




lowercase__							:	Optional[Any]											=		[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	0
	while number:
		# Increased Speed Slightly by checking every 5 digits together.
		sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
		number //= 100000

	return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowercase__							:	list[bool | None]											=		[None] * 1000_0000
lowercase__							:	Tuple											=		True
lowercase__							:	Any											=		False


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	if CHAINS[number - 1] is not None:
		return CHAINS[number - 1]  # type: ignore

	snake_case_						       =	chain(next_number(_A				)				)
	snake_case_						       =	number_chain

	while number < 10000000:
		snake_case_						       =	number_chain
		number *= 10

	return number_chain


def 		lowerCamelCase__       (						_A = 10000000				):







	'''simple docstring'''





	for i in range(1	,      _A				):
		if CHAINS[i] is None:
			chain(i + 1				)

	return CHAINS[:number].count(_A				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f'''{solution() = }''')






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

import pytest
from parameterized import parameterized, 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_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]     )
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						def 			snake_case__		(		self				:	int		):






							"""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=__lowercase			,      )
							assert hasattr(self			,      "env"		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	{
							    "enabled": True,
							    "processes_per_host": 8,
							}
							snake_case_						       =	{
							    "enabled": True,
							    "parameters": {
							        "microbatches": 4,
							        "placement_strategy": "spread",
							        "pipeline": "interleaved",
							        "optimize": "speed",
							        "partitions": 4,
							        "ddp": True,
							    },
							}

							snake_case_						       =	{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

							snake_case_						       =	"trainer" if self.script == "run_glue.py" else "smtrainer"
							# 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}-{instance_count}-smp-{name_extension}"			,      instance_count=__lowercase			,      instance_type=self.instance_type			,      debugger_hook_config=__lowercase			,      hyperparameters={
							        **self.env.hyperparameters,
							        "model_name_or_path": self.model_name_or_path,
							        "max_steps": 5_00,
							    }			,      metric_definitions=self.env.metric_definitions			,      distribution=__lowercase			,      py_version="py36"			,      )






						def 			snake_case__		(		self				:	Tuple			,      __lowercase				:	Dict		):






							"""simple docstring"""






							TrainingJobAnalytics(__lowercase		).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"		)






						@parameterized.expand([(1,)]		)
						def 			snake_case__		(		self				:	Any			,      __lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.create_estimator(__lowercase		)

							# run training
							estimator.fit()

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

							# extract kpis
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							snake_case_						       =	(
							    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}			,      __lowercase		)
