


'''simple docstring'''




from collections.abc import Iterable
from typing import Any







class 					a			:
 '''simple docstring'''




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




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




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




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    None:
    if new_children is not None:  # reset its kids
       _a					:  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
          _a					:  Optional[Any]						=  new_children
       else:
          _a					:  Union[str, Any]						=  new_children
    else:
       _a					:  Tuple						=  new_children




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




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




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    None:
    _a					:  Optional[int]						=  Node(a_      )  # create a new Node
    if self.empty():  # if Tree is empty
       _a					:  int						=  new_node  # set its root
    else:  # Tree is not empty
       _a					:  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:
                _a					:  List[str]						=  new_node  # We insert the new node in a leaf
                break
             else:
                _a					:  Any						=  parent_node.left
          else:
             if parent_node.right is None:
                _a					:  List[str]						=  new_node
                break
             else:
                _a					:  Any						=  parent_node.right
       _a					:  Optional[int]						=  parent_node




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




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




 def   __UpperCamelCase   (    self		,      lowerCamelCase_ = None      )      ->    Node | None:
    if node is None:
       if self.root is None:
          return None
       _a					:  Tuple						=  self.root

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




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




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    None:
    _a					:  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:
          _a					:  Any						=  self.get_max(
              node.left      )  # Gets the max value of the left branch
          self.remove(tmp_node.value      )  # type: ignore
          _a					:  Dict						=  (
              tmp_node.value  # type: ignore
          )  # Assigns the value to the node to delete and keep tree structure




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




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




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





def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Tuple						=  []
   if curr_node is not None:
      _a					:  str						=  postorder(curr_node.left      ) + postorder(curr_node.right      ) + [curr_node]
   return node_list


def    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  List[str]						=  (8, 3, 6, 1, 1_0, 1_4, 1_3, 4, 7)
   _a					:  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"							)


