
"""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 __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			def   lowerCamelCase__  (					self    :       Union[str, 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=_snake_case							, )
								assert hasattr(self							, "env"						)




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       str						)   ->			int:







								"""simple docstring"""
								# configuration for running training on smdistributed Model Parallel
								A_								=    {
								    "enabled": True,
								    "processes_per_host": 8,
								}
								A_								=    {
								    "enabled": True,
								    "parameters": {
								        "microbatches": 4,
								        "placement_strategy": "spread",
								        "pipeline": "interleaved",
								        "optimize": "speed",
								        "partitions": 4,
								        "ddp": True,
								    },
								}

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

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




			def   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       Optional[int]						)   ->			Optional[int]:







								"""simple docstring"""
								TrainingJobAnalytics(_snake_case						).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'						)






			@parameterized.expand([(1,)]						)
			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Tuple						)   ->			Dict:







								"""simple docstring"""
								# create estimator
								A_								=    self.create_estimator(_snake_case						)

								# run training
								estimator.fit()

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

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






"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					, __a = None					, __a = None					):

					'''simple docstring'''





					if start is None:
										A_								=    0

					if end is None:
										A_								=    len(__a					) - 1

					if start >= end:
										return

					A_								=    (start + end) // 2

					slowsort(__a					, __a					, __a					)
					slowsort(__a					, mid + 1					, __a					)

					if sequence[end] < sequence[mid]:
										A_			,     A_								=    sequence[mid], sequence[end]

					slowsort(__a					, __a					, end - 1					)


if __name__ == "__main__":
		from doctest import testmod

		testmod()




