


from __future__ import annotations

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







@dataclass
class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						42
							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						None







def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     Dict					=					Node(1				)
			SCREAMING_SNAKE_CASE_		:     Tuple					=					Node(2				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					Node(3				)
			SCREAMING_SNAKE_CASE_		:     int					=					Node(4				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					Node(5				)
			return tree






def 						a__			(				A__				):
			return [root.data, *preorder(root.left				), *preorder(root.right				)] if root else []






def 						a__			(				A__				):
			return postorder(root.left				) + postorder(root.right				) + [root.data] if root else []






def 						a__			(				A__				):
			return [*inorder(root.left				), root.data, *inorder(root.right				)] if root else []






def 						a__			(				A__				):
			return (max(height(root.left				),      height(root.right				)				) + 1) if root else 0






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     list[Any]					=					[]

			if root is None:
						return output

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

			while process_queue:
						SCREAMING_SNAKE_CASE_		:     int					=					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 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     list[Any]					=					[]

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






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     list[Any]					=					[]

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






def 						a__			(				A__				):
			if root is None:
						return []

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

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					0
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					height(A__				)

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

			return output






def 						a__			(				):  # Main function for testing.
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					make_tree()
			print(F'''In-order Traversal: {inorder(A__				)}'''				)
			print(F'''Pre-order Traversal: {preorder(A__				)}'''				)
			print(F'''Post-order Traversal: {postorder(A__				)}''',      '\n'				)

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

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

			print('Level-wise order Traversal: '				)

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

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


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()






import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowerCAmelCase       =		logging.getLogger(__name__)
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return (preds == labels).mean()
@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Pretrained config name or path if not the same as model_name"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},  )




@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
							UpperCAmelCase				   =     field(metadata={"help": "Should contain the data files for the task."} )
							UpperCAmelCase				   =     field(
							    default=128,  metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    },  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Overwrite the cached training and evaluation sets"} )




def 					_snake_case	(  ):
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					_UpperCamelCase			     =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
					_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir						)
					    and os.listdir(training_args.output_dir						)
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
										    ''' --overwrite_output_dir to overcome.'''						)

					# Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,     datefmt='''%m/%d/%Y %H:%M:%S'''  ,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,     )
					logger.warning(
					    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''  ,     training_args.local_rank  ,     training_args.device  ,     training_args.n_gpu  ,     bool(training_args.local_rank != -1						)  ,     training_args.fpaa  ,     )
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank						):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info('''Training/evaluation parameters %s'''  ,     __snake_case						)

					# Set seed
					set_seed(training_args.seed						)

					try:
										_UpperCamelCase			     =     processors[data_args.task_name]()
										_UpperCamelCase			     =     processor.get_labels()
										_UpperCamelCase			     =     len(__snake_case						)
					except KeyError:
										raise ValueError('''Task not found: %s''' % (data_args.task_name)						)

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					_UpperCamelCase			     =     AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,     num_labels=__snake_case  ,     finetuning_task=data_args.task_name  ,     cache_dir=model_args.cache_dir  ,     )
					_UpperCamelCase			     =     AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,     cache_dir=model_args.cache_dir  ,     )
					_UpperCamelCase			     =     AutoModelForMultipleChoice.from_pretrained(
					    model_args.model_name_or_path  ,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)  ,     config=__snake_case  ,     cache_dir=model_args.cache_dir  ,     )

					# Get datasets
					_UpperCamelCase			     =     (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir  ,     tokenizer=__snake_case  ,     task=data_args.task_name  ,     max_seq_length=data_args.max_seq_length  ,     overwrite_cache=data_args.overwrite_cache  ,     mode=Split.train  ,     )
					    if training_args.do_train
					    else None
					)
					_UpperCamelCase			     =     (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir  ,     tokenizer=__snake_case  ,     task=data_args.task_name  ,     max_seq_length=data_args.max_seq_length  ,     overwrite_cache=data_args.overwrite_cache  ,     mode=Split.dev  ,     )
					    if training_args.do_eval
					    else None
					)

					def compute_metrics(__snake_case						) -> Dict:
										_UpperCamelCase			     =     np.argmax(p.predictions  ,     axis=1						)
										return {"acc": simple_accuracy(__snake_case  ,     p.label_ids						)}

					# Data collator
					_UpperCamelCase			     =     DataCollatorWithPadding(__snake_case  ,     pad_to_multiple_of=8						) if training_args.fpaa else None

					# Initialize our Trainer
					_UpperCamelCase			     =     Trainer(
					    model=__snake_case  ,     args=__snake_case  ,     train_dataset=__snake_case  ,     eval_dataset=__snake_case  ,     compute_metrics=__snake_case  ,     data_collator=__snake_case  ,     )

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
					_UpperCamelCase			     =     {}
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''						)

										_UpperCamelCase			     =     trainer.evaluate()

										_UpperCamelCase			     =     os.path.join(training_args.output_dir  ,     '''eval_results.txt'''						)
										if trainer.is_world_master():
															with open(__snake_case  ,     '''w'''						) as writer:
																				logger.info('''***** Eval results *****'''						)
																				for key, value in result.items():
																									logger.info('''  %s = %s'''  ,     __snake_case  ,     __snake_case						)
																									writer.write('''%s = %s\n''' % (key, value)						)

																				results.update(__snake_case						)

					return results
def 					_snake_case	(  __snake_case						):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
					main()
