



from math import factorial





def 							A     (					_UpperCAmelCase    :       int = 100   )    ->			int:







			'''simple docstring'''
			return sum(map(_UpperCAmelCase							,       str(factorial(_UpperCAmelCase   )   )   )   )


if __name__ == "__main__":
							print(solution(int(input("Enter the Number: ").strip())))





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''': 6_5_0, '''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''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]						)
class       __lowerCAmelCase   (      unittest.TestCase						):



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Any:



						"""simple docstring"""






						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    _lowerCamelCase	(						self       :  Any			,		A       :  Tuple=1)       ->   List[str]:



						"""simple docstring"""






						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    _lowerCamelCase	(						self       :  Dict			,		A       :  int)       ->   str:



						"""simple docstring"""






						TrainingJobAnalytics(A).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.create_estimator()

						# run training
						estimator.fit()

						# result dataframe
						_UpperCAmelCase			=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

						# extract kpis
						_UpperCAmelCase			=       list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'])
						_UpperCAmelCase			=       list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'])
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						_UpperCAmelCase			=       (
						    Session().describe_training_job(estimator.latest_training_job.name).get('TrainingTimeInSeconds'			,		99_99_99)
						)

						# 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)
