





import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/text-classification/requirements.txt''')

_lowerCamelCase :    str  							= logging.getLogger(__name__)


@dataclass
class 						lowerCamelCase       :







							"""simple docstring"""




							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=__lowerCamelCase							,     metadata={"help": "Overwrite the cached preprocessed datasets or not."}      )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={
							        "help": (
							            "Whether to pad all samples to `max_seq_length`. "
							            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
							        )
							    }							,     )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }							,     )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }							,     )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
							            "value if set."
							        )
							    }							,     )


@dataclass
class 						lowerCamelCase       :







							"""simple docstring"""




							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}      )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={"help": "Train language if it is different from the evaluation language."}      )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}      )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,     )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}							,     )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}							,     )
							UpperCAmelCase_      						=   field(
							    default="main"							,     metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}							,     )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }							,     )
							UpperCAmelCase_      						=   field(
							    default=__lowerCamelCase							,     metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}							,     )




def _a (  )	->    Optional[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[str]     =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry("run_xnli"			,				SCREAMING_SNAKE_CASE__       )

							# Setup logging
							logging.basicConfig(
							    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,				datefmt="%m/%d/%Y %H:%M:%S"			,				handlers=[logging.StreamHandler(sys.stdout       )]			,				)

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							SCREAMING_SNAKE_CASE__       :      Tuple     =						training_args.get_process_log_level()
							logger.setLevel(SCREAMING_SNAKE_CASE__       )
							datasets.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE__       )
							transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE__       )
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()

							# Log on each process the small summary:
							logger.warning(
							    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
							    + f'''distributed training: {bool(training_args.local_rank != -1       )}, 16-bits training: {training_args.fpaa}'''       )
							logger.info(f'''Training/evaluation parameters {training_args}'''       )

							# Detecting last checkpoint.
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						None
							if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
														SCREAMING_SNAKE_CASE__       :      Tuple     =						get_last_checkpoint(training_args.output_dir       )
														if last_checkpoint is None and len(os.listdir(training_args.output_dir       )       ) > 0:
																					raise ValueError(
																					    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
																					    "Use --overwrite_output_dir to overcome."       )
														elif last_checkpoint is not None:
																					logger.info(
																					    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
																					    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."       )

    # Set seed before initializing model.
							set_seed(training_args.seed       )

							# In distributed training, the load_dataset function guarantees that only one local process can concurrently
							# download the dataset.
							# Downloading and loading xnli dataset from the hub.
							if training_args.do_train:
														if model_args.train_language is None:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						load_dataset(
																					    "xnli"			,				model_args.language			,				split="train"			,				cache_dir=model_args.cache_dir			,				use_auth_token=True if model_args.use_auth_token else None			,				)
														else:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						load_dataset(
																					    "xnli"			,				model_args.train_language			,				split="train"			,				cache_dir=model_args.cache_dir			,				use_auth_token=True if model_args.use_auth_token else None			,				)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						train_dataset.features["label"].names

							if training_args.do_eval:
														SCREAMING_SNAKE_CASE__       :      Tuple     =						load_dataset(
														    "xnli"			,				model_args.language			,				split="validation"			,				cache_dir=model_args.cache_dir			,				use_auth_token=True if model_args.use_auth_token else None			,				)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						eval_dataset.features["label"].names

							if training_args.do_predict:
														SCREAMING_SNAKE_CASE__       :      Dict     =						load_dataset(
														    "xnli"			,				model_args.language			,				split="test"			,				cache_dir=model_args.cache_dir			,				use_auth_token=True if model_args.use_auth_token else None			,				)
														SCREAMING_SNAKE_CASE__       :      str     =						predict_dataset.features["label"].names

							# Labels
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						len(SCREAMING_SNAKE_CASE__       )

							# Load pretrained model and tokenizer
							# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,				num_labels=SCREAMING_SNAKE_CASE__			,				idalabel={str(SCREAMING_SNAKE_CASE__       ): label for i, label in enumerate(SCREAMING_SNAKE_CASE__       )}			,				labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__       )}			,				finetuning_task="xnli"			,				cache_dir=model_args.cache_dir			,				revision=model_args.model_revision			,				use_auth_token=True if model_args.use_auth_token else None			,				)
							SCREAMING_SNAKE_CASE__       :      Any     =						AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,				do_lower_case=model_args.do_lower_case			,				cache_dir=model_args.cache_dir			,				use_fast=model_args.use_fast_tokenizer			,				revision=model_args.model_revision			,				use_auth_token=True if model_args.use_auth_token else None			,				)
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						AutoModelForSequenceClassification.from_pretrained(
							    model_args.model_name_or_path			,				from_tf=bool(".ckpt" in model_args.model_name_or_path       )			,				config=SCREAMING_SNAKE_CASE__			,				cache_dir=model_args.cache_dir			,				revision=model_args.model_revision			,				use_auth_token=True if model_args.use_auth_token else None			,				ignore_mismatched_sizes=model_args.ignore_mismatched_sizes			,				)

							# Preprocessing the datasets
							# Padding strategy
							if data_args.pad_to_max_length:
														SCREAMING_SNAKE_CASE__       :      int     =						"max_length"
							else:
														# We will pad later, dynamically at batch creation, to the max sequence length in each batch
														SCREAMING_SNAKE_CASE__       :      int     =						False

							def preprocess_function(SCREAMING_SNAKE_CASE__      :  List[str]       ):
														# Tokenize the texts
														return tokenizer(
														    examples["premise"]			,				examples["hypothesis"]			,				padding=SCREAMING_SNAKE_CASE__			,				max_length=data_args.max_seq_length			,				truncation=SCREAMING_SNAKE_CASE__			,				)

							if training_args.do_train:
														if data_args.max_train_samples is not None:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						min(len(SCREAMING_SNAKE_CASE__       )			,				data_args.max_train_samples       )
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						train_dataset.select(range(SCREAMING_SNAKE_CASE__       )       )
														with training_args.main_process_first(desc="train dataset map pre-processing"       ):
																					SCREAMING_SNAKE_CASE__       :      str     =						train_dataset.map(
																					    SCREAMING_SNAKE_CASE__			,				batched=SCREAMING_SNAKE_CASE__			,				load_from_cache_file=not data_args.overwrite_cache			,				desc="Running tokenizer on train dataset"			,				)
														# Log a few random samples from the training set:
														for index in random.sample(range(len(SCREAMING_SNAKE_CASE__       )       )			,				3       ):
																					logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.'''       )

							if training_args.do_eval:
														if data_args.max_eval_samples is not None:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						min(len(SCREAMING_SNAKE_CASE__       )			,				data_args.max_eval_samples       )
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						eval_dataset.select(range(SCREAMING_SNAKE_CASE__       )       )
														with training_args.main_process_first(desc="validation dataset map pre-processing"       ):
																					SCREAMING_SNAKE_CASE__       :      Any     =						eval_dataset.map(
																					    SCREAMING_SNAKE_CASE__			,				batched=SCREAMING_SNAKE_CASE__			,				load_from_cache_file=not data_args.overwrite_cache			,				desc="Running tokenizer on validation dataset"			,				)

							if training_args.do_predict:
														if data_args.max_predict_samples is not None:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						min(len(SCREAMING_SNAKE_CASE__       )			,				data_args.max_predict_samples       )
																					SCREAMING_SNAKE_CASE__       :      Dict     =						predict_dataset.select(range(SCREAMING_SNAKE_CASE__       )       )
														with training_args.main_process_first(desc="prediction dataset map pre-processing"       ):
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						predict_dataset.map(
																					    SCREAMING_SNAKE_CASE__			,				batched=SCREAMING_SNAKE_CASE__			,				load_from_cache_file=not data_args.overwrite_cache			,				desc="Running tokenizer on prediction dataset"			,				)

    # Get the metric function
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						evaluate.load("xnli"       )

							# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
							# predictions and label_ids field) and has to return a dictionary string to float.
							def compute_metrics(SCREAMING_SNAKE_CASE__      :  EvalPrediction       ):
														SCREAMING_SNAKE_CASE__       :      Any     =						p.predictions[0] if isinstance(p.predictions			,				SCREAMING_SNAKE_CASE__       ) else p.predictions
														SCREAMING_SNAKE_CASE__       :      List[str]     =						np.argmax(SCREAMING_SNAKE_CASE__			,				axis=1       )
														return metric.compute(predictions=SCREAMING_SNAKE_CASE__			,				references=p.label_ids       )

							# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
							if data_args.pad_to_max_length:
														SCREAMING_SNAKE_CASE__       :      Dict     =						default_data_collator
							elif training_args.fpaa:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						DataCollatorWithPadding(SCREAMING_SNAKE_CASE__			,				pad_to_multiple_of=8       )
							else:
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						None

							# Initialize our Trainer
							SCREAMING_SNAKE_CASE__       :      Any     =						Trainer(
							    model=SCREAMING_SNAKE_CASE__			,				args=SCREAMING_SNAKE_CASE__			,				train_dataset=train_dataset if training_args.do_train else None			,				eval_dataset=eval_dataset if training_args.do_eval else None			,				compute_metrics=SCREAMING_SNAKE_CASE__			,				tokenizer=SCREAMING_SNAKE_CASE__			,				data_collator=SCREAMING_SNAKE_CASE__			,				)

							# Training
							if training_args.do_train:
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						None
														if training_args.resume_from_checkpoint is not None:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						last_checkpoint
														SCREAMING_SNAKE_CASE__       :      Dict     =						trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Tuple     =						train_result.metrics
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						(
														    data_args.max_train_samples if data_args.max_train_samples is not None else len(SCREAMING_SNAKE_CASE__       )
														)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						min(SCREAMING_SNAKE_CASE__			,				len(SCREAMING_SNAKE_CASE__       )       )

														trainer.save_model()  # Saves the tokenizer too for easy upload

														trainer.log_metrics("train"			,				SCREAMING_SNAKE_CASE__       )
														trainer.save_metrics("train"			,				SCREAMING_SNAKE_CASE__       )
														trainer.save_state()

							# Evaluation
							if training_args.do_eval:
														logger.info("*** Evaluate ***"       )
														SCREAMING_SNAKE_CASE__       :      int     =						trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE__       )

														SCREAMING_SNAKE_CASE__       :      str     =						data_args.max_eval_samples if data_args.max_eval_samples is not None else len(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						min(SCREAMING_SNAKE_CASE__			,				len(SCREAMING_SNAKE_CASE__       )       )

														trainer.log_metrics("eval"			,				SCREAMING_SNAKE_CASE__       )
														trainer.save_metrics("eval"			,				SCREAMING_SNAKE_CASE__       )

							# Prediction
							if training_args.do_predict:
														logger.info("*** Predict ***"       )
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						trainer.predict(SCREAMING_SNAKE_CASE__			,				metric_key_prefix="predict"       )

														SCREAMING_SNAKE_CASE__       :      str     =						(
														    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(SCREAMING_SNAKE_CASE__       )
														)
														SCREAMING_SNAKE_CASE__       :      int     =						min(SCREAMING_SNAKE_CASE__			,				len(SCREAMING_SNAKE_CASE__       )       )

														trainer.log_metrics("predict"			,				SCREAMING_SNAKE_CASE__       )
														trainer.save_metrics("predict"			,				SCREAMING_SNAKE_CASE__       )

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						np.argmax(SCREAMING_SNAKE_CASE__			,				axis=1       )
														SCREAMING_SNAKE_CASE__       :      Any     =						os.path.join(training_args.output_dir			,				"predictions.txt"       )
														if trainer.is_world_process_zero():
																					with open(SCREAMING_SNAKE_CASE__			,				"w"       ) as writer:
																												writer.write("index\tprediction\n"       )
																												for index, item in enumerate(SCREAMING_SNAKE_CASE__       ):
																																			SCREAMING_SNAKE_CASE__       :      Dict     =						label_list[item]
																																			writer.write(f'''{index}\t{item}\n'''       )


if __name__ == "__main__":
		main()













class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						name
														SCREAMING_SNAKE_CASE__       :      Tuple     =						val






							def __str__( self     :					str						)				->			List[Any]:





														"""simple docstring"""
														return F'''{self.__class__.__name__}({self.name}, {self.val})'''






							def __lt__( self     :					str,			_UpperCAmelCase     :					Dict						)				->			Union[str, Any]:





														"""simple docstring"""
														return self.val < other.val


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Optional[Any],			_UpperCAmelCase     :					str						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{}
														SCREAMING_SNAKE_CASE__       :      str     =						{}
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.build_heap(_UpperCAmelCase						)






							def __getitem__( self     :					Union[str, Any],			_UpperCAmelCase     :					Any						)				->			Dict:





														"""simple docstring"""
														return self.get_value(_UpperCAmelCase						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Optional[int]						)				->			Any:





														"""simple docstring"""
														return (idx - 1) // 2






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Any						)				->			Dict:





														"""simple docstring"""
														return idx * 2 + 1






							def  A_     ( self     :					str,			_UpperCAmelCase     :					List[str]						)				->			Union[str, Any]:





														"""simple docstring"""
														return idx * 2 + 2






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					int						)				->			Optional[int]:





														"""simple docstring"""
														return self.heap_dict[key]






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					Dict						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						len(_UpperCAmelCase						) - 1
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.get_parent_idx(_UpperCAmelCase						)

														for idx, i in enumerate(_UpperCAmelCase						):
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						idx
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						i.val

														for i in range(_UpperCAmelCase,			-1,			-1						):
																					self.sift_down(_UpperCAmelCase,			_UpperCAmelCase						)
														return array






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Tuple						)				->			Tuple:





														"""simple docstring"""
														while True:
																					SCREAMING_SNAKE_CASE__       :      str     =						self.get_left_child_idx(_UpperCAmelCase						)  # noqa: E741
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_right_child_idx(_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Tuple     =						idx
																					if l < len(_UpperCAmelCase						) and array[l] < array[idx]:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						l
																					if r < len(_UpperCAmelCase						) and array[r] < array[smallest]:
																												SCREAMING_SNAKE_CASE__       :      int     =						r

																					if smallest != idx:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						array[smallest], array[idx]
																												(
																												    (
																												    SCREAMING_SNAKE_CASE__
																												)   ,(
																												    SCREAMING_SNAKE_CASE__
																												)   ,
																												)       :      Optional[Any]     =						(
																												    self.idx_of_element[array[smallest]],
																												    self.idx_of_element[array[idx]],
																												)
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						smallest
																					else:
																												break






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						self.get_parent_idx(_UpperCAmelCase						)
														while p >= 0 and self.heap[p] > self.heap[idx]:
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						self.heap[idx], self.heap[p]
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						(
																					    self.idx_of_element[self.heap[idx]],
																					    self.idx_of_element[self.heap[p]],
																					)
																					SCREAMING_SNAKE_CASE__       :      Dict     =						p
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						self.get_parent_idx(_UpperCAmelCase						)






							def  A_     ( self     :					str						)				->			List[str]:





														"""simple docstring"""
														return self.heap[0]






							def  A_     ( self     :					Tuple						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.heap[-1], self.heap[0]
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[Any]     =						(
														    self.idx_of_element[self.heap[-1]],
														    self.idx_of_element[self.heap[0]],
														)

														SCREAMING_SNAKE_CASE__       :      Any     =						self.heap.pop()
														del self.idx_of_element[x]
														self.sift_down(0,			self.heap						)
														return x






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														self.heap.append(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						len(self.heap						) - 1
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						node.val
														self.sift_up(len(self.heap						) - 1						)






							def  A_     ( self     :					Optional[int]						)				->			int:





														"""simple docstring"""
														return len(self.heap						) == 0






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					str						)				->			Dict:





														"""simple docstring"""
														assert (
														    self.heap[self.idx_of_element[node]].val > new_value
														), "newValue must be less that current value"
														SCREAMING_SNAKE_CASE__       :      Tuple     =						new_value
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						new_value
														self.sift_up(self.idx_of_element[node]						)


_lowerCamelCase :    Tuple  							= Node('''R''', -1)
_lowerCamelCase :    int  							= Node('''B''', 6)
_lowerCamelCase :    str  							= Node('''A''', 3)
_lowerCamelCase :    Optional[Any]  							= Node('''X''', 1)
_lowerCamelCase :    str  							= Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
_lowerCamelCase :    int  							= MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
		print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
		print(i)

if __name__ == "__main__":
		import doctest

		doctest.testmod()






