



"""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  :						Tuple    							=      False
__SCREAMING_SNAKE_CASE  :						Dict    							=      False



def        lowerCAmelCase_(   lowercase_     :							Namespace							)	->       Optional[int]:
				return TrainCommand(lowercase_							)




class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					@staticmethod
					def 			snake_case__      (    lowerCamelCase__   ):
									_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=3_2				,       help='''Batch size for training.'''   )
									train_parser.add_argument('''--valid_batch_size'''				,       type=lowerCamelCase__				,       default=6_4				,       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__   ):
									_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 			snake_case__      (    self   ):
									if self.framework == "tf":
													return self.run_tf()
									return self.run_torch()

					def 			snake_case__      (    self   ):
									raise NotImplementedError





					def 			snake_case__      (    self   ):
									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"""
from typing import Any


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				data
									_lowerCamelCase        =				None


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self   ):
									_lowerCamelCase        =				None

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.head
									while temp is not None:
													print(temp.data				,       end=''' '''   )
													_lowerCamelCase        =				temp.next
									print()

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				Node(lowerCamelCase__   )
									_lowerCamelCase        =				self.head
									_lowerCamelCase        =				new_node





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									if node_data_a == node_data_a:
													return
									else:
													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													if node_a is None or node_a is None:
																	return

													_lowerCamelCase  ,     _lowerCamelCase        =				node_a.data, node_a.data


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      LinkedList()
			for i in range(5, 0, -1):
						ll.push(i)

			ll.print_list()

			ll.swap_nodes(1, 4)
			print('''After swapping''')
			ll.print_list()
