


from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any







@dataclass
class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						42
							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						None







def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     Dict					=					Node(1				)
			SCREAMING_SNAKE_CASE_		:     Tuple					=					Node(2				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					Node(3				)
			SCREAMING_SNAKE_CASE_		:     int					=					Node(4				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					Node(5				)
			return tree






def 						a__			(				A__				):
			return [root.data, *preorder(root.left				), *preorder(root.right				)] if root else []






def 						a__			(				A__				):
			return postorder(root.left				) + postorder(root.right				) + [root.data] if root else []






def 						a__			(				A__				):
			return [*inorder(root.left				), root.data, *inorder(root.right				)] if root else []






def 						a__			(				A__				):
			return (max(height(root.left				),      height(root.right				)				) + 1) if root else 0






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     list[Any]					=					[]

			if root is None:
						return output

			SCREAMING_SNAKE_CASE_		:     List[str]					=					deque([root]				)

			while process_queue:
						SCREAMING_SNAKE_CASE_		:     int					=					process_queue.popleft()
						output.append(node.data				)

						if node.left:
									process_queue.append(node.left				)
						if node.right:
									process_queue.append(node.right				)
			return output






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     list[Any]					=					[]

			def populate_output(A__,      A__				) -> None:
						if not root:
									return
						if level == 1:
									output.append(root.data				)
						elif level > 1:
									populate_output(root.left,      level - 1				)
									populate_output(root.right,      level - 1				)

			populate_output(A__,      A__				)
			return output






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     list[Any]					=					[]

			def populate_output(A__,      A__				) -> None:
						if root is None:
									return
						if level == 1:
									output.append(root.data				)
						elif level > 1:
									populate_output(root.right,      level - 1				)
									populate_output(root.left,      level - 1				)

			populate_output(A__,      A__				)
			return output






def 						a__			(				A__				):
			if root is None:
						return []

			SCREAMING_SNAKE_CASE_		:     list[Sequence[Node | None]]					=					[]

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					0
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					height(A__				)

			for h in range(1,      height_tree + 1				):
						if not flag:
									output.append(get_nodes_from_left_to_right(A__,      A__				)				)
									SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					1
						else:
									output.append(get_nodes_from_right_to_left(A__,      A__				)				)
									SCREAMING_SNAKE_CASE_		:     List[Any]					=					0

			return output






def 						a__			(				):  # Main function for testing.
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					make_tree()
			print(F'''In-order Traversal: {inorder(A__				)}'''				)
			print(F'''Pre-order Traversal: {preorder(A__				)}'''				)
			print(F'''Post-order Traversal: {postorder(A__				)}''',      '\n'				)

			print(F'''Height of Tree: {height(A__				)}''',      '\n'				)

			print('Complete Level Order Traversal: '				)
			print(level_order(A__				),      '\n'				)

			print('Level-wise order Traversal: '				)

			for level in range(1,      height(A__				) + 1				):
						print(F'''Level {level}:''',      get_nodes_from_left_to_right(A__,      level=A__				)				)

			print('\nZigZag order Traversal: '				)
			print(zigzag(A__				)				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()







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    a_ (			unittest.TestCase    ):






			def    UpperCamelCase_				(      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    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# configuration for running training on smdistributed Model Parallel
									_lowercase					       =					{
									    """enabled""": True,
									    """processes_per_host""": 8,
									}
									_lowercase					       =					{
									    """enabled""": True,
									    """parameters""": {
									        """microbatches""": 4,
									        """placement_strategy""": """spread""",
									        """pipeline""": """interleaved""",
									        """optimize""": """speed""",
									        """partitions""": 4,
									        """ddp""": True,
									    },
									}

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

									_lowercase					       =					"""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=__UpperCamelCase				,			instance_type=self.instance_type				,			debugger_hook_config=__UpperCamelCase				,			hyperparameters={
									        **self.env.hyperparameters,
									        """model_name_or_path""": self.model_name_or_path,
									        """max_steps""": 500,
									    }				,			metric_definitions=self.env.metric_definitions				,			distribution=__UpperCamelCase				,			py_version="""py36"""				,			)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									TrainingJobAnalytics(__UpperCamelCase  ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""  )






			@parameterized.expand([(1,)]  )
			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# create estimator
									_lowercase					       =					self.create_estimator(__UpperCamelCase  )

									# run training
									estimator.fit()

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

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