



'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						bool:







	"""simple docstring"""
	lowerCAmelCase   =     len(_SCREAMING_SNAKE_CASE							) + 1
	lowerCAmelCase   =     len(_SCREAMING_SNAKE_CASE							) + 1

	# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
	# length i of input_string matches with prefix string of length j of
	# given pattern.
	# "dp" stands for dynamic programming.
	lowerCAmelCase   =     [[0 for i in range(_SCREAMING_SNAKE_CASE							)] for j in range(_SCREAMING_SNAKE_CASE							)]

	# since string of zero length match pattern of zero length
	lowerCAmelCase   =     1

	# since pattern of zero length will never match with string of non-zero length
	for i in range(1							,		_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     0

	# since string of zero length will match with pattern where there
	# is at least one * alternatively
	for j in range(1							,		_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     dp[0][j - 2] if pattern[j - 1] == """*""" else 0

	# now using bottom-up approach to find for all remaining lengths
	for i in range(1							,		_SCREAMING_SNAKE_CASE							):
		for j in range(1							,		_SCREAMING_SNAKE_CASE							):
			if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
				lowerCAmelCase   =     dp[i - 1][j - 1]

			elif pattern[j - 1] == "*":
				if dp[i][j - 2] == 1:
					lowerCAmelCase   =     1
				elif pattern[j - 2] in (input_string[i - 1], "."):
					lowerCAmelCase   =     dp[i - 1][j]
				else:
					lowerCAmelCase   =     0
			else:
				lowerCAmelCase   =     0

	return bool(dp[-1][-1]							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# inputing the strings
				# input_string = input("input a string :")
				# pattern = input("input a pattern :")

				UpperCAmelCase     		=   'aab'
				UpperCAmelCase     		=   'c*a*b'

				# using function to check whether given string matches the given pattern
				if match_pattern(input_string, pattern):
								print(F'''{input_string} matches the given pattern {pattern}''')
				else:
								print(F'''{input_string} does not match with the given pattern {pattern}''')




'''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
UpperCAmelCase     		=   False
UpperCAmelCase     		=   False






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Namespace							)      ->						Tuple:







	"""simple docstring"""
	return TrainCommand(_SCREAMING_SNAKE_CASE							)





class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						@staticmethod
						def     __snake_case   ( A_				)     ->     Optional[int]:
							lowerCAmelCase   =     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     ,	A_				)     ->     Tuple:
							lowerCAmelCase   =     logging.get_logger("""transformers-cli/training"""				)

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

							os.makedirs(args.output     ,	exist_ok=A_				)
							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				)     ->     Optional[int]:
							if self.framework == "tf":
								return self.run_tf()
							return self.run_torch()


						def     __snake_case   ( self				)     ->     Tuple:
							raise NotImplementedError




						def     __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				)