'''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
_lowercase						: List[str]						=						False
_lowercase						: List[str]						=						False




def 							lowerCamelCase	(      UpperCAmelCase__       :					List[Any] )			->      List[str]:
							return TrainCommand(A__ )





class     __magic_name__						(    lowerCamelCase__):







			@staticmethod
			def 	SCREAMING_SNAKE_CASE_     (	lowercase_				:  ArgumentParser    ):
										lowercase_					:			Dict										=       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    ):
										lowercase_					:			List[Any]										=       logging.get_logger("""transformers-cli/training"""    )

										lowercase_					:			List[str]										=       """tf""" if is_tf_available() else """torch"""

										os.makedirs(args.output				,			exist_ok=lowercase__    )
										lowercase_					:			Union[str, Any]										=       args.output

										lowercase_					:			Optional[Any]										=       args.column_label
										lowercase_					:			Optional[Any]										=       args.column_text
										lowercase_					:			Union[str, Any]										=       args.column_id

										self.logger.info(f'''Loading {args.task} pipeline for {args.model}'''    )
										if args.task == "text_classification":
																	lowercase_					:			Optional[int]										=       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}'''    )
										lowercase_					:			Any										=       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				,			)
										lowercase_					:			Tuple										=       None
										if args.validation_data:
																	self.logger.info(f'''Loading validation dataset from {args.validation_data}'''    )
																	lowercase_					:			str										=       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				,			)

										lowercase_					:			Union[str, Any]										=       args.validation_split
										lowercase_					:			int										=       args.train_batch_size
										lowercase_					:			str										=       args.valid_batch_size
										lowercase_					:			List[str]										=       args.learning_rate
										lowercase_					:			Dict										=       args.adam_epsilon







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										raise NotImplementedError







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										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 __future__ import annotations


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = len(A__      )
 # We need to create solution object to save path.
 __lowercase							  = [[0 for _ in range(A__      )] for _ in range(A__      )]
 __lowercase							  = run_maze(A__     ,				0     ,				0     ,				A__      )
 if solved:
  print('''\n'''.join(str(A__      ) for row in solutions      )      )
 else:
  print('''No solution exists!'''      )
 return solved


def 		_A     (    A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = len(A__      )
 # Final check point.
 if i == j == (size - 1):
  __lowercase							  = 1
  return True

 __lowercase							  = (not i < 0) and (not j < 0)  # Check lower bounds
 __lowercase							  = (i < size) and (j < size)  # Check upper bounds

 if lower_flag and upper_flag:
  # check for already visited and block points.
  __lowercase							  = (not solutions[i][j]) and (not maze[i][j])
  if block_flag:
   # check visited
   __lowercase							  = 1

   # check for directions
   if (
       run_maze(A__     ,				i + 1     ,				A__     ,				A__      )
       or run_maze(A__     ,				A__     ,				j + 1     ,				A__      )
       or run_maze(A__     ,				i - 1     ,				A__     ,				A__      )
       or run_maze(A__     ,				A__     ,				j - 1     ,				A__      )
   ):
    return True

   __lowercase							  = 0
   return False
 return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()

