'''simple docstring'''

import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_lowerCamelCase :				str														= logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_lowerCamelCase :				Tuple														= {
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_lowerCamelCase :				Dict														= {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_lowerCamelCase :				Optional[Any]														= sorted(arg_to_scheduler.keys())
_lowerCamelCase :				str														= """{""" + """, """.join(arg_to_scheduler_choices) + """}"""




class    lowerCamelCase__  (   pl.LightningModule     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__="base"					,      lowerCAmelCase__=None					,      lowerCAmelCase__=None					,      lowerCAmelCase__=None					,      **lowerCAmelCase__					,      )				->     str:
        """simple docstring"""





        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(lowerCAmelCase__     )
        _UpperCamelCase  :Union[str, Any]       =0
        _UpperCamelCase  :Optional[int]       =Path(self.hparams.output_dir     )
        _UpperCamelCase  :Any       =self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
             _UpperCamelCase  :List[Any]       =AutoConfig.from_pretrained(
                 self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path					,      **({"""num_labels""": num_labels} if num_labels is not None else {})					,      cache_dir=lowerCAmelCase__					,      **lowerCAmelCase__					,      )
        else:
             _UpperCamelCase  :PretrainedConfig       =config

        _UpperCamelCase  :int       =("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
        for p in extra_model_params:
             if getattr(self.hparams					,      lowerCAmelCase__					,      lowerCAmelCase__     ):
                  assert hasattr(self.config					,      lowerCAmelCase__     ), f'''model config doesn\'t have a `{p}` attribute'''
                  setattr(self.config					,      lowerCAmelCase__					,      getattr(self.hparams					,      lowerCAmelCase__     )     )

        if tokenizer is None:
             _UpperCamelCase  :Optional[Any]       =AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,      cache_dir=lowerCAmelCase__					,      )
        else:
             _UpperCamelCase  :PreTrainedTokenizer       =tokenizer
        _UpperCamelCase  :Union[str, Any]       =MODEL_MODES[mode]
        if model is None:
             _UpperCamelCase  :Optional[int]       =self.model_type.from_pretrained(
                 self.hparams.model_name_or_path					,      from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path     )					,      config=self.config					,      cache_dir=lowerCAmelCase__					,      )
        else:
             _UpperCamelCase  :Optional[Any]       =model



   def  _UpperCamelCase		(    self					,      *lowerCAmelCase__					,      **lowerCAmelCase__     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =self.model_type.from_pretrained(*lowerCAmelCase__					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =arg_to_scheduler[self.hparams.lr_scheduler]
        _UpperCamelCase  :int       =get_schedule_func(
            self.opt					,      num_warmup_steps=self.hparams.warmup_steps					,      num_training_steps=self.total_steps()     )
        _UpperCamelCase  :Union[str, Any]       ={"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
        return scheduler



   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.model
        _UpperCamelCase  :List[str]       =["""bias""", """LayerNorm.weight"""]
        _UpperCamelCase  :List[Any]       =[
            {
                """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay     )],
                """weight_decay""": self.hparams.weight_decay,
            },
            {
                """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay     )],
                """weight_decay""": 0.0,
            },
        ]
        if self.hparams.adafactor:
             _UpperCamelCase  :Dict       =Adafactor(
                 lowerCAmelCase__					,      lr=self.hparams.learning_rate					,      scale_parameter=lowerCAmelCase__					,      relative_step=lowerCAmelCase__     )

        else:
             _UpperCamelCase  :List[str]       =AdamW(
                 lowerCAmelCase__					,      lr=self.hparams.learning_rate					,      eps=self.hparams.adam_epsilon     )
        _UpperCamelCase  :str       =optimizer

        _UpperCamelCase  :Tuple       =self.get_lr_scheduler()

        return [optimizer], [scheduler]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        return self.validation_step(lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        return self.validation_end(lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =max(1					,      self.hparams.gpus     )  # TODO: consider num_tpu_cores
        _UpperCamelCase  :Union[str, Any]       =self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        if mode == "test":
             _UpperCamelCase  :Union[str, Any]       =len(self.test_dataloader().dataset     )
        else:
             _UpperCamelCase  :Optional[int]       =self.get_dataloader("""train"""					,      self.hparams.train_batch_size					,      shuffle=lowerCAmelCase__     )
             _UpperCamelCase  :List[str]       =len(self.train_dataloader().dataset     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = False     )				->     List[str]:
        """simple docstring"""





        raise NotImplementedError("""You must implement this for your task"""     )



   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        return self.train_loader



   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        return self.get_dataloader("""dev"""					,      self.hparams.eval_batch_size					,      shuffle=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        return self.get_dataloader("""test"""					,      self.hparams.eval_batch_size					,      shuffle=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     int:
        """simple docstring"""





        return os.path.join(
            self.hparams.data_dir					,      """cached_{}_{}_{}""".format(
                lowerCAmelCase__					,      list(filter(lowerCAmelCase__					,      self.hparams.model_name_or_path.split("""/"""     )     )     ).pop()					,      str(self.hparams.max_seq_length     )					,      )					,      )



   @pl.utilities.rank_zero_only
   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     None:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.output_dir.joinpath("""best_tfmr"""     )
        _UpperCamelCase  :Optional[Any]       =self.step_count
        self.model.save_pretrained(lowerCAmelCase__     )
        self.tokenizer.save_pretrained(lowerCAmelCase__     )




   @staticmethod
   def  _UpperCamelCase		(    lowerCAmelCase__					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        parser.add_argument(
            """--model_name_or_path"""					,      default=lowerCAmelCase__					,      type=lowerCAmelCase__					,      required=lowerCAmelCase__					,      help="""Path to pretrained model or model identifier from huggingface.co/models"""					,      )
        parser.add_argument(
            """--config_name"""					,      default=""""""					,      type=lowerCAmelCase__					,      help="""Pretrained config name or path if not the same as model_name"""     )
        parser.add_argument(
            """--tokenizer_name"""					,      default=lowerCAmelCase__					,      type=lowerCAmelCase__					,      help="""Pretrained tokenizer name or path if not the same as model_name"""					,      )
        parser.add_argument(
            """--cache_dir"""					,      default=""""""					,      type=lowerCAmelCase__					,      help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""					,      )
        parser.add_argument(
            """--encoder_layerdrop"""					,      type=lowerCAmelCase__					,      help="""Encoder layer dropout probability (Optional). Goes into model.config"""					,      )
        parser.add_argument(
            """--decoder_layerdrop"""					,      type=lowerCAmelCase__					,      help="""Decoder layer dropout probability (Optional). Goes into model.config"""					,      )
        parser.add_argument(
            """--dropout"""					,      type=lowerCAmelCase__					,      help="""Dropout probability (Optional). Goes into model.config"""					,      )
        parser.add_argument(
            """--attention_dropout"""					,      type=lowerCAmelCase__					,      help="""Attention dropout probability (Optional). Goes into model.config"""					,      )
        parser.add_argument("""--learning_rate"""					,      default=5e-5					,      type=lowerCAmelCase__					,      help="""The initial learning rate for Adam."""     )
        parser.add_argument(
            """--lr_scheduler"""					,      default="""linear"""					,      choices=lowerCAmelCase__					,      metavar=lowerCAmelCase__					,      type=lowerCAmelCase__					,      help="""Learning rate scheduler"""					,      )
        parser.add_argument("""--weight_decay"""					,      default=0.0					,      type=lowerCAmelCase__					,      help="""Weight decay if we apply some."""     )
        parser.add_argument("""--adam_epsilon"""					,      default=1e-8					,      type=lowerCAmelCase__					,      help="""Epsilon for Adam optimizer."""     )
        parser.add_argument("""--warmup_steps"""					,      default=0					,      type=lowerCAmelCase__					,      help="""Linear warmup over warmup_steps."""     )
        parser.add_argument("""--num_workers"""					,      default=4					,      type=lowerCAmelCase__					,      help="""kwarg passed to DataLoader"""     )
        parser.add_argument("""--num_train_epochs"""					,      dest="""max_epochs"""					,      default=3					,      type=lowerCAmelCase__     )
        parser.add_argument("""--train_batch_size"""					,      default=32					,      type=lowerCAmelCase__     )
        parser.add_argument("""--eval_batch_size"""					,      default=32					,      type=lowerCAmelCase__     )
        parser.add_argument("""--adafactor"""					,      action="""store_true"""     )




class    lowerCamelCase__  (   pl.Callback     ):



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =trainer.lr_schedulers[0]["""scheduler"""]
        _UpperCamelCase  :Union[str, Any]       ={f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
        pl_module.logger.log_metrics(lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        rank_zero_info("""***** Validation results *****"""     )
        _UpperCamelCase  :Optional[Any]       =trainer.callback_metrics
        # Log results
        for key in sorted(lowerCAmelCase__     ):
             if key not in ["log", "progress_bar"]:
                  rank_zero_info("""{} = {}\n""".format(lowerCAmelCase__					,      str(metrics[key]     )     )     )




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        rank_zero_info("""***** Test results *****"""     )
        _UpperCamelCase  :List[str]       =trainer.callback_metrics
        # Log and save results to file
        _UpperCamelCase  :Dict       =os.path.join(pl_module.hparams.output_dir					,      """test_results.txt"""     )
        with open(lowerCAmelCase__					,      """w"""     ) as writer:
             for key in sorted(lowerCAmelCase__     ):
                  if key not in ["log", "progress_bar"]:
                       rank_zero_info("""{} = {}\n""".format(lowerCAmelCase__					,      str(metrics[key]     )     )     )
                       writer.write("""{} = {}\n""".format(lowerCAmelCase__					,      str(metrics[key]     )     )     )




def  _lowerCAmelCase				(				__a			,      __a      )  ->      None:
     '''simple docstring'''







     parser.add_argument(
         """--output_dir"""			,      default=__a			,      type=__a			,      required=__a			,      help="""The output directory where the model predictions and checkpoints will be written."""			,      )
     parser.add_argument(
         """--fp16"""			,      action="""store_true"""			,      help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""			,      )

     parser.add_argument(
         """--fp16_opt_level"""			,      type=__a			,      default="""O2"""			,      help=(
             """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
             """See details at https://nvidia.github.io/apex/amp.html"""
         )			,      )
     parser.add_argument("""--n_tpu_cores"""			,      dest="""tpu_cores"""			,      type=__a      )
     parser.add_argument("""--max_grad_norm"""			,      dest="""gradient_clip_val"""			,      default=1.0			,      type=__a			,      help="""Max gradient norm"""      )
     parser.add_argument("""--do_train"""			,      action="""store_true"""			,      help="""Whether to run training."""      )
     parser.add_argument("""--do_predict"""			,      action="""store_true"""			,      help="""Whether to run predictions on the test set."""      )
     parser.add_argument(
         """--gradient_accumulation_steps"""			,      dest="""accumulate_grad_batches"""			,      type=__a			,      default=1			,      help="""Number of updates steps to accumulate before performing a backward/update pass."""			,      )
     parser.add_argument("""--seed"""			,      type=__a			,      default=42			,      help="""random seed for initialization"""      )
     parser.add_argument(
         """--data_dir"""			,      default=__a			,      type=__a			,      required=__a			,      help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""			,      )



def  _lowerCAmelCase				(				__a			,      __a			,      __a=None			,      __a=True			,      __a=[]			,      __a=None			,      __a=None			,      **__a			,      )  ->      str:
     '''simple docstring'''







     pl.seed_everything(args.seed      )

     # init model
     _UpperCamelCase  :Any       =Path(model.hparams.output_dir      )
     odir.mkdir(exist_ok=__a      )

     # add custom checkpoints
     if checkpoint_callback is None:
          _UpperCamelCase  :Dict       =pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir			,      prefix="""checkpoint"""			,      monitor="""val_loss"""			,      mode="""min"""			,      save_top_k=1      )
     if early_stopping_callback:
          extra_callbacks.append(__a      )
     if logging_callback is None:
          _UpperCamelCase  :List[Any]       =LoggingCallback()

     _UpperCamelCase  :Union[str, Any]       ={}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          _UpperCamelCase  :Optional[Any]       =16
          _UpperCamelCase  :int       =args.fpaa_opt_level

     if args.gpus > 1:
          _UpperCamelCase  :int       ="""ddp"""

     _UpperCamelCase  :List[str]       =args.accumulate_grad_batches
     _UpperCamelCase  :Tuple       =extra_train_kwargs.get("""accelerator"""			,      __a      )
     _UpperCamelCase  :Optional[int]       =extra_train_kwargs.get("""profiler"""			,      __a      )

     _UpperCamelCase  :Any       =pl.Trainer.from_argparse_args(
         __a			,      weights_summary=__a			,      callbacks=[logging_callback] + extra_callbacks			,      logger=__a			,      checkpoint_callback=__a			,      **__a			,      )

     if args.do_train:
          trainer.fit(__a      )

     return trainer
'''simple docstring'''




from __future__ import annotations

import unittest
from collections.abc import Iterator


class    UpperCAmelCase__	:






   """simple docstring"""
   def __init__(self							,					_a							,					_a					)     ->  None:
        lowercase_       :							Union[str, Any]  					=       label
        lowercase_       :							List[str]  					=       parent
        lowercase_       :							Node | None  					=       None
        lowercase_       :							Node | None  					=       None



class    UpperCAmelCase__	:






   """simple docstring"""
   def __init__(self					)     ->  None:
        lowercase_       :							Node | None  					=       None
   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							int  					=       None
   def 		_lowerCamelCase			(self					)     ->  bool:
        return self.root is None
   def 		_lowerCamelCase			(self							,					_a					)     ->  None:
        lowercase_       :							Any  					=       self._put(self.root							,					_a					)
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a = None					)     ->  Node:
        if node is None:
             lowercase_       :							Union[str, Any]  					=       Node(_a							,					_a					)
        else:
             if label < node.label:
                  lowercase_       :							Optional[int]  					=       self._put(node.left							,					_a							,					_a					)
             elif label > node.label:
                  lowercase_       :							List[str]  					=       self._put(node.right							,					_a							,					_a					)
             else:
                  lowercase_       :							Optional[int]  					=       f'''Node with label {label} already exists'''
                  raise Exception(_a					)

        return node
   def 		_lowerCamelCase			(self							,					_a					)     ->  Node:
        return self._search(self.root							,					_a					)
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  Node:
        if node is None:
             lowercase_       :							Optional[int]  					=       f'''Node with label {label} does not exist'''
             raise Exception(_a					)
        else:
             if label < node.label:
                  lowercase_       :							int  					=       self._search(node.left							,					_a					)
             elif label > node.label:
                  lowercase_       :							str  					=       self._search(node.right							,					_a					)

        return node
   def 		_lowerCamelCase			(self							,					_a					)     ->  None:
        lowercase_       :							List[str]  					=       self.search(_a					)
        if node.right and node.left:
             lowercase_       :							int  					=       self._get_lowest_node(node.right					)
             lowercase_       :							Any  					=       node.left
             lowercase_       :							str  					=       node.right
             lowercase_       :							Any  					=       lowest_node
             if node.right:
                  lowercase_       :							Any  					=       lowest_node
             self._reassign_nodes(_a							,					_a					)
        elif not node.right and node.left:
             self._reassign_nodes(_a							,					node.left					)
        elif node.right and not node.left:
             self._reassign_nodes(_a							,					node.right					)
        else:
             self._reassign_nodes(_a							,					_a					)
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  None:
        if new_children:
             lowercase_       :							List[str]  					=       node.parent

        if node.parent:
             if node.parent.right == node:
                  lowercase_       :							str  					=       new_children
             else:
                  lowercase_       :							Tuple  					=       new_children
        else:
             lowercase_       :							Union[str, Any]  					=       new_children
   def 		_lowerCamelCase			(self							,					_a					)     ->  Node:
        if node.left:
             lowercase_       :							Optional[int]  					=       self._get_lowest_node(node.left					)
        else:
             lowercase_       :							str  					=       node
             self._reassign_nodes(_a							,					node.right					)

        return lowest_node
   def 		_lowerCamelCase			(self							,					_a					)     ->  bool:
        try:
             self.search(_a					)
             return True
        except Exception:
             return False
   def 		_lowerCamelCase			(self					)     ->  int:
        if self.root is None:
             raise Exception('Binary search tree is empty'					)

        lowercase_       :							Tuple  					=       self.root
        while node.right is not None:
             lowercase_       :							Union[str, Any]  					=       node.right

        return node.label
   def 		_lowerCamelCase			(self					)     ->  int:
        if self.root is None:
             raise Exception('Binary search tree is empty'					)

        lowercase_       :							Optional[Any]  					=       self.root
        while node.left is not None:
             lowercase_       :							Optional[int]  					=       node.left

        return node.label
   def 		_lowerCamelCase			(self					)     ->  Iterator[Node]:
        return self._inorder_traversal(self.root					)
   def 		_lowerCamelCase			(self							,					_a					)     ->  Iterator[Node]:
        if node is not None:
             yield from self._inorder_traversal(node.left					)
             yield node
             yield from self._inorder_traversal(node.right					)
   def 		_lowerCamelCase			(self					)     ->  Iterator[Node]:
        return self._preorder_traversal(self.root					)





   def 		_lowerCamelCase			(self							,					_a					)     ->  Iterator[Node]:
        if node is not None:
             yield node
             yield from self._preorder_traversal(node.left					)
             yield from self._preorder_traversal(node.right					)



class    UpperCAmelCase__	(							unittest.TestCase      ):






   """simple docstring"""
   @staticmethod
   def 		_lowerCamelCase			()     ->  BinarySearchTree:
        lowercase_       :							int  					=       BinarySearchTree()
        t.put(8					)
        t.put(3					)
        t.put(6					)
        t.put(1					)
        t.put(10					)
        t.put(14					)
        t.put(13					)
        t.put(4					)
        t.put(7					)
        t.put(5					)

        return t
   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							List[Any]  					=       BinarySearchTree()
        assert t.is_empty()

        t.put(8					)
        assert t.root is not None
        assert t.root.parent is None
        assert t.root.label == 8

        t.put(10					)
        assert t.root.right is not None
        assert t.root.right.parent == t.root
        assert t.root.right.label == 10

        t.put(3					)
        assert t.root.left is not None
        assert t.root.left.parent == t.root
        assert t.root.left.label == 3

        t.put(6					)
        assert t.root.left.right is not None
        assert t.root.left.right.parent == t.root.left
        assert t.root.left.right.label == 6

        t.put(1					)
        assert t.root.left.left is not None
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.left.label == 1

        with self.assertRaises(_a					):  # noqa: B017
             t.put(1					)
   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							List[Any]  					=       self._get_binary_search_tree()

        lowercase_       :							int  					=       t.search(6					)
        assert node.label == 6

        lowercase_       :							str  					=       t.search(13					)
        assert node.label == 13

        with self.assertRaises(_a					):  # noqa: B017
             t.search(2					)
   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							Any  					=       self._get_binary_search_tree()

        t.remove(13					)
        assert t.root is not None
        assert t.root.right is not None
        assert t.root.right.right is not None
        assert t.root.right.right.right is None
        assert t.root.right.right.left is None

        t.remove(7					)
        assert t.root.left is not None
        assert t.root.left.right is not None
        assert t.root.left.right.left is not None
        assert t.root.left.right.right is None
        assert t.root.left.right.left.label == 4

        t.remove(6					)
        assert t.root.left.left is not None
        assert t.root.left.right.right is not None
        assert t.root.left.left.label == 1
        assert t.root.left.right.label == 4
        assert t.root.left.right.right.label == 5
        assert t.root.left.right.left is None
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.right.parent == t.root.left

        t.remove(3					)
        assert t.root is not None
        assert t.root.left.label == 4
        assert t.root.left.right.label == 5
        assert t.root.left.left.label == 1
        assert t.root.left.parent == t.root
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.right.parent == t.root.left

        t.remove(4					)
        assert t.root.left is not None
        assert t.root.left.left is not None
        assert t.root.left.label == 5
        assert t.root.left.right is None
        assert t.root.left.left.label == 1
        assert t.root.left.parent == t.root
        assert t.root.left.left.parent == t.root.left
   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							Union[str, Any]  					=       self._get_binary_search_tree()

        t.remove(3					)
        assert t.root is not None
        assert t.root.left is not None
        assert t.root.left.left is not None
        assert t.root.left.right is not None
        assert t.root.left.right.left is not None
        assert t.root.left.right.right is not None
        assert t.root.left.label == 4
        assert t.root.left.right.label == 6
        assert t.root.left.left.label == 1
        assert t.root.left.right.right.label == 7
        assert t.root.left.right.left.label == 5
        assert t.root.left.parent == t.root
        assert t.root.left.right.parent == t.root.left
        assert t.root.left.left.parent == t.root.left
        assert t.root.left.right.left.parent == t.root.left.right
   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							Union[str, Any]  					=       self._get_binary_search_tree()
        t.empty()
        assert t.root is None
   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							str  					=       self._get_binary_search_tree()
        assert not t.is_empty()

        t.empty()
        assert t.is_empty()
   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							List[Any]  					=       self._get_binary_search_tree()

        assert t.exists(6					)
        assert not t.exists(-1					)
   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							Dict  					=       self._get_binary_search_tree()

        assert t.get_max_label() == 14

        t.empty()
        with self.assertRaises(_a					):  # noqa: B017
             t.get_max_label()
   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							Tuple  					=       self._get_binary_search_tree()

        assert t.get_min_label() == 1

        t.empty()
        with self.assertRaises(_a					):  # noqa: B017
             t.get_min_label()
   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							Tuple  					=       self._get_binary_search_tree()

        lowercase_       :							List[str]  					=       [i.label for i in t.inorder_traversal()]
        assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]





   def 		_lowerCamelCase			(self					)     ->  None:
        lowercase_       :							Tuple  					=       self._get_binary_search_tree()

        lowercase_       :							List[Any]  					=       [i.label for i in t.preorder_traversal()]
        assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]



def _UpperCamelCase (							):
     lowercase_       :							int  					=       BinarySearchTree()
     t.put(8				)
     t.put(3				)
     t.put(6				)
     t.put(1				)
     t.put(10				)
     t.put(14				)
     t.put(13				)
     t.put(4				)
     t.put(7				)
     t.put(5				)

     print(
         '\n            8\n           / \\\n          3   10\n         / \\    \\\n        1   6    14\n           / \\   /\n          4   7 13\n           \\\n            5\n        '				)

     print('Label 6 exists:'	,							t.exists(6				)				)
     print('Label 13 exists:'	,							t.exists(13				)				)
     print('Label -1 exists:'	,							t.exists(-1				)				)
     print('Label 12 exists:'	,							t.exists(12				)				)

     # Prints all the elements of the list in inorder traversal
     lowercase_       :							Optional[int]  					=       [i.label for i in t.inorder_traversal()]
     print('Inorder traversal:'	,							SCREAMING_SNAKE_CASE_				)

     # Prints all the elements of the list in preorder traversal
     lowercase_       :							str  					=       [i.label for i in t.preorder_traversal()]
     print('Preorder traversal:'	,							SCREAMING_SNAKE_CASE_				)

     print('Max. label:'	,							t.get_max_label()				)
     print('Min. label:'	,							t.get_min_label()				)

     # Delete elements
     print('\nDeleting elements 13, 10, 8, 3, 6, 14'				)
     print(
         '\n          4\n         / \\\n        1   7\n             \\\n              5\n        '				)
     t.remove(13				)
     t.remove(10				)
     t.remove(8				)
     t.remove(3				)
     t.remove(6				)
     t.remove(14				)

     # Prints all the elements of the list in inorder traversal after delete
     lowercase_       :							Dict  					=       [i.label for i in t.inorder_traversal()]
     print('Inorder traversal after delete:'	,							SCREAMING_SNAKE_CASE_				)

     # Prints all the elements of the list in preorder traversal after delete
     lowercase_       :							Dict  					=       [i.label for i in t.preorder_traversal()]
     print('Preorder traversal after delete:'	,							SCREAMING_SNAKE_CASE_				)

     print('Max. label:'	,							t.get_max_label()				)
     print('Min. label:'	,							t.get_min_label()				)


if __name__ == "__main__":
 binary_search_tree_example()




