



from math import factorial

A__  :  str								=					{str(d): factorial(d) for d in range(10)}


def 			UpperCamelCase(  __UpperCamelCase							:		int ):
		return sum(DIGIT_FACTORIAL[d] for d in str(__UpperCamelCase ) )


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     str									= 7 * factorial(9 ) + 1
		return sum(i for i in range(3     ,__UpperCamelCase ) if sum_of_digit_factorial(__UpperCamelCase ) == i )


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










import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
						from sagemaker import Session, TrainingJobAnalytics
						from sagemaker.huggingface import HuggingFace



@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''						,'''False'''						)						) is not True						,reason='''Skipping test because should only be run when releasing minor transformers version'''						,)
@pytest.mark.usefixtures('''sm_env'''						)
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]						)
class 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       int):
									if self.framework == "pytorch":
											subprocess.run(
											    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()				,				encoding='''utf-8'''				,				check=A_				,				)
									assert hasattr(self				,				'''env''')




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       str=1):
									# creates estimator
									return HuggingFace(
									    entry_point=self.script				,				source_dir=self.env.test_path				,				role=self.env.role				,				image_uri=self.env.image_uri				,				base_job_name=F"""{self.env.base_job_name}-single"""				,				instance_count=A_				,				instance_type=self.instance_type				,				debugger_hook_config=A_				,				hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}				,				metric_definitions=self.env.metric_definitions				,				py_version='''py36'''				,				)




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Optional[Any]):
									TrainingJobAnalytics(A_).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







							def    UpperCAmelCase__			(       self     :       int):
									# create estimator
									lowerCAmelCase_			:     List[Any]									= self.create_estimator()

									# run training
									estimator.fit()

									# result dataframe
									lowerCAmelCase_			:     Union[str, Any]									= TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

									# extract kpis
									lowerCAmelCase_			:     Optional[Any]									= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
									lowerCAmelCase_			:     Optional[int]									= list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									lowerCAmelCase_			:     Dict									= (
									    Session().describe_training_job(estimator.latest_training_job.name).get('''TrainingTimeInSeconds'''				,				9_9_9_9_9_9)
									)

									# assert kpis
									assert train_runtime <= self.results["train_runtime"]
									assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy)
									assert all(t <= self.results['''eval_loss'''] for t in eval_loss)

									# dump tests result into json file to share in PR
									with open(F"""{estimator.latest_training_job.name}.json"""				,				'''w''') as outfile:
											json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}				,				A_)





