

from __future__ import annotations

import math
import random
from typing import Any



class 						_A     :





  '''simple docstring'''



  def __init__(  self    ):

      '''simple docstring'''
      snake_case							:		list[Any]        =		[]
      snake_case							:		int        =		0
      snake_case							:		int        =		0





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return self.head == self.tail





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      self.data.append(SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		self.tail + 1





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		self.data[self.head]
      snake_case							:		int        =		self.head + 1
      return ret





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return self.tail - self.head





  def    snake_case_       (  self    ):

      '''simple docstring'''
      print(self.data    )
      print("""**************"""    )
      print(self.data[self.head : self.tail]    )







class 						_A     :





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		data
      snake_case							:		MyNode | None        =		None
      snake_case							:		MyNode | None        =		None
      snake_case							:		int        =		1





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return self.data





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return self.left





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return self.right





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return self.height





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		data





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		node





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		node





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Dict        =		height







def       lowercase					(	__A :					MyNode | None		)				->			int:





    '''simple docstring'''




    if node is None:
        return 0
    return node.get_height()







def       lowercase					(	__A :					int ,    __A :					int		)				->			int:





    '''simple docstring'''




    if a > b:
        return a
    return b







def       lowercase					(	__A :					MyNode		)				->			MyNode:





    '''simple docstring'''




    print("""left rotation node:""" ,    node.get_data()		)
    snake_case							:		Optional[int]        =		node.get_left()
    assert ret is not None
    node.set_left(ret.get_right()		)
    ret.set_right(__A		)
    snake_case							:		List[str]        =		my_max(get_height(node.get_right()		) ,    get_height(node.get_left()		)		) + 1
    node.set_height(__A		)
    snake_case							:		Dict        =		my_max(get_height(ret.get_right()		) ,    get_height(ret.get_left()		)		) + 1
    ret.set_height(__A		)
    return ret







def       lowercase					(	__A :					MyNode		)				->			MyNode:





    '''simple docstring'''




    print("""right rotation node:""" ,    node.get_data()		)
    snake_case							:		Tuple        =		node.get_right()
    assert ret is not None
    node.set_right(ret.get_left()		)
    ret.set_left(__A		)
    snake_case							:		str        =		my_max(get_height(node.get_right()		) ,    get_height(node.get_left()		)		) + 1
    node.set_height(__A		)
    snake_case							:		str        =		my_max(get_height(ret.get_right()		) ,    get_height(ret.get_left()		)		) + 1
    ret.set_height(__A		)
    return ret







def       lowercase					(	__A :					MyNode		)				->			MyNode:





    '''simple docstring'''




    snake_case							:		Optional[Any]        =		node.get_left()
    assert left_child is not None
    node.set_left(left_rotation(__A		)		)
    return right_rotation(__A		)







def       lowercase					(	__A :					MyNode		)				->			MyNode:





    '''simple docstring'''




    snake_case							:		Dict        =		node.get_right()
    assert right_child is not None
    node.set_right(right_rotation(__A		)		)
    return left_rotation(__A		)







def       lowercase					(	__A :					MyNode | None ,    __A :					Any		)				->			MyNode | None:





    '''simple docstring'''




    if node is None:
        return MyNode(__A		)
    if data < node.get_data():
        node.set_left(insert_node(node.get_left() ,    __A		)		)
        if (
            get_height(node.get_left()		) - get_height(node.get_right()		) == 2
        ):  # an unbalance detected
            snake_case							:		Any        =		node.get_left()
            assert left_child is not None
            if (
                data < left_child.get_data()
            ):  # new node is the left child of the left child
                snake_case							:		Optional[Any]        =		right_rotation(__A		)
            else:
                snake_case							:		List[str]        =		lr_rotation(__A		)
    else:
        node.set_right(insert_node(node.get_right() ,    __A		)		)
        if get_height(node.get_right()		) - get_height(node.get_left()		) == 2:
            snake_case							:		Tuple        =		node.get_right()
            assert right_child is not None
            if data < right_child.get_data():
                snake_case							:		int        =		rl_rotation(__A		)
            else:
                snake_case							:		Tuple        =		left_rotation(__A		)
    snake_case							:		str        =		my_max(get_height(node.get_right()		) ,    get_height(node.get_left()		)		) + 1
    node.set_height(__A		)
    return node







def       lowercase					(	__A :					MyNode		)				->			Any:





    '''simple docstring'''




    while True:
        snake_case							:		Dict        =		root.get_right()
        if right_child is None:
            break
        snake_case							:		int        =		right_child
    return root.get_data()







def       lowercase					(	__A :					MyNode		)				->			Any:





    '''simple docstring'''




    while True:
        snake_case							:		Dict        =		root.get_left()
        if left_child is None:
            break
        snake_case							:		Optional[Any]        =		left_child
    return root.get_data()







def       lowercase					(	__A :					MyNode ,    __A :					Any		)				->			MyNode | None:





    '''simple docstring'''




    snake_case							:		Union[str, Any]        =		root.get_left()
    snake_case							:		Optional[Any]        =		root.get_right()
    if root.get_data() == data:
        if left_child is not None and right_child is not None:
            snake_case							:		List[Any]        =		get_left_most(__A		)
            root.set_data(__A		)
            root.set_right(del_node(__A ,    __A		)		)
        elif left_child is not None:
            snake_case							:		int        =		left_child
        elif right_child is not None:
            snake_case							:		Union[str, Any]        =		right_child
        else:
            return None
    elif root.get_data() > data:
        if left_child is None:
            print("""No such data"""		)
            return root
        else:
            root.set_left(del_node(__A ,    __A		)		)
    else:  # root.get_data() < data
        if right_child is None:
            return root
        else:
            root.set_right(del_node(__A ,    __A		)		)

    if get_height(__A		) - get_height(__A		) == 2:
        assert right_child is not None
        if get_height(right_child.get_right()		) > get_height(right_child.get_left()		):
            snake_case							:		List[str]        =		left_rotation(__A		)
        else:
            snake_case							:		Optional[int]        =		rl_rotation(__A		)
    elif get_height(__A		) - get_height(__A		) == -2:
        assert left_child is not None
        if get_height(left_child.get_left()		) > get_height(left_child.get_right()		):
            snake_case							:		Any        =		right_rotation(__A		)
        else:
            snake_case							:		str        =		lr_rotation(__A		)
    snake_case							:		Dict        =		my_max(get_height(root.get_right()		) ,    get_height(root.get_left()		)		) + 1
    root.set_height(__A		)
    return root



class 						_A     :





  '''simple docstring'''



  def __init__(  self    ):

      '''simple docstring'''
      snake_case							:		MyNode | None        =		None





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return get_height(self.root    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      print("""insert:""" + str(SCREAMING_SNAKE_CASE_    )    )
      snake_case							:		int        =		insert_node(self.root   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      print("""delete:""" + str(SCREAMING_SNAKE_CASE_    )    )
      if self.root is None:
          print("""Tree is empty!"""    )
          return
      snake_case							:		List[Any]        =		del_node(self.root   ,SCREAMING_SNAKE_CASE_    )





  def __str__(  self   ,):  # a level traversale, gives a more intuitive look on the tree

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		""""""
      snake_case							:		Union[str, Any]        =		MyQueue()
      q.push(self.root    )
      snake_case							:		Optional[int]        =		self.get_height()
      if layer == 0:
          return output
      snake_case							:		List[str]        =		0
      while not q.is_empty():
          snake_case							:		Dict        =		q.pop()
          snake_case							:		Dict        =		""" """ * int(math.pow(2   ,layer - 1    )    )
          output += space
          if node is None:
              output += "*"
              q.push(SCREAMING_SNAKE_CASE_    )
              q.push(SCREAMING_SNAKE_CASE_    )
          else:
              output += str(node.get_data()    )
              q.push(node.get_left()    )
              q.push(node.get_right()    )
          output += space
          snake_case							:		Tuple        =		cnt + 1
          for i in range(100    ):
              if cnt == math.pow(2   ,SCREAMING_SNAKE_CASE_    ) - 1:
                  snake_case							:		int        =		layer - 1
                  if layer == 0:
                      output += "\n*************************************"
                      return output
                  output += "\n"
                  break
      output += "\n*************************************"
      return output







def       lowercase					(	)				->			None:





    '''simple docstring'''




    import doctest

    doctest.testmod()


if __name__ == "__main__":
     _test()
     __lowercase     : str       =						AVLtree()
     __lowercase     : Union[str, Any]       =						list(range(10))
     random.shuffle(lst)
     for i in lst:
          t.insert(i)
          print(str(t))
     random.shuffle(lst)
     for i in lst:
          t.del_node(i)
          print(str(t))






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''': 6_50, '''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''': 6_00, '''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''': 6_00, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]							)
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__     ):
						A__				:       int	  =						F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
						# distributed data settings
						A__				:       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=A__       ,			instance_count=A__       ,			instance_type=self.instance_type       ,			debugger_hook_config=A__       ,			hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}       ,			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([(2,)]     )
					def     __A     (					self       ,			A__     ):
						# create estimator
						A__				:       str	  =						self.create_estimator(A__     )

						# run training
						estimator.fit()

						# result dataframe
						A__				:       str	  =						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__				:       Optional[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__				:       str	  =						(
						    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__     )



