
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       ):




				def      __a						(					self     )				->      Optional[int]:

							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=_SCREAMING_SNAKE_CASE				,				)
							assert hasattr(self				,				"env"     )




				def      __a						(					self				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	{
							    "enabled": True,
							    "processes_per_host": 8,
							}
							lowerCAmelCase_						=	{
							    "enabled": True,
							    "parameters": {
							        "microbatches": 4,
							        "placement_strategy": "spread",
							        "pipeline": "interleaved",
							        "optimize": "speed",
							        "partitions": 4,
							        "ddp": True,
							    },
							}

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

							lowerCAmelCase_						=	"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=_SCREAMING_SNAKE_CASE				,				instance_type=self.instance_type				,				debugger_hook_config=_SCREAMING_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=_SCREAMING_SNAKE_CASE				,				py_version="py36"				,				)




				def      __a						(					self				,				_UpperCamelCase     )				->      List[Any]:
							TrainingJobAnalytics(_SCREAMING_SNAKE_CASE     ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""     )




				@parameterized.expand([(1,)]     )
				def      __a						(					self				,				_UpperCamelCase     )				->      Optional[Any]:
							lowerCAmelCase_						=	self.create_estimator(_SCREAMING_SNAKE_CASE     )

							# run training
							estimator.fit()

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

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








"""simple docstring"""






from ..utils import DummyObject, requires_backends



class 			A__   (  metaclass=__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	['torch', 'torchsde']

	def __init__(     self:		int       ,			*_SCREAMING_SNAKE_CASE:		Optional[Any]       ,			**_SCREAMING_SNAKE_CASE:		str)				->       Dict:

				"""simple docstring"""

				requires_backends(self       ,			["torch", "torchsde"])

	@classmethod
	def 							_SCREAMING_SNAKE_CASE   (     cls:		Optional[Any]       ,			*_SCREAMING_SNAKE_CASE:		Optional[int]       ,			**_SCREAMING_SNAKE_CASE:		Optional[int])				->       Optional[Any]:

				"""simple docstring"""

				requires_backends(cls       ,			["torch", "torchsde"])







	@classmethod
	def 							_SCREAMING_SNAKE_CASE   (     cls:		Dict       ,			*_SCREAMING_SNAKE_CASE:		List[Any]       ,			**_SCREAMING_SNAKE_CASE:		Any)				->       Optional[int]:

				"""simple docstring"""

				requires_backends(cls       ,			["torch", "torchsde"])