

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 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def 						__A    (				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=A__					, )
						assert hasattr(self					, """env"""    )






					def 						__A    (				self					, A__    ):
						# configuration for running training on smdistributed Model Parallel
						A__							:					List[str]       =	{
						    """enabled""": True,
						    """processes_per_host""": 8,
						}
						A__							:					Union[str, Any]       =	{
						    """enabled""": True,
						    """parameters""": {
						        """microbatches""": 4,
						        """placement_strategy""": """spread""",
						        """pipeline""": """interleaved""",
						        """optimize""": """speed""",
						        """partitions""": 4,
						        """ddp""": True,
						    },
						}

						A__							:					Dict       =	{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

						A__							:					Tuple       =	"""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""": 500,
						    }					, metric_definitions=self.env.metric_definitions					, distribution=A__					, py_version="""py36"""					, )






					def 						__A    (				self					, A__    ):
						TrainingJobAnalytics(A__    ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""    )






					@parameterized.expand([(1,)]    )
					def 						__A    (				self					, A__    ):
						# create estimator
						A__							:					int       =	self.create_estimator(A__    )

						# run training
						estimator.fit()

						# result dataframe
						A__							:					str       =	TrainingJobAnalytics(estimator.latest_training_job.name    ).dataframe()

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






from functools import lru_cache

@lru_cache
def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	if num < 0:
		raise ValueError("""Number should not be negative."""      )

	return 1 if num in (0, 1) else num * factorial(num - 1      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



