

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 				__lowercase				(			self       )    ->    Optional[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=_a       ,							)
												assert hasattr(self       ,							'''env'''       )


							def 				__lowercase				(			self       ,							_a       )    ->    Optional[Any]:
												# configuration for running training on smdistributed Model Parallel
												_a	:    Optional[int]         =		{
												    '''enabled''': True,
												    '''processes_per_host''': 8,
												}
												_a	:    List[Any]         =		{
												    '''enabled''': True,
												    '''parameters''': {
												        '''microbatches''': 4,
												        '''placement_strategy''': '''spread''',
												        '''pipeline''': '''interleaved''',
												        '''optimize''': '''speed''',
												        '''partitions''': 4,
												        '''ddp''': True,
												    },
												}

												_a	:    Union[str, Any]         =		{'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

												_a	:    int         =		'''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=_a       ,							instance_type=self.instance_type       ,							debugger_hook_config=_a       ,							hyperparameters={
												        **self.env.hyperparameters,
												        '''model_name_or_path''': self.model_name_or_path,
												        '''max_steps''': 5_0_0,
												    }       ,							metric_definitions=self.env.metric_definitions       ,							distribution=_a       ,							py_version='''py36'''       ,							)


							def 				__lowercase				(			self       ,							_a       )    ->    str:
												TrainingJobAnalytics(_a       ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""       )




							@parameterized.expand([(1,)]       )
							def 				__lowercase				(			self       ,							_a       )    ->    List[str]:
												# create estimator
												_a	:    Union[str, Any]         =		self.create_estimator(_a       )

												# run training
												estimator.fit()

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

												# extract kpis
												_a	:    Tuple         =		list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']       )
												_a	:    Tuple         =		list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']       )
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												_a	:    Union[str, Any]         =		(
												    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       )






from diffusers.utils.testing_utils import require_onnxruntime

@require_onnxruntime
class 				UpperCAmelCase_		:
							"""simple docstring"""







							pass



