):






							"""simple docstring"""






							snake_case_		    =     logging.get_logger("transformers-cli/training"						)

							snake_case_		    =     "tf" if is_tf_available() else "torch"

							os.makedirs(args.output       ,	exist_ok=__lowercase						)
							snake_case_		    =     args.output

							snake_case_		    =     args.column_label
							snake_case_		    =     args.column_text
							snake_case_		    =     args.column_id

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

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






						def        snake_case__						(						self							:     Tuple						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							raise NotImplementedError






						def        snake_case__						(						self							:     int						):






							"""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 functools


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	if not isinstance(_A     ,	_A							) or not all(isinstance(_A     ,	_A							) for day in days							):
		raise ValueError("The parameter days should be a list of integers"							)

	if len(_A							) != 3 or not all(isinstance(_A     ,	_A							) for cost in costs							):
		raise ValueError("The parameter costs should be a list of three integers"							)

	if len(_A							) == 0:
		return 0

	if min(_A							) <= 0:
		raise ValueError("All days elements should be greater than 0"							)

	if max(_A							) >= 366:
		raise ValueError("All days elements should be less than 366"							)

	snake_case_		    =     set(_A							)

	@functools.cache
	def dynamic_programming(_A							) -> int:
		if index > 365:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1							)

		return min(
		    costs[0] + dynamic_programming(index + 1							)     ,	costs[1] + dynamic_programming(index + 7							)     ,	costs[2] + dynamic_programming(index + 30							)     ,	)

	return dynamic_programming(1							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
