from __future__ import annotations



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       float ,     __UpperCamelCase    :       float ,     __UpperCamelCase    :       float	)							->					float:







  """simple docstring"""





  if days_between_payments <= 0:
    raise ValueError("""days_between_payments must be > 0"""	)
  if daily_interest_rate < 0:
    raise ValueError("""daily_interest_rate must be >= 0"""	)
  if principal <= 0:
    raise ValueError("""principal must be > 0"""	)
  return principal * daily_interest_rate * days_between_payments



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       float ,     __UpperCamelCase    :       float ,     __UpperCamelCase    :       float ,     )							->					float:







  """simple docstring"""





  if number_of_compounding_periods <= 0:
    raise ValueError("""number_of_compounding_periods must be > 0"""	)
  if nominal_annual_interest_rate_percentage < 0:
    raise ValueError("""nominal_annual_interest_rate_percentage must be >= 0"""	)
  if principal <= 0:
    raise ValueError("""principal must be > 0"""	)

  return principal * (
      (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
      - 1
  )



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       float ,     __UpperCamelCase    :       float ,     __UpperCamelCase    :       float ,     )							->					float:







  """simple docstring"""





  if number_of_years <= 0:
    raise ValueError("""number_of_years must be > 0"""	)
  if nominal_annual_percentage_rate < 0:
    raise ValueError("""nominal_annual_percentage_rate must be >= 0"""	)
  if principal <= 0:
    raise ValueError("""principal must be > 0"""	)

  return compound_interest(
      __UpperCamelCase ,     nominal_annual_percentage_rate / 3_65 ,     number_of_years * 3_65	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

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







class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args      )
     assert isinstance(converted_args["""model_name_or_path"""]       ,	_lowercase      )
     assert isinstance(converted_args["""do_train"""]       ,	_lowercase      )
     assert isinstance(converted_args["""epochs"""]       ,	_lowercase      )
     assert isinstance(converted_args["""learning_rate"""]       ,	_lowercase      )
     assert isinstance(converted_args["""max_steps"""]       ,	_lowercase      )

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