






from __future__ import annotations

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




@dataclass
class 							_lowerCAmelCase:





       """simple docstring"""







       a							:						int
       a							:						Node | None										=None
       a							:						Node | None										=None







def     snake_case   ()			->							Node | None:
  UpperCamelCase_:   int        =							Node(1    )
  UpperCamelCase_:   Any        =							Node(2    )
  UpperCamelCase_:   Any        =							Node(3    )
  UpperCamelCase_:   List[str]        =							Node(4    )
  UpperCamelCase_:   List[Any]        =							Node(5    )
  return tree

def     snake_case   (UpperCAmelCase__    )			->							list[int]:
  return [root.data, *preorder(root.left    ), *preorder(root.right    )] if root else []

def     snake_case   (UpperCAmelCase__    )			->							list[int]:
  return postorder(root.left    ) + postorder(root.right    ) + [root.data] if root else []

def     snake_case   (UpperCAmelCase__    )			->							list[int]:
  return [*inorder(root.left    ), root.data, *inorder(root.right    )] if root else []

def     snake_case   (UpperCAmelCase__    )			->							int:
  return (max(height(root.left    )      ,       height(root.right    )    ) + 1) if root else 0

def     snake_case   (UpperCAmelCase__    )			->							Sequence[Node | None]:
  UpperCamelCase_:   list[Any]        =							[]

  if root is None:
    return output

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

  while process_queue:
    UpperCamelCase_:   Dict        =							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     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Sequence[Node | None]:
  UpperCamelCase_:   list[Any]        =							[]

  def populate_output(UpperCAmelCase__      ,       UpperCAmelCase__    ) -> 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(UpperCAmelCase__      ,       UpperCAmelCase__    )
  return output

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Sequence[Node | None]:
  UpperCamelCase_:   list[Any]        =							[]

  def populate_output(UpperCAmelCase__      ,       UpperCAmelCase__    ) -> 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(UpperCAmelCase__      ,       UpperCAmelCase__    )
  return output

def     snake_case   (UpperCAmelCase__    )			->							Sequence[Node | None] | list[Any]:
  if root is None:
    return []

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

  UpperCamelCase_:   Optional[int]        =							0
  UpperCamelCase_:   Optional[Any]        =							height(UpperCAmelCase__    )

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

  return output

def     snake_case   ()			->							None:  # Main function for testing.
  UpperCamelCase_:   Union[str, Any]        =							make_tree()
  print(F'''In-order Traversal: {inorder(UpperCAmelCase__    )}'''    )
  print(F'''Pre-order Traversal: {preorder(UpperCAmelCase__    )}'''    )
  print(F'''Post-order Traversal: {postorder(UpperCAmelCase__    )}'''      ,       '\n'    )

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

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

  print('Level-wise order Traversal: '    )

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

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()



'''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"							)


