






"""simple docstring"""
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''': 16_00, '''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''': 16_00, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]     )
class 			UpperCamelCase_       (unittest.TestCase     ):





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Any:
				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=_lowerCamelCase	,	)
				assert hasattr(self	,	"env"						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: Union[str, Any]						)						->			Dict:
				# configuration for running training on smdistributed Model Parallel
				UpperCAmelCase_     : Dict			     =					{
				    "enabled": True,
				    "processes_per_host": 8,
				}
				UpperCAmelCase_     : List[str]			     =					{
				    "enabled": True,
				    "parameters": {
				        "microbatches": 4,
				        "placement_strategy": "spread",
				        "pipeline": "interleaved",
				        "optimize": "speed",
				        "partitions": 4,
				        "ddp": True,
				    },
				}

				UpperCAmelCase_     : Dict			     =					{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

				UpperCAmelCase_     : Dict			     =					"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=_lowerCamelCase	,	instance_type=self.instance_type	,	debugger_hook_config=_lowerCamelCase	,	hyperparameters={
				        **self.env.hyperparameters,
				        "model_name_or_path": self.model_name_or_path,
				        "max_steps": 500,
				    }	,	metric_definitions=self.env.metric_definitions	,	distribution=_lowerCamelCase	,	py_version="py36"	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: Optional[Any]						)						->			Optional[Any]:
				TrainingJobAnalytics(_lowerCamelCase						).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""						)





	@parameterized.expand([(1,)]						)
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: Dict						)						->			List[Any]:
				# create estimator
				UpperCAmelCase_     : Dict			     =					self.create_estimator(_lowerCamelCase						)

				# run training
				estimator.fit()

				# result dataframe
				UpperCAmelCase_     : Optional[int]			     =					TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

				# extract kpis
				UpperCAmelCase_     : List[str]			     =					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]						)
				UpperCAmelCase_     : Union[str, Any]			     =					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]						)
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				UpperCAmelCase_     : Dict			     =					(
				    Session().describe_training_job(estimator.latest_training_job.name						).get("TrainingTimeInSeconds"	,	999_999						)
				)

				# 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}	,	_lowerCamelCase						)











"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int = 1_00    ):





  '''simple docstring'''





  lowercase							    =       0
  lowercase							    =       0
  for i in range(1		,					n + 1    ):
    sum_of_squares += i**2
    sum_of_ints += i
  return sum_of_ints**2 - sum_of_squares


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


