



"""simple docstring"""

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_6_0_0, "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_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]      )
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



						def     lowerCAmelCase__(self							):





													'''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=_lowercase						,     )
													assert hasattr(self						,     """env"""							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       {
													    """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		:		Optional[Any]									=       {"""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=_lowercase						,     instance_type=self.instance_type						,     debugger_hook_config=_lowercase						,     hyperparameters={
													        **self.env.hyperparameters,
													        """model_name_or_path""": self.model_name_or_path,
													        """max_steps""": 500,
													    }						,     metric_definitions=self.env.metric_definitions						,     distribution=_lowercase						,     py_version="""py36"""						,     )



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

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





						@parameterized.expand([(1,)]							)
						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		Any									=       self.create_estimator(_lowercase							)

													# run training
													estimator.fit()

													# result dataframe
													__a		:		Tuple									=       TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

													# extract kpis
													__a		:		List[Any]									=       list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]							)
													__a		:		List[str]									=       list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]							)
													# get train time from SageMaker job, this includes starting, preprocessing, stopping
													__a		:		Dict									=       (
													    Session().describe_training_job(estimator.latest_training_job.name							).get("""TrainingTimeInSeconds"""						,     999999							)
													)

													# 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}						,     _lowercase							)






"""simple docstring"""

import unittest

from knapsack import knapsack as k




class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       0
													__a		:		Optional[Any]									=       [0]
													__a		:		int									=       [0]
													__a		:		str									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     0							)

													__a		:		int									=       [60]
													__a		:		Union[str, Any]									=       [10]
													__a		:		Tuple									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     0							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       3
													__a		:		str									=       [1, 2, 3]
													__a		:		Optional[Any]									=       [3, 2, 1]
													__a		:		int									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     5							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       50
													__a		:		Tuple									=       [60, 100, 120]
													__a		:		List[str]									=       [10, 20, 30]
													__a		:		Union[str, Any]									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     220							)


if __name__ == "__main__":
	unittest.main()

