


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": 1_600, "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": 1_600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]					)
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def A   (					self    ) ->      List[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 A   (					self			,							snake_case_    ) ->      List[Any]:







								'''simple docstring'''

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

								__lowercase			       = {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

								__lowercase			       = '''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''': 5_0_0,
								    }			,							metric_definitions=self.env.metric_definitions			,							distribution=snake_case_			,							py_version='''py36'''			,							)






					def A   (					self			,							snake_case_    ) ->      int:







								'''simple docstring'''

								TrainingJobAnalytics(snake_case_    ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'    )






					@parameterized.expand([(1,)]    )
					def A   (					self			,							snake_case_    ) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = self.create_estimator(snake_case_    )

								# run training
								estimator.fit()

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

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






a			:			Any												=					[
    '''Audio''',
    '''Array2D''',
    '''Array3D''',
    '''Array4D''',
    '''Array5D''',
    '''ClassLabel''',
    '''Features''',
    '''Sequence''',
    '''Value''',
    '''Image''',
    '''Translation''',
    '''TranslationVariableLanguages''',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages


