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






							'''simple docstring'''





							__UpperCamelCase	:   int			   =					len(_lowerCamelCase) + 1
							__UpperCamelCase	:   int			   =					len(_lowerCamelCase) + 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.
							__UpperCamelCase	:   List[str]			   =					[[0 for i in range(_lowerCamelCase)] for j in range(_lowerCamelCase)]

							# since string of zero length match pattern of zero length
							__UpperCamelCase	:   Optional[Any]			   =					1

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

							# since string of zero length will match with pattern where there
							# is at least one * alternatively
							for j in range(1						,  _lowerCamelCase):
														__UpperCamelCase	:   str			   =					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						,  _lowerCamelCase):
														for j in range(1						,  _lowerCamelCase):
																					if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																												__UpperCamelCase	:   Any			   =					dp[i - 1][j - 1]

																					elif pattern[j - 1] == "*":
																												if dp[i][j - 2] == 1:
																																			__UpperCamelCase	:   str			   =					1
																												elif pattern[j - 2] in (input_string[i - 1], "."):
																																			__UpperCamelCase	:   int			   =					dp[i - 1][j]
																												else:
																																			__UpperCamelCase	:   Optional[int]			   =					0
																					else:
																												__UpperCamelCase	:   Union[str, Any]			   =					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 :")

		lowercase  :					List[str]													=       'aab'
		lowercase  :					Tuple													=       '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}")

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     ComputeEnvironment.AMAZON_SAGEMAKER
	_A								=     True
	_A								=     'ml.p3.2xlarge'
	_A								=     'accelerate_sagemaker_execution_role'
	_A								=     'hf-sm'
	_A								=     'us-east-1'
	_A								=     1
	_A								=     'accelerate-sagemaker-1'
	_A								=     '1.6'
	_A								=     '4.4'
	_A								=     'train.py'
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Any						)		-> Optional[int]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase	:   Optional[int]			   =					_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args						)
								assert isinstance(converted_args["model_name_or_path"]     ,   a						)
								assert isinstance(converted_args["do_train"]     ,   a						)
								assert isinstance(converted_args["epochs"]     ,   a						)
								assert isinstance(converted_args["learning_rate"]     ,   a						)
								assert isinstance(converted_args["max_steps"]     ,   a						)

								with pytest.raises(a						):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args						)