


'''simple docstring'''
from collections.abc import Iterable
from typing import Any




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_ = None							)    ->  Optional[Any]:
					lowercase						:		Dict   					=		value
					lowercase						:		Node | None   					=		None  # Added in order to delete a node easier
					lowercase						:		Node | None   					=		None
					lowercase						:		Node | None   					=		None


				def __repr__(     self							)    ->  str:
					from pprint import pformat

					if self.left is None and self.right is None:
						return str(self.value							)
					return pformat({F'''{self.value}''': (self.left, self.right)}							,      indent=1							)




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_ = None							)    ->  List[str]:
					lowercase						:		Any   					=		root

				def __str__(     self							)    ->  str:
					return str(self.root							)

				def 	a__		(     self							,      a_							,      a_							)    ->  None:
					if new_children is not None:  # reset its kids
						lowercase						:		Union[str, Any]   					=		node.parent
					if node.parent is not None:  # reset its parent
						if self.is_right(a_							):  # If it is the right children
							lowercase						:		Optional[Any]   					=		new_children
						else:
							lowercase						:		Union[str, Any]   					=		new_children
					else:
						lowercase						:		Tuple   					=		new_children

				def 	a__		(     self							,      a_							)    ->  bool:
					if node.parent and node.parent.right:
						return node == node.parent.right
					return False

				def 	a__		(     self							)    ->  bool:
					return self.root is None

				def 	a__		(     self							,      a_							)    ->  None:
					lowercase						:		Optional[int]   					=		Node(a_							)  # create a new Node
					if self.empty():  # if Tree is empty
						lowercase						:		int   					=		new_node  # set its root
					else:  # Tree is not empty
						lowercase						:		Optional[int]   					=		self.root  # from root
						if parent_node is None:
							return
						while True:  # While we don't get to a leaf
							if value < parent_node.value:  # We go left
								if parent_node.left is None:
									lowercase						:		List[str]   					=		new_node  # We insert the new node in a leaf
									break
								else:
									lowercase						:		Any   					=		parent_node.left
							else:
								if parent_node.right is None:
									lowercase						:		List[str]   					=		new_node
									break
								else:
									lowercase						:		Any   					=		parent_node.right
						lowercase						:		Optional[int]   					=		parent_node

				def 	a__		(     self							,      *a_							)    ->  None:
					for value in values:
						self.__insert(a_							)

				def 	a__		(     self							,      a_							)    ->  Node | None:
					if self.empty():
						raise IndexError("Warning: Tree is empty! please use another."							)
					else:
						lowercase						:		List[Any]   					=		self.root
						# use lazy evaluation here to avoid NoneType Attribute error
						while node is not None and node.value is not value:
							lowercase						:		List[Any]   					=		node.left if value < node.value else node.right
						return node

				def 	a__		(     self							,      a_ = None							)    ->  Node | None:
					if node is None:
						if self.root is None:
							return None
						lowercase						:		Tuple   					=		self.root

					if not self.empty():
						while node.right is not None:
							lowercase						:		List[Any]   					=		node.right
					return node

				def 	a__		(     self							,      a_ = None							)    ->  Node | None:
					if node is None:
						lowercase						:		Union[str, Any]   					=		self.root
					if self.root is None:
						return None
					if not self.empty():
						lowercase						:		List[str]   					=		self.root
						while node.left is not None:
							lowercase						:		Tuple   					=		node.left
					return node

				def 	a__		(     self							,      a_							)    ->  None:
					lowercase						:		Any   					=		self.search(a_							)  # Look for the node with that label
					if node is not None:
						if node.left is None and node.right is None:  # If it has no children
							self.__reassign_nodes(a_							,      a_							)
						elif node.left is None:  # Has only right children
							self.__reassign_nodes(a_							,      node.right							)
						elif node.right is None:  # Has only left children
							self.__reassign_nodes(a_							,      node.left							)
						else:
							lowercase						:		Any   					=		self.get_max(
							    node.left							)  # Gets the max value of the left branch
							self.remove(tmp_node.value							)  # type: ignore
							lowercase						:		Dict   					=		(
							    tmp_node.value  # type: ignore
							)  # Assigns the value to the node to delete and keep tree structure

				def 	a__		(     self							,      a_							)    ->  Iterable:
					if node is not None:
						yield node  # Preorder Traversal
						yield from self.preorder_traverse(node.left							)
						yield from self.preorder_traverse(node.right							)

				def 	a__		(     self							,      a_=None							)    ->  Any:
					if traversal_function is None:
						return self.preorder_traverse(self.root							)
					else:
						return traversal_function(self.root							)

				def 	a__		(     self							,      a_							,      a_							)    ->  None:
					if node:
						self.inorder(a_							,      node.left							)
						arr.append(node.value							)
						self.inorder(a_							,      node.right							)


				def 	a__		(     self							,      a_							,      a_							)    ->  int:
					lowercase						:		list[int]   					=		[]
					self.inorder(a_							,      a_							)  # append all values to list using inorder traversal
					return arr[k - 1]




def 	_A  (       A							)  ->				list[Node]:
	lowercase						:		Tuple   					=		[]
	if curr_node is not None:
		lowercase						:		str   					=		postorder(curr_node.left							) + postorder(curr_node.right							) + [curr_node]
	return node_list




def 	_A  (       )  ->				None:
	lowercase						:		List[str]   					=		(8, 3, 6, 1, 1_0, 1_4, 1_3, 4, 7)
	lowercase						:		List[Any]   					=		BinarySearchTree()
	for i in testlist:
		t.insert(A							)

	# Prints all the elements of the list in order traversal
	print(A							)

	if t.search(6							) is not None:
		print("The value 6 exists"							)
	else:
		print("The value 6 doesn't exist"							)

	if t.search(-1							) is not None:
		print("The value -1 exists"							)
	else:
		print("The value -1 doesn't exist"							)

	if not t.empty():
		print("Max Value: "							,t.get_max().value							)  # type: ignore
		print("Min Value: "							,t.get_min().value							)  # type: ignore

	for i in testlist:
		t.remove(A							)
		print(A							)


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)






'''simple docstring'''
import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


lowerCAmelCase   : Dict             =				logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      *a_							,      a_=None							,      a_=None							,      a_=None							,      **a_							)    ->  Any:
					super().__init__(*a_							,      **a_							)
					lowercase						:		Optional[Any]   					=		eval_examples
					lowercase						:		Any   					=		post_process_function
					lowercase						:		Dict   					=		quant_trainer_args
					lowercase						:		List[Any]   					=		1_2_8  # default number of calibration samples

				def 	a__		(     self							,      a_=None							)    ->  List[str]:
					if calib_dataset is None and self.calib_dataset is None:
						raise ValueError("Trainer: calibration requires an calib_dataset."							)
					lowercase						:		Dict   					=		calib_dataset if calib_dataset is not None else self.calib_dataset

					lowercase						:		Optional[int]   					=		self._remove_unused_columns(a_							,      description="Calibration"							)

					return DataLoader(
					    a_							,      batch_size=self.args.eval_batch_size							,      collate_fn=self.data_collator							,      drop_last=self.args.dataloader_drop_last							,      num_workers=self.args.dataloader_num_workers							,      pin_memory=self.args.dataloader_pin_memory							,      shuffle=a_							,      )

				def 	a__		(     self							,      a_=None							)    ->  str:
					lowercase						:		Optional[Any]   					=		self.train_dataset if calib_dataset is None else calib_dataset
					lowercase						:		Optional[Any]   					=		self.get_calib_dataloader(a_							)

					lowercase						:		Union[str, Any]   					=		self.model
					quant_trainer.configure_model(a_							,      self.quant_trainer_args							,      calib=a_							)
					model.eval()
					quant_trainer.enable_calibration(a_							)

					logger.info("***** Running calibration *****"							)
					logger.info(F'''  Num examples = {self.calib_num}'''							)
					logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''							)

					for step, inputs in enumerate(a_							):
						# Prediction step
						lowercase					,		lowercase					,		lowercase						:		Union[str, Any]   					=		self.prediction_step(a_							,      a_							,      prediction_loss_only=a_							)
						if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
							break

					quant_trainer.finish_calibration(a_							,      self.quant_trainer_args							)
					lowercase						:		List[Any]   					=		model

				def 	a__		(     self							,      a_=None							,      a_=None							,      a_=None							,      a_ = "eval"							)    ->  int:
					lowercase						:		List[str]   					=		self.eval_dataset if eval_dataset is None else eval_dataset
					lowercase						:		Union[str, Any]   					=		self.get_eval_dataloader(a_							)
					lowercase						:		Tuple   					=		self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					lowercase						:		List[str]   					=		self.compute_metrics
					lowercase						:		Dict   					=		None
					lowercase						:		int   					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
						lowercase						:		Tuple   					=		eval_loop(
						    a_							,      description="Evaluation"							,      prediction_loss_only=True if compute_metrics is None else None							,      ignore_keys=a_							,      )
					finally:
						lowercase						:		List[Any]   					=		compute_metrics

					if self.post_process_function is not None and self.compute_metrics is not None:
						lowercase						:		Union[str, Any]   					=		self.post_process_function(a_							,      a_							,      output.predictions							)
						lowercase						:		Union[str, Any]   					=		self.compute_metrics(a_							)

						# Prefix all keys with metric_key_prefix + '_'
						for key in list(metrics.keys()							):
							if not key.startswith(F'''{metric_key_prefix}_'''							):
								lowercase						:		Dict   					=		metrics.pop(a_							)

						self.log(a_							)
					else:
						lowercase						:		Tuple   					=		{}

					if self.args.tpu_metrics_debug or self.args.debug:
						# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
						xm.master_print(met.metrics_report()							)

					lowercase						:		int   					=		self.callback_handler.on_evaluate(self.args							,      self.state							,      self.control							,      a_							)
					return metrics

				def 	a__		(     self							,      a_							,      a_							,      a_=None							,      a_ = "test"							)    ->  Optional[int]:
					lowercase						:		Dict   					=		self.get_test_dataloader(a_							)

					# Temporarily disable metric computation, we will do it in the loop here.
					lowercase						:		List[str]   					=		self.compute_metrics
					lowercase						:		List[str]   					=		None
					lowercase						:		int   					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
						lowercase						:		Optional[Any]   					=		eval_loop(
						    a_							,      description="Prediction"							,      prediction_loss_only=True if compute_metrics is None else None							,      ignore_keys=a_							,      )
					finally:
						lowercase						:		int   					=		compute_metrics

					if self.post_process_function is None or self.compute_metrics is None:
						return output

					lowercase						:		List[str]   					=		self.post_process_function(a_							,      a_							,      output.predictions							,      "predict"							)
					lowercase						:		Dict   					=		self.compute_metrics(a_							)

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()							):
						if not key.startswith(F'''{metric_key_prefix}_'''							):
							lowercase						:		Union[str, Any]   					=		metrics.pop(a_							)

					return PredictionOutput(predictions=predictions.predictions							,      label_ids=predictions.label_ids							,      metrics=a_							)


				def 	a__		(     self							,      a_="./"							)    ->  Optional[Any]:
					lowercase						:		str   					=		self.eval_dataset
					lowercase						:		Dict   					=		self.get_eval_dataloader(a_							)

					lowercase						:		Any   					=		next(iter(a_							)							)

					# saving device - to make it consistent
					lowercase						:		List[Any]   					=		torch.device("cuda" if torch.cuda.is_available() else "cpu"							)

					# convert to tuple
					lowercase						:		Tuple   					=		tuple(v.to(a_							) for k, v in batch.items()							)

					logger.info("Converting model to be onnx compatible"							)
					from pytorch_quantization.nn import TensorQuantizer

					lowercase						:		Optional[Any]   					=		True

					lowercase						:		int   					=		self.model.to(a_							)

					model.eval()
					model.float()

					lowercase						:		Union[str, Any]   					=		model.module if hasattr(a_							,      "module"							) else model
					quant_trainer.configure_model(a_							,      self.quant_trainer_args							)

					lowercase						:		List[str]   					=		os.path.join(a_							,      "model.onnx"							)
					logger.info(F'''exporting model to {output_model_file}'''							)

					lowercase						:		Any   					=		{0: "batch_size", 1: "seq_len"}

					torch.onnx.export(
					    a_							,      a_							,      a_							,      export_params=a_							,      opset_version=1_3							,      do_constant_folding=a_							,      input_names=["input_ids", "attention_mask", "token_type_ids"]							,      output_names=["output_start_logits", "output_end_logits"]							,      dynamic_axes={
					        "input_ids": axes,
					        "attention_mask": axes,
					        "token_type_ids": axes,
					        "output_start_logits": axes,
					        "output_end_logits": axes,
					    }							,      verbose=a_							,      )
					logger.info("onnx export finished"							)


