




"""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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]    )
class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	List[str]						):
						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       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	Optional[Any]						):
						__lowercase				:		Optional[Any]												=				f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
						# distributed data settings
						__lowercase				:		str												=				{"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

						# 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=lowercase__    ,   instance_count=lowercase__    ,   instance_type=self.instance_type    ,   debugger_hook_config=lowercase__    ,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}    ,   metric_definitions=self.env.metric_definitions    ,   distribution=lowercase__    ,   py_version="py36"    ,   )



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	Optional[Any]						):
						TrainingJobAnalytics(lowercase__						).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'						)



				@parameterized.expand([(2,)]						)
				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	Optional[int]						):
						# create estimator
						__lowercase				:		Any												=				self.create_estimator(lowercase__						)

						# run training
						estimator.fit()

						# result dataframe
						__lowercase				:		Union[str, Any]												=				TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

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






"""simple docstring"""







class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	List[Any]						):
						__lowercase				:		Tuple												=				val
						__lowercase				:		Optional[Any]												=				None
						__lowercase				:		Tuple												=				None



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	List[str]						):
						if self.val:
								if val < self.val:
										if self.left is None:
												__lowercase				:		Tuple												=				Node(lowercase__						)
										else:
												self.left.insert(lowercase__						)
								elif val > self.val:
										if self.right is None:
												__lowercase				:		Any												=				Node(lowercase__						)
										else:
												self.right.insert(lowercase__						)
						else:
								__lowercase				:		Optional[Any]												=				val





def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Any:






		"""simple docstring"""


		if root:
				inorder(root.left,  _lowerCamelCase				)
				res.append(root.val				)
				inorder(root.right,  _lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase				)			->Optional[Any]:






		"""simple docstring"""


		if len(_lowerCamelCase				) == 0:
				return arr
		__lowercase				:		int												=				Node(arr[0]				)
		for i in range(1,  len(_lowerCamelCase				)				):
				root.insert(arr[i]				)
		# Traverse BST in order.
		__lowercase				:		Tuple												=				[]
		inorder(_lowerCamelCase,  _lowerCamelCase				)
		return res


if __name__ == "__main__":
						print(tree_sort([1_0, 1, 3, 2, 9, 1_4, 1_3]))
