





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       :List[Any]       =  False
__lowercase       :Union[str, Any]       =  False







def       UpperCAmelCase							(     _lowerCamelCase      :  Namespace     ):





						'''simple docstring'''


						return TrainCommand(_lowerCamelCase     )



class    _a     (				lowercase__							):






							"""simple docstring"""

							@staticmethod
							def      A_  (      a				:     ArgumentParser    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					parser.add_parser("train"							,	help="CLI tool to train a model on a task."    )

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

													train_parser.add_argument(
													    "--task"							,	type=a							,	default="text_classification"							,	help="Task to train the model on."    )
													train_parser.add_argument(
													    "--model"							,	type=a							,	default="bert-base-uncased"							,	help="Model's name or path to stored model."    )
													train_parser.add_argument("--train_batch_size"							,	type=a							,	default=32							,	help="Batch size for training."    )
													train_parser.add_argument("--valid_batch_size"							,	type=a							,	default=64							,	help="Batch size for validation."    )
													train_parser.add_argument("--learning_rate"							,	type=a							,	default=3E-5							,	help="Learning rate."    )
													train_parser.add_argument("--adam_epsilon"							,	type=a							,	default=1E-08							,	help="Epsilon for Adam optimizer."    )
													train_parser.set_defaults(func=a    )

							def __init__(      self				:     Union[str, Any]							,	a				:     Namespace    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					logging.get_logger("transformers-cli/training"    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					"tf" if is_tf_available() else "torch"

													os.makedirs(args.output							,	exist_ok=a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					args.output

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					args.column_label
													SCREAMING_SNAKE_CASE__     :				int   =					args.column_text
													SCREAMING_SNAKE_CASE__     :				int   =					args.column_id

													self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""    )
													if args.task == "text_classification":
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					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__     :				Tuple   =					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__     :				str   =					None
													if args.validation_data:
																			self.logger.info(f"""Loading validation dataset from {args.validation_data}"""    )
																			SCREAMING_SNAKE_CASE__     :				Dict   =					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__     :				Tuple   =					args.validation_split
													SCREAMING_SNAKE_CASE__     :				str   =					args.train_batch_size
													SCREAMING_SNAKE_CASE__     :				Tuple   =					args.valid_batch_size
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					args.learning_rate
													SCREAMING_SNAKE_CASE__     :				List[str]   =					args.adam_epsilon

							def      A_  (      self				:     Dict    )	->Dict:
													if self.framework == "tf":
																			return self.run_tf()
													return self.run_torch()

							def      A_  (      self				:     int    )	->Union[str, Any]:
													raise NotImplementedError






							def      A_  (      self				:     Tuple    )	->str:
													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 __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list[int]			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if len(_lowerCamelCase     ) < k or k < 0:
												raise ValueError("Invalid Input"     )
						SCREAMING_SNAKE_CASE__     :				int   =					sum(array[:k]     )
						for i in range(len(_lowerCamelCase     ) - k     ):
												SCREAMING_SNAKE_CASE__     :				str   =					current_sum - array[i] + array[i + k]
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					max(_lowerCamelCase			,   _lowerCamelCase     )
						return max_sum


if __name__ == "__main__":
				from doctest import testmod
				from random import randint

				testmod()
				__lowercase       :List[str]       =  [randint(-1_000, 1_000) for i in range(100)]
				__lowercase       :Any       =  randint(0, 110)
				print(f"The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}")