from __future__ import annotations

import math
import random
from typing import Any


class 						a__							:



			def __init__(						self   )			->   None:
								__a		 =       []
								__a		 =       0
								__a		 =       0



			def __SCREAMING_SNAKE_CASE							(						self   )			->   bool:
								return self.head == self.tail



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								self.data.append(UpperCAmelCase   )
								__a		 =       self.tail + 1



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       self.data[self.head]
								__a		 =       self.head + 1
								return ret



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								return self.tail - self.head







			def __SCREAMING_SNAKE_CASE							(						self   )			->   None:
								print(self.data   )
								print('**************'   )
								print(self.data[self.head : self.tail]   )






class 						a__							:



			def __init__(						self				,						UpperCAmelCase   )			->   None:
								__a		 =       data
								__a		 =       None
								__a		 =       None
								__a		 =       1



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								return self.data



			def __SCREAMING_SNAKE_CASE							(						self   )			->   MyNode | None:
								return self.left



			def __SCREAMING_SNAKE_CASE							(						self   )			->   MyNode | None:
								return self.right



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								return self.height



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								__a		 =       data



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								__a		 =       node



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								__a		 =       node







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								__a		 =       height






def 							lowerCAmelCase(     __lowerCamelCase						):
					if node is None:
										return 0
					return node.get_height()
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					if a > b:
										return a
					return b
def 							lowerCAmelCase(     __lowerCamelCase						):
					print('left rotation node:'				,    node.get_data()						)
					__a		 =       node.get_left()
					assert ret is not None
					node.set_left(ret.get_right()						)
					ret.set_right(__lowerCamelCase						)
					__a		 =       my_max(get_height(node.get_right()						)				,    get_height(node.get_left()						)						) + 1
					node.set_height(__lowerCamelCase						)
					__a		 =       my_max(get_height(ret.get_right()						)				,    get_height(ret.get_left()						)						) + 1
					ret.set_height(__lowerCamelCase						)
					return ret
def 							lowerCAmelCase(     __lowerCamelCase						):
					print('right rotation node:'				,    node.get_data()						)
					__a		 =       node.get_right()
					assert ret is not None
					node.set_right(ret.get_left()						)
					ret.set_left(__lowerCamelCase						)
					__a		 =       my_max(get_height(node.get_right()						)				,    get_height(node.get_left()						)						) + 1
					node.set_height(__lowerCamelCase						)
					__a		 =       my_max(get_height(ret.get_right()						)				,    get_height(ret.get_left()						)						) + 1
					ret.set_height(__lowerCamelCase						)
					return ret
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       node.get_left()
					assert left_child is not None
					node.set_left(left_rotation(__lowerCamelCase						)						)
					return right_rotation(__lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       node.get_right()
					assert right_child is not None
					node.set_right(right_rotation(__lowerCamelCase						)						)
					return left_rotation(__lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					if node is None:
										return MyNode(__lowerCamelCase						)
					if data < node.get_data():
										node.set_left(insert_node(node.get_left()				,    __lowerCamelCase						)						)
										if (
										    get_height(node.get_left()						) - get_height(node.get_right()						) == 2
										):  # an unbalance detected
															__a		 =       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
																				__a		 =       right_rotation(__lowerCamelCase						)
															else:
																				__a		 =       lr_rotation(__lowerCamelCase						)
					else:
										node.set_right(insert_node(node.get_right()				,    __lowerCamelCase						)						)
										if get_height(node.get_right()						) - get_height(node.get_left()						) == 2:
															__a		 =       node.get_right()
															assert right_child is not None
															if data < right_child.get_data():
																				__a		 =       rl_rotation(__lowerCamelCase						)
															else:
																				__a		 =       left_rotation(__lowerCamelCase						)
					__a		 =       my_max(get_height(node.get_right()						)				,    get_height(node.get_left()						)						) + 1
					node.set_height(__lowerCamelCase						)
					return node
def 							lowerCAmelCase(     __lowerCamelCase						):
					while True:
										__a		 =       root.get_right()
										if right_child is None:
															break
										__a		 =       right_child
					return root.get_data()
def 							lowerCAmelCase(     __lowerCamelCase						):
					while True:
										__a		 =       root.get_left()
										if left_child is None:
															break
										__a		 =       left_child
					return root.get_data()
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       root.get_left()
					__a		 =       root.get_right()
					if root.get_data() == data:
										if left_child is not None and right_child is not None:
															__a		 =       get_left_most(__lowerCamelCase						)
															root.set_data(__lowerCamelCase						)
															root.set_right(del_node(__lowerCamelCase				,    __lowerCamelCase						)						)
										elif left_child is not None:
															__a		 =       left_child
										elif right_child is not None:
															__a		 =       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(__lowerCamelCase				,    __lowerCamelCase						)						)
					else:  # root.get_data() < data
										if right_child is None:
															return root
										else:
															root.set_right(del_node(__lowerCamelCase				,    __lowerCamelCase						)						)

					if get_height(__lowerCamelCase						) - get_height(__lowerCamelCase						) == 2:
										assert right_child is not None
										if get_height(right_child.get_right()						) > get_height(right_child.get_left()						):
															__a		 =       left_rotation(__lowerCamelCase						)
										else:
															__a		 =       rl_rotation(__lowerCamelCase						)
					elif get_height(__lowerCamelCase						) - get_height(__lowerCamelCase						) == -2:
										assert left_child is not None
										if get_height(left_child.get_left()						) > get_height(left_child.get_right()						):
															__a		 =       right_rotation(__lowerCamelCase						)
										else:
															__a		 =       lr_rotation(__lowerCamelCase						)
					__a		 =       my_max(get_height(root.get_right()						)				,    get_height(root.get_left()						)						) + 1
					root.set_height(__lowerCamelCase						)
					return root


class 						a__							:



			def __init__(						self   )			->   None:
								__a		 =       None



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								return get_height(self.root   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								print('insert:' + str(UpperCAmelCase   )   )
								__a		 =       insert_node(self.root				,						UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								print('delete:' + str(UpperCAmelCase   )   )
								if self.root is None:
													print('Tree is empty!'   )
													return
								__a		 =       del_node(self.root				,						UpperCAmelCase   )







			def __str__(						self				,						)			->   str:  # a level traversale, gives a more intuitive look on the tree
								__a		 =       ''
								__a		 =       MyQueue()
								q.push(self.root   )
								__a		 =       self.get_height()
								if layer == 0:
													return output
								__a		 =       0
								while not q.is_empty():
													__a		 =       q.pop()
													__a		 =       ' ' * int(math.pow(2				,						layer - 1   )   )
													output += space
													if node is None:
																		output += "*"
																		q.push(UpperCAmelCase   )
																		q.push(UpperCAmelCase   )
													else:
																		output += str(node.get_data()   )
																		q.push(node.get_left()   )
																		q.push(node.get_right()   )
													output += space
													__a		 =       cnt + 1
													for i in range(1_0_0   ):
																		if cnt == math.pow(2				,						UpperCAmelCase   ) - 1:
																							__a		 =       layer - 1
																							if layer == 0:
																												output += "\n*************************************"
																												return output
																							output += "\n"
																							break
								output += "\n*************************************"
								return output






def 							lowerCAmelCase(     ):
					import doctest

					doctest.testmod()


if __name__ == "__main__":
	_test()
	lowerCamelCase_    :		Optional[Any]							=	AVLtree()
	lowerCamelCase_    :		Tuple							=	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 os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[Any]:
								for model_result in results.values():
													for batch_size, sequence_length in zip(model_result['bs']				,						model_result['ss']   ):
																		__a		 =       model_result['result'][batch_size][sequence_length]
																		self.assertIsNotNone(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sgugger/tiny-distilbert-classification'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						only_pretrain_model=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       'patrickvonplaten/t5-tiny-random'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						configs=[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'   )   ) == 0				,						'Cannot do xla on CPU.'   )
			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						use_xla=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       'sshleifer/tiny-gpt2'
								with tempfile.TemporaryDirectory() as tmp_dir:
													__a		 =       TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,						inference=UpperCAmelCase				,						save_to_csv=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						inference_time_csv_file=os.path.join(UpperCAmelCase				,						'inf_time.csv'   )				,						inference_memory_csv_file=os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )				,						env_info_csv_file=os.path.join(UpperCAmelCase				,						'env.csv'   )				,						multi_process=UpperCAmelCase				,						)
													__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
													benchmark.run()
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_time.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'env.csv'   )   ).exists()   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       'sshleifer/tiny-gpt2'

								def _check_summary_is_not_empty(UpperCAmelCase   ):
													self.assertTrue(hasattr(UpperCAmelCase				,						'sequential'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'cumulative'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'current'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'total'   )   )

								with tempfile.TemporaryDirectory() as tmp_dir:
													__a		 =       TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						log_filename=os.path.join(UpperCAmelCase				,						'log.txt'   )				,						log_print=UpperCAmelCase				,						trace_memory_line_by_line=UpperCAmelCase				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
													__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
													__a		 =       benchmark.run()
													_check_summary_is_not_empty(result.inference_summary   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'log.txt'   )   ).exists()   )




