


"""simple docstring"""






from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
	import accelerate


def __lowercase     (      lowerCamelCase_  :      List[str]  ):
				if not is_accelerate_available():
								return method
				SCREAMING_SNAKE_CASE__     							=				version.parse(accelerate.__version__  ).base_version
				if version.parse(lowerCamelCase_  ) < version.parse("0.17.0"  ):
								return method

				def wrapper(self  :      List[Any]						,					*lowerCamelCase_  :      Optional[Any]						,					**lowerCamelCase_  :      Tuple  ):
								if hasattr(self						,					"_hf_hook"  ) and hasattr(self._hf_hook						,					"pre_forward"  ):
												self._hf_hook.pre_forward(self  )
								return method(self						,					*lowerCamelCase_						,					**lowerCamelCase_  )

				return wrapper




"""simple docstring"""






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

import pytest
from parameterized import 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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]   )
class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):
								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=UpperCAmelCase__       ,		)
								assert hasattr(self       ,		"env"     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__=1     ):
								# 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}-single'''       ,		instance_count=UpperCAmelCase__       ,		instance_type=self.instance_type       ,		debugger_hook_config=UpperCAmelCase__       ,		hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,		metric_definitions=self.env.metric_definitions       ,		py_version="py36"       ,		)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								TrainingJobAnalytics(UpperCAmelCase__     ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''     )






				def   lowerCAmelCase__		(						self     ):
								# create estimator
								SCREAMING_SNAKE_CASE__     							=				self.create_estimator()

								# run training
								estimator.fit()

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

								# extract kpis
								SCREAMING_SNAKE_CASE__     							=				list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]     )
								SCREAMING_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
								SCREAMING_SNAKE_CASE__     							=				(
								    Session().describe_training_job(estimator.latest_training_job.name     ).get("TrainingTimeInSeconds"       ,		99_9999     )
								)

								# 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}       ,		UpperCAmelCase__     )
