

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 					lowercase							(			a_     ):
				__SCREAMING_SNAKE_CASE   :					int			     =     ComputeEnvironment.AMAZON_SAGEMAKER
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     True
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     '''ml.p3.2xlarge'''
				__SCREAMING_SNAKE_CASE   :					Any			     =     '''accelerate_sagemaker_execution_role'''
				__SCREAMING_SNAKE_CASE   :					Any			     =     '''hf-sm'''
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     '''us-east-1'''
				__SCREAMING_SNAKE_CASE   :					Any			     =     1
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''accelerate-sagemaker-1'''
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     '''1.6'''
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     '''4.4'''
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''train.py'''
				__SCREAMING_SNAKE_CASE   :					str			     =     [
				    '''--model_name_or_path''',
				    '''bert''',
				    '''--do_train''',
				    '''False''',
				    '''--epochs''',
				    '''3''',
				    '''--learning_rate''',
				    '''5e-5''',
				    '''--max_steps''',
				    '''50.5''',
				]
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     [
				    '''--model_name_or_path''',
				    '''bert''',
				    '''--do_train''',
				    '''--do_test''',
				    '''False''',
				    '''--do_predict''',
				    '''--epochs''',
				    '''3''',
				    '''--learning_rate''',
				    '''5e-5''',
				    '''--max_steps''',
				    '''50.5''',
				]







class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											# If no defaults are changed, `to_kwargs` returns an empty dict.
											snake_case_     							=       _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args			)
											assert isinstance(converted_args['model_name_or_path'] ,  lowerCAmelCase_			)
											assert isinstance(converted_args['do_train'] ,  lowerCAmelCase_			)
											assert isinstance(converted_args['epochs'] ,  lowerCAmelCase_			)
											assert isinstance(converted_args['learning_rate'] ,  lowerCAmelCase_			)
											assert isinstance(converted_args['max_steps'] ,  lowerCAmelCase_			)

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








'''simple docstring'''





from __future__ import annotations

from functools import lru_cache
from math import ceil

UpperCAmelCase__  			=				1_0_0

UpperCAmelCase__  			=				set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase__  			=				42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
					if prime not in primes:
										continue
					primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))






@lru_cache(maxsize=100 )
def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			__A=	set()
			__A=	42
			__A=	42

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime ):
									ret.add(sub * prime )

			return ret






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int = 5000 ):






			"""simple docstring"""




			for number_to_partition in range(1,_SCREAMING_SNAKE_CASE ):
						if len(partition(_SCREAMING_SNAKE_CASE ) ) > number_unique_partitions:
									return number_to_partition
			return None


if __name__ == "__main__":
					print(F"""{solution() = }""")



