





'''simple docstring'''




import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


lowercase__   :				Optional[int]      =   logging.getLogger(__name__)


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''sequence-classification'''


     def __init__(						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if type(_UpperCAmelCase) == dict:
               __A						:			Any								=	Namespace(**_UpperCAmelCase)
          __A						:			Union[str, Any]								=	glue_output_modes[hparams.task]
          __A						:			List[Any]								=	glue_tasks_num_labels[hparams.task]

          super().__init__(_UpperCAmelCase						,     _UpperCAmelCase						,     self.mode)


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






          return self.model(**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Any								=	{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

          if self.config.model_type not in ["distilbert", "bart"]:
               __A						:			List[Any]								=	batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

          __A						:			Dict								=	self(**_UpperCAmelCase)
          __A						:			str								=	outputs[0]

          __A						:			str								=	self.trainer.lr_schedulers[0]['scheduler']
          __A						:			List[Any]								=	{'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
          return {"loss": loss, "log": tensorboard_logs}


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	self.hparams
          __A						:			Optional[Any]								=	processors[args.task]()
          __A						:			Union[str, Any]								=	processor.get_labels()

          for mode in ["train", "dev"]:
               __A						:			int								=	self._feature_file(_UpperCAmelCase)
               if os.path.exists(_UpperCAmelCase) and not args.overwrite_cache:
                    logger.info('Loading features from cached file %s'						,     _UpperCAmelCase)
               else:
                    logger.info('Creating features from dataset file at %s'						,     args.data_dir)
                    __A						:			Optional[int]								=	(
                        processor.get_dev_examples(args.data_dir)
                        if mode == 'dev'
                        else processor.get_train_examples(args.data_dir)
                    )
                    __A						:			Any								=	convert_examples_to_features(
                        _UpperCAmelCase						,     self.tokenizer						,     max_length=args.max_seq_length						,     label_list=self.labels						,     output_mode=args.glue_output_mode						,     )
                    logger.info('Saving features into cached file %s'						,     _UpperCAmelCase)
                    torch.save(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase = False):

          '''simple docstring'''






          __A						:			Dict								=	'dev' if mode == 'test' else mode

          __A						:			Optional[Any]								=	self._feature_file(_UpperCAmelCase)
          logger.info('Loading features from cached file %s'						,     _UpperCAmelCase)
          __A						:			Optional[Any]								=	torch.load(_UpperCAmelCase)
          __A						:			Union[str, Any]								=	torch.tensor([f.input_ids for f in features]						,     dtype=torch.long)
          __A						:			Any								=	torch.tensor([f.attention_mask for f in features]						,     dtype=torch.long)
          __A						:			Optional[Any]								=	torch.tensor([f.token_type_ids for f in features]						,     dtype=torch.long)
          if self.hparams.glue_output_mode == "classification":
               __A						:			Union[str, Any]								=	torch.tensor([f.label for f in features]						,     dtype=torch.long)
          elif self.hparams.glue_output_mode == "regression":
               __A						:			int								=	torch.tensor([f.label for f in features]						,     dtype=torch.float)

          return DataLoader(
              TensorDataset(_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase)						,     batch_size=_UpperCAmelCase						,     shuffle=_UpperCAmelCase						,     )


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Tuple								=	{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

          if self.config.model_type not in ["distilbert", "bart"]:
               __A						:			Dict								=	batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

          __A						:			Any								=	self(**_UpperCAmelCase)
          __A						:			str								=	outputs[:2]
          __A						:			Optional[Any]								=	logits.detach().cpu().numpy()
          __A						:			List[str]								=	inputs['labels'].detach().cpu().numpy()

          return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	torch.stack([x['val_loss'] for x in outputs]).mean().detach().cpu().item()
          __A						:			str								=	np.concatenate([x['pred'] for x in outputs]						,     axis=0)

          if self.hparams.glue_output_mode == "classification":
               __A						:			int								=	np.argmax(_UpperCAmelCase						,     axis=1)
          elif self.hparams.glue_output_mode == "regression":
               __A						:			Dict								=	np.squeeze(_UpperCAmelCase)

          __A						:			Union[str, Any]								=	np.concatenate([x['target'] for x in outputs]						,     axis=0)
          __A						:			Dict								=	[[] for _ in range(out_label_ids.shape[0])]
          __A						:			int								=	[[] for _ in range(out_label_ids.shape[0])]

          __A						:			Dict								=	{**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task						,     _UpperCAmelCase						,     _UpperCAmelCase)}

          __A						:			Tuple								=	dict(results.items())
          __A						:			Optional[int]								=	results
          return ret, preds_list, out_label_list


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			int								=	self._eval_end(_UpperCAmelCase)
          __A						:			Dict								=	ret['log']
          return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self._eval_end(_UpperCAmelCase)
          __A						:			Tuple								=	ret['log']
          # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
          return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


     @staticmethod
     def 							SCREAMING_SNAKE_CASE  (						_UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          BaseTransformer.add_model_specific_args(_UpperCAmelCase						,     _UpperCAmelCase)
          parser.add_argument(
              '--max_seq_length'						,     default=128						,     type=_UpperCAmelCase						,     help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )						,     )

          parser.add_argument(
              '--task'						,     default=''						,     type=_UpperCAmelCase						,     required=_UpperCAmelCase						,     help='The GLUE task to run'						,     )
          parser.add_argument(
              '--gpus'						,     default=0						,     type=_UpperCAmelCase						,     help='The number of GPUs allocated for this, it is by default 0 meaning none'						,     )

          parser.add_argument(
              '--overwrite_cache'						,     action='store_true'						,     help='Overwrite the cached training and evaluation sets')

          return parser






def 	_lowerCAmelCase      (  )					->					Dict:
     __A						:			int								=	argparse.ArgumentParser()
     add_generic_args(__snake_case			,							os.getcwd()      )
     __A						:			Tuple								=	GLUETransformer.add_model_specific_args(__snake_case			,							os.getcwd()      )
     __A						:			Union[str, Any]								=	parser.parse_args()

     # If output_dir not provided, a folder will be generated in pwd
     if args.output_dir is None:
          __A						:			List[str]								=	os.path.join(
              './results'			,							f'{args.task}_{time.strftime("%Y%m%d_%H%M%S"      )}'			,							)
          os.makedirs(args.output_dir      )

     __A						:			Any								=	GLUETransformer(__snake_case      )
     __A						:			List[Any]								=	generic_train(__snake_case			,							__snake_case      )

     # Optionally, predict on dev set and write to output_dir
     if args.do_predict:
          __A						:			List[str]								=	sorted(glob.glob(os.path.join(args.output_dir			,							'checkpoint-epoch=*.ckpt'      )			,							recursive=__snake_case      )      )
          __A						:			List[Any]								=	model.load_from_checkpoint(checkpoints[-1]      )
          return trainer.test(__snake_case      )


if __name__ == "__main__":
  main()













'''simple docstring'''




from collections.abc import Iterable
from typing import Any


class 			SCREAMING_SNAKE_CASE   :


     def __init__(						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          __A						:			str								=	value
          __A						:			Node | None								=	None  # Added in order to delete a node easier
          __A						:			Node | None								=	None
          __A						:			Node | None								=	None


     def __repr__(						self):

          '''simple docstring'''






          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 			SCREAMING_SNAKE_CASE   :


     def __init__(						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          __A						:			int								=	root


     def __str__(						self):

          '''simple docstring'''






          return str(self.root)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          if new_children is not None:  # reset its kids
               __A						:			Optional[int]								=	node.parent
          if node.parent is not None:  # reset its parent
               if self.is_right(_UpperCAmelCase):  # If it is the right children
                    __A						:			int								=	new_children
               else:
                    __A						:			Union[str, Any]								=	new_children
          else:
               __A						:			Tuple								=	new_children


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if node.parent and node.parent.right:
               return node == node.parent.right
          return False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.root is None


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	Node(_UpperCAmelCase)  # create a new Node
          if self.empty():  # if Tree is empty
               __A						:			Union[str, Any]								=	new_node  # set its root
          else:  # Tree is not empty
               __A						:			Dict								=	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						:			Tuple								=	new_node  # We insert the new node in a leaf
                              break
                         else:
                              __A						:			str								=	parent_node.left
                    else:
                         if parent_node.right is None:
                              __A						:			List[str]								=	new_node
                              break
                         else:
                              __A						:			int								=	parent_node.right
               __A						:			int								=	parent_node


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase):

          '''simple docstring'''






          for value in values:
               self.__insert(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if self.empty():
               raise IndexError('Warning: Tree is empty! please use another.')
          else:
               __A						:			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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          if node is None:
               if self.root is None:
                    return None
               __A						:			str								=	self.root

          if not self.empty():
               while node.right is not None:
                    __A						:			Dict								=	node.right
          return node


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          if node is None:
               __A						:			Optional[Any]								=	self.root
          if self.root is None:
               return None
          if not self.empty():
               __A						:			Optional[int]								=	self.root
               while node.left is not None:
                    __A						:			Tuple								=	node.left
          return node


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.search(_UpperCAmelCase)  # 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(_UpperCAmelCase						,     _UpperCAmelCase)
               elif node.left is None:  # Has only right children
                    self.__reassign_nodes(_UpperCAmelCase						,     node.right)
               elif node.right is None:  # Has only left children
                    self.__reassign_nodes(_UpperCAmelCase						,     node.left)
               else:
                    __A						:			str								=	self.get_max(
                        node.left)  # Gets the max value of the left branch
                    self.remove(tmp_node.value)  # type: ignore
                    __A						:			List[Any]								=	(
                        tmp_node.value  # type: ignore
                    )  # Assigns the value to the node to delete and keep tree structure


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if node is not None:
               yield node  # Preorder Traversal
               yield from self.preorder_traverse(node.left)
               yield from self.preorder_traverse(node.right)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase=None):

          '''simple docstring'''






          if traversal_function is None:
               return self.preorder_traverse(self.root)
          else:
               return traversal_function(self.root)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          if node:
               self.inorder(_UpperCAmelCase						,     node.left)
               arr.append(node.value)
               self.inorder(_UpperCAmelCase						,     node.right)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			list[int]								=	[]
          self.inorder(_UpperCAmelCase						,     _UpperCAmelCase)  # append all values to list using inorder traversal
          return arr[k - 1]






def 	_lowerCAmelCase      (  __snake_case  :			Node | None      )					->					list[Node]:
     __A						:			Tuple								=	[]
     if curr_node is not None:
          __A						:			Optional[Any]								=	postorder(curr_node.left      ) + postorder(curr_node.right      ) + [curr_node]
     return node_list






def 	_lowerCAmelCase      (  )					->					None:
     __A						:			Optional[int]								=	(8, 3, 6, 1, 10, 14, 13, 4, 7)
     __A						:			str								=	BinarySearchTree()
     for i in testlist:
          t.insert(__snake_case      )

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

     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(__snake_case      )
          print(__snake_case      )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)