

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')

a_     =			logging.getLogger(__name__)






@dataclass
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__							=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.'
			        )
			    }							,		)
			lowerCamelCase__							=field(
			    default=UpperCAmelCase__							,		metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}      )
			lowerCamelCase__							=field(
			    default=UpperCAmelCase__							,		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.'
			        )
			    }							,		)
			lowerCamelCase__							=field(
			    default=UpperCAmelCase__							,		metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of training examples to this '
			            'value if set.'
			        )
			    }							,		)
			lowerCamelCase__							=field(
			    default=UpperCAmelCase__							,		metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
			            'value if set.'
			        )
			    }							,		)
			lowerCamelCase__							=field(
			    default=UpperCAmelCase__							,		metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of prediction examples to this '
			            'value if set.'
			        )
			    }							,		)






@dataclass
class _UpperCamelCase							:
			'''simple docstring'''







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

def        lowerCamelCase__       (    ):
	# 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.

	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
	SCREAMING_SNAKE_CASE      :					int    =				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"							,	__lowerCAmelCase)

	# 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      :					int    =				training_args.get_process_log_level()
	logger.setLevel(__lowerCAmelCase)
	datasets.utils.logging.set_verbosity(__lowerCAmelCase)
	transformers.utils.logging.set_verbosity(__lowerCAmelCase)
	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      :					Union[str, Any]    =				None
	if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
		SCREAMING_SNAKE_CASE      :					Dict    =				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      :					List[Any]    =				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      :					Union[str, 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      :					Optional[Any]    =				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      :					Optional[Any]    =				eval_dataset.features["label"].names

	if training_args.do_predict:
		SCREAMING_SNAKE_CASE      :					int    =				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      :					Dict    =				predict_dataset.features["label"].names

	# Labels
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				len(__lowerCAmelCase)

	# 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      :					List[str]    =				AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,	num_labels=__lowerCAmelCase							,	idalabel={str(__lowerCAmelCase): label for i, label in enumerate(__lowerCAmelCase)}							,	labelaid={label: i for i, label in enumerate(__lowerCAmelCase)}							,	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      :					Union[str, 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      :					str    =				AutoModelForSequenceClassification.from_pretrained(
	    model_args.model_name_or_path							,	from_tf=bool(".ckpt" in model_args.model_name_or_path)							,	config=__lowerCAmelCase							,	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      :					Optional[Any]    =				"max_length"
	else:
		# We will pad later, dynamically at batch creation, to the max sequence length in each batch
		SCREAMING_SNAKE_CASE      :					List[str]    =				False

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

	if training_args.do_train:
		if data_args.max_train_samples is not None:
			SCREAMING_SNAKE_CASE      :					Any    =				min(len(__lowerCAmelCase)							,	data_args.max_train_samples)
			SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				train_dataset.select(range(__lowerCAmelCase))
		with training_args.main_process_first(desc="train dataset map pre-processing"):
			SCREAMING_SNAKE_CASE      :					Optional[Any]    =				train_dataset.map(
			    __lowerCAmelCase							,	batched=__lowerCAmelCase							,	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(__lowerCAmelCase))							,	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[str]    =				min(len(__lowerCAmelCase)							,	data_args.max_eval_samples)
			SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				eval_dataset.select(range(__lowerCAmelCase))
		with training_args.main_process_first(desc="validation dataset map pre-processing"):
			SCREAMING_SNAKE_CASE      :					Tuple    =				eval_dataset.map(
			    __lowerCAmelCase							,	batched=__lowerCAmelCase							,	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      :					int    =				min(len(__lowerCAmelCase)							,	data_args.max_predict_samples)
			SCREAMING_SNAKE_CASE      :					Dict    =				predict_dataset.select(range(__lowerCAmelCase))
		with training_args.main_process_first(desc="prediction dataset map pre-processing"):
			SCREAMING_SNAKE_CASE      :					Tuple    =				predict_dataset.map(
			    __lowerCAmelCase							,	batched=__lowerCAmelCase							,	load_from_cache_file=not data_args.overwrite_cache							,	desc="Running tokenizer on prediction dataset"							,	)

    # Get the metric function
	SCREAMING_SNAKE_CASE      :					Dict    =				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(_a):
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				p.predictions[0] if isinstance(p.predictions							,	__lowerCAmelCase) else p.predictions
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				np.argmax(__lowerCAmelCase							,	axis=1)
		return metric.compute(predictions=__lowerCAmelCase							,	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      :					Union[str, Any]    =				default_data_collator
	elif training_args.fpaa:
		SCREAMING_SNAKE_CASE      :					List[Any]    =				DataCollatorWithPadding(__lowerCAmelCase							,	pad_to_multiple_of=8)
	else:
		SCREAMING_SNAKE_CASE      :					Tuple    =				None

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

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

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

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

	# Evaluation
	if training_args.do_eval:
		logger.info("*** Evaluate ***")
		SCREAMING_SNAKE_CASE      :					Any    =				trainer.evaluate(eval_dataset=__lowerCAmelCase)

		SCREAMING_SNAKE_CASE      :					Tuple    =				data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase)
		SCREAMING_SNAKE_CASE      :					List[str]    =				min(__lowerCAmelCase							,	len(__lowerCAmelCase))

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

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

		SCREAMING_SNAKE_CASE      :					List[Any]    =				(
		    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase)
		)
		SCREAMING_SNAKE_CASE      :					int    =				min(__lowerCAmelCase							,	len(__lowerCAmelCase))

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

		SCREAMING_SNAKE_CASE      :					Optional[int]    =				np.argmax(__lowerCAmelCase							,	axis=1)
		SCREAMING_SNAKE_CASE      :					Dict    =				os.path.join(training_args.output_dir							,	"predictions.txt")
		if trainer.is_world_process_zero():
			with open(__lowerCAmelCase							,	"w") as writer:
				writer.write("index\tprediction\n")
				for index, item in enumerate(__lowerCAmelCase):
					SCREAMING_SNAKE_CASE      :					Tuple    =				label_list[item]
					writer.write(f"{index}\t{item}\n")


if __name__ == "__main__":
			main()



'''simple docstring'''


from collections import deque
from math import floor
from random import random
from time import time






class 		lowerCAmelCase__	:




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



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					{}




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   List[str]		,     lowerCamelCase__     :   int=1       )      ->Optional[Any]:



					'''simple docstring'''







					if self.graph.get(lowerCamelCase__       ):
							if self.graph[u].count([w, v]       ) == 0:
									self.graph[u].append([w, v]       )
					else:
							_UpperCAmelCase						:				Dict	    =					[[w, v]]
					if not self.graph.get(lowerCamelCase__       ):
							_UpperCAmelCase						:				str	    =					[]




			def 							lowerCAmelCase__			(						self     :   Any       )      ->Tuple:



					'''simple docstring'''







					return list(self.graph       )




			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   Union[str, Any]		,     lowerCamelCase__     :   str       )      ->Any:



					'''simple docstring'''







					if self.graph.get(lowerCamelCase__       ):
							for _ in self.graph[u]:
									if _[1] == v:
											self.graph[u].remove(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Optional[Any]=-2		,     lowerCamelCase__     :   Optional[int]=-1       )      ->int:



					'''simple docstring'''







					if s == d:
							return []
					_UpperCAmelCase						:				Tuple	    =					[]
					_UpperCAmelCase						:				Optional[int]	    =					[]
					if s == -2:
							_UpperCAmelCase						:				Union[str, Any]	    =					list(self.graph       )[0]
					stack.append(lowerCamelCase__       )
					visited.append(lowerCamelCase__       )
					_UpperCAmelCase						:				Optional[Any]	    =					s

					while True:
							# check if there is any non isolated nodes
							if len(self.graph[s]       ) != 0:
									_UpperCAmelCase						:				str	    =					s
									for node in self.graph[s]:
											if visited.count(node[1]       ) < 1:
													if node[1] == d:
															visited.append(lowerCamelCase__       )
															return visited
													else:
															stack.append(node[1]       )
															visited.append(node[1]       )
															_UpperCAmelCase						:				Union[str, Any]	    =					node[1]
															break

            # check if all the children are visited
							if s == ss:
									stack.pop()
									if len(lowerCamelCase__       ) != 0:
											_UpperCAmelCase						:				Tuple	    =					stack[len(lowerCamelCase__       ) - 1]
							else:
									_UpperCAmelCase						:				Optional[int]	    =					ss

							# check if se have reached the starting point
							if len(lowerCamelCase__       ) == 0:
									return visited




			def 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   Any=-1       )      ->str:



					'''simple docstring'''







					if c == -1:
							_UpperCAmelCase						:				str	    =					floor(random() * 1_00_00       ) + 10
					for i in range(lowerCamelCase__       ):
							# every vertex has max 100 edges
							for _ in range(floor(random() * 1_02       ) + 1       ):
									_UpperCAmelCase						:				Tuple	    =					floor(random() * c       ) + 1
									if n != i:
											self.add_pair(lowerCamelCase__		,     lowerCamelCase__		,     1       )




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   Union[str, Any]=-2       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					deque()
					_UpperCAmelCase						:				int	    =					[]
					if s == -2:
							_UpperCAmelCase						:				str	    =					list(self.graph       )[0]
					d.append(lowerCamelCase__       )
					visited.append(lowerCamelCase__       )
					while d:
							_UpperCAmelCase						:				int	    =					d.popleft()
							if len(self.graph[s]       ) != 0:
									for node in self.graph[s]:
											if visited.count(node[1]       ) < 1:
													d.append(node[1]       )
													visited.append(node[1]       )
					return visited




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Union[str, Any]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					0
					for x in self.graph:
							for y in self.graph[x]:
									if y[1] == u:
											count += 1
					return count




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Union[str, Any]       )      ->int:



					'''simple docstring'''







					return len(self.graph[u]       )




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Any=-2       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					[]
					_UpperCAmelCase						:				List[str]	    =					[]
					if s == -2:
							_UpperCAmelCase						:				str	    =					list(self.graph       )[0]
					stack.append(lowerCamelCase__       )
					visited.append(lowerCamelCase__       )
					_UpperCAmelCase						:				List[Any]	    =					s
					_UpperCAmelCase						:				Tuple	    =					[]

					while True:
							# check if there is any non isolated nodes
							if len(self.graph[s]       ) != 0:
									_UpperCAmelCase						:				str	    =					s
									for node in self.graph[s]:
											if visited.count(node[1]       ) < 1:
													stack.append(node[1]       )
													visited.append(node[1]       )
													_UpperCAmelCase						:				List[Any]	    =					node[1]
													break

            # check if all the children are visited
							if s == ss:
									sorted_nodes.append(stack.pop()       )
									if len(lowerCamelCase__       ) != 0:
											_UpperCAmelCase						:				Dict	    =					stack[len(lowerCamelCase__       ) - 1]
							else:
									_UpperCAmelCase						:				List[str]	    =					ss

							# check if se have reached the starting point
							if len(lowerCamelCase__       ) == 0:
									return sorted_nodes




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



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					[]
					_UpperCAmelCase						:				str	    =					[]
					_UpperCAmelCase						:				int	    =					list(self.graph       )[0]
					stack.append(lowerCamelCase__       )
					visited.append(lowerCamelCase__       )
					_UpperCAmelCase						:				str	    =					-2
					_UpperCAmelCase						:				Optional[int]	    =					[]
					_UpperCAmelCase						:				Union[str, Any]	    =					s
					_UpperCAmelCase						:				Tuple	    =					False
					_UpperCAmelCase						:				List[str]	    =					set()

					while True:
							# check if there is any non isolated nodes
							if len(self.graph[s]       ) != 0:
									_UpperCAmelCase						:				Optional[Any]	    =					s
									for node in self.graph[s]:
											if (
											    visited.count(node[1]       ) > 0
											    and node[1] != parent
											    and indirect_parents.count(node[1]       ) > 0
											    and not on_the_way_back
											):
													_UpperCAmelCase						:				Optional[int]	    =					len(lowerCamelCase__       ) - 1
													while len_stack >= 0:
															if stack[len_stack] == node[1]:
																	anticipating_nodes.add(node[1]       )
																	break
															else:
																	anticipating_nodes.add(stack[len_stack]       )
																	len_stack -= 1
											if visited.count(node[1]       ) < 1:
													stack.append(node[1]       )
													visited.append(node[1]       )
													_UpperCAmelCase						:				Dict	    =					node[1]
													break

            # check if all the children are visited
							if s == ss:
									stack.pop()
									_UpperCAmelCase						:				List[str]	    =					True
									if len(lowerCamelCase__       ) != 0:
											_UpperCAmelCase						:				Tuple	    =					stack[len(lowerCamelCase__       ) - 1]
							else:
									_UpperCAmelCase						:				Optional[Any]	    =					False
									indirect_parents.append(lowerCamelCase__       )
									_UpperCAmelCase						:				Any	    =					s
									_UpperCAmelCase						:				Dict	    =					ss

							# check if se have reached the starting point
							if len(lowerCamelCase__       ) == 0:
									return list(lowerCamelCase__       )




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



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					[]
					_UpperCAmelCase						:				Optional[Any]	    =					[]
					_UpperCAmelCase						:				str	    =					list(self.graph       )[0]
					stack.append(lowerCamelCase__       )
					visited.append(lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					-2
					_UpperCAmelCase						:				Optional[Any]	    =					[]
					_UpperCAmelCase						:				Tuple	    =					s
					_UpperCAmelCase						:				List[str]	    =					False
					_UpperCAmelCase						:				Optional[Any]	    =					set()

					while True:
							# check if there is any non isolated nodes
							if len(self.graph[s]       ) != 0:
									_UpperCAmelCase						:				Optional[int]	    =					s
									for node in self.graph[s]:
											if (
											    visited.count(node[1]       ) > 0
											    and node[1] != parent
											    and indirect_parents.count(node[1]       ) > 0
											    and not on_the_way_back
											):
													_UpperCAmelCase						:				Optional[int]	    =					len(lowerCamelCase__       ) - 1
													while len_stack_minus_one >= 0:
															if stack[len_stack_minus_one] == node[1]:
																	anticipating_nodes.add(node[1]       )
																	break
															else:
																	return True
											if visited.count(node[1]       ) < 1:
													stack.append(node[1]       )
													visited.append(node[1]       )
													_UpperCAmelCase						:				Tuple	    =					node[1]
													break

            # check if all the children are visited
							if s == ss:
									stack.pop()
									_UpperCAmelCase						:				Dict	    =					True
									if len(lowerCamelCase__       ) != 0:
											_UpperCAmelCase						:				List[str]	    =					stack[len(lowerCamelCase__       ) - 1]
							else:
									_UpperCAmelCase						:				Union[str, Any]	    =					False
									indirect_parents.append(lowerCamelCase__       )
									_UpperCAmelCase						:				Optional[Any]	    =					s
									_UpperCAmelCase						:				Optional[Any]	    =					ss

							# check if se have reached the starting point
							if len(lowerCamelCase__       ) == 0:
									return False




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   Tuple=-2		,     lowerCamelCase__     :   Union[str, Any]=-1       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					time()
					self.dfs(lowerCamelCase__		,     lowerCamelCase__       )
					_UpperCAmelCase						:				Tuple	    =					time()
					return end - begin






			def 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   Dict=-2       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					time()
					self.bfs(lowerCamelCase__       )
					_UpperCAmelCase						:				Tuple	    =					time()
					return end - begin






class 		lowerCAmelCase__	:




			def __init__(						self     :   Tuple       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					{}




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   List[str]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Tuple=1       )      ->int:



					'''simple docstring'''







					if self.graph.get(lowerCamelCase__       ):
							# if there already is a edge
							if self.graph[u].count([w, v]       ) == 0:
									self.graph[u].append([w, v]       )
					else:
							# if u does not exist
							_UpperCAmelCase						:				Dict	    =					[[w, v]]
					# add the other way
					if self.graph.get(lowerCamelCase__       ):
							# if there already is a edge
							if self.graph[v].count([w, u]       ) == 0:
									self.graph[v].append([w, u]       )
					else:
							# if u does not exist
							_UpperCAmelCase						:				Tuple	    =					[[w, u]]




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Any		,     lowerCamelCase__     :   Optional[Any]       )      ->Dict:



					'''simple docstring'''







					if self.graph.get(lowerCamelCase__       ):
							for _ in self.graph[u]:
									if _[1] == v:
											self.graph[u].remove(lowerCamelCase__       )
        # the other way round
					if self.graph.get(lowerCamelCase__       ):
							for _ in self.graph[v]:
									if _[1] == u:
											self.graph[v].remove(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   List[str]=-2		,     lowerCamelCase__     :   List[str]=-1       )      ->List[Any]:



					'''simple docstring'''







					if s == d:
							return []
					_UpperCAmelCase						:				List[Any]	    =					[]
					_UpperCAmelCase						:				List[Any]	    =					[]
					if s == -2:
							_UpperCAmelCase						:				List[Any]	    =					list(self.graph       )[0]
					stack.append(lowerCamelCase__       )
					visited.append(lowerCamelCase__       )
					_UpperCAmelCase						:				List[Any]	    =					s

					while True:
							# check if there is any non isolated nodes
							if len(self.graph[s]       ) != 0:
									_UpperCAmelCase						:				Optional[Any]	    =					s
									for node in self.graph[s]:
											if visited.count(node[1]       ) < 1:
													if node[1] == d:
															visited.append(lowerCamelCase__       )
															return visited
													else:
															stack.append(node[1]       )
															visited.append(node[1]       )
															_UpperCAmelCase						:				Optional[int]	    =					node[1]
															break

            # check if all the children are visited
							if s == ss:
									stack.pop()
									if len(lowerCamelCase__       ) != 0:
											_UpperCAmelCase						:				Optional[int]	    =					stack[len(lowerCamelCase__       ) - 1]
							else:
									_UpperCAmelCase						:				Union[str, Any]	    =					ss

							# check if se have reached the starting point
							if len(lowerCamelCase__       ) == 0:
									return visited




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   Dict=-1       )      ->str:



					'''simple docstring'''







					if c == -1:
							_UpperCAmelCase						:				str	    =					floor(random() * 1_00_00       ) + 10
					for i in range(lowerCamelCase__       ):
							# every vertex has max 100 edges
							for _ in range(floor(random() * 1_02       ) + 1       ):
									_UpperCAmelCase						:				str	    =					floor(random() * c       ) + 1
									if n != i:
											self.add_pair(lowerCamelCase__		,     lowerCamelCase__		,     1       )




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   Union[str, Any]=-2       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					deque()
					_UpperCAmelCase						:				Optional[int]	    =					[]
					if s == -2:
							_UpperCAmelCase						:				Union[str, Any]	    =					list(self.graph       )[0]
					d.append(lowerCamelCase__       )
					visited.append(lowerCamelCase__       )
					while d:
							_UpperCAmelCase						:				Any	    =					d.popleft()
							if len(self.graph[s]       ) != 0:
									for node in self.graph[s]:
											if visited.count(node[1]       ) < 1:
													d.append(node[1]       )
													visited.append(node[1]       )
					return visited




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Dict       )      ->Optional[Any]:



					'''simple docstring'''







					return len(self.graph[u]       )




			def 							lowerCAmelCase__			(						self     :   str       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					[]
					_UpperCAmelCase						:				str	    =					[]
					_UpperCAmelCase						:				Optional[int]	    =					list(self.graph       )[0]
					stack.append(lowerCamelCase__       )
					visited.append(lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					-2
					_UpperCAmelCase						:				Union[str, Any]	    =					[]
					_UpperCAmelCase						:				str	    =					s
					_UpperCAmelCase						:				int	    =					False
					_UpperCAmelCase						:				List[Any]	    =					set()

					while True:
							# check if there is any non isolated nodes
							if len(self.graph[s]       ) != 0:
									_UpperCAmelCase						:				int	    =					s
									for node in self.graph[s]:
											if (
											    visited.count(node[1]       ) > 0
											    and node[1] != parent
											    and indirect_parents.count(node[1]       ) > 0
											    and not on_the_way_back
											):
													_UpperCAmelCase						:				Union[str, Any]	    =					len(lowerCamelCase__       ) - 1
													while len_stack >= 0:
															if stack[len_stack] == node[1]:
																	anticipating_nodes.add(node[1]       )
																	break
															else:
																	anticipating_nodes.add(stack[len_stack]       )
																	len_stack -= 1
											if visited.count(node[1]       ) < 1:
													stack.append(node[1]       )
													visited.append(node[1]       )
													_UpperCAmelCase						:				Union[str, Any]	    =					node[1]
													break

            # check if all the children are visited
							if s == ss:
									stack.pop()
									_UpperCAmelCase						:				List[Any]	    =					True
									if len(lowerCamelCase__       ) != 0:
											_UpperCAmelCase						:				List[str]	    =					stack[len(lowerCamelCase__       ) - 1]
							else:
									_UpperCAmelCase						:				Dict	    =					False
									indirect_parents.append(lowerCamelCase__       )
									_UpperCAmelCase						:				Dict	    =					s
									_UpperCAmelCase						:				Dict	    =					ss

							# check if se have reached the starting point
							if len(lowerCamelCase__       ) == 0:
									return list(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					[]
					_UpperCAmelCase						:				List[str]	    =					[]
					_UpperCAmelCase						:				Tuple	    =					list(self.graph       )[0]
					stack.append(lowerCamelCase__       )
					visited.append(lowerCamelCase__       )
					_UpperCAmelCase						:				Dict	    =					-2
					_UpperCAmelCase						:				Union[str, Any]	    =					[]
					_UpperCAmelCase						:				Dict	    =					s
					_UpperCAmelCase						:				Dict	    =					False
					_UpperCAmelCase						:				str	    =					set()

					while True:
							# check if there is any non isolated nodes
							if len(self.graph[s]       ) != 0:
									_UpperCAmelCase						:				Union[str, Any]	    =					s
									for node in self.graph[s]:
											if (
											    visited.count(node[1]       ) > 0
											    and node[1] != parent
											    and indirect_parents.count(node[1]       ) > 0
											    and not on_the_way_back
											):
													_UpperCAmelCase						:				int	    =					len(lowerCamelCase__       ) - 1
													while len_stack_minus_one >= 0:
															if stack[len_stack_minus_one] == node[1]:
																	anticipating_nodes.add(node[1]       )
																	break
															else:
																	return True
											if visited.count(node[1]       ) < 1:
													stack.append(node[1]       )
													visited.append(node[1]       )
													_UpperCAmelCase						:				Union[str, Any]	    =					node[1]
													break

            # check if all the children are visited
							if s == ss:
									stack.pop()
									_UpperCAmelCase						:				List[Any]	    =					True
									if len(lowerCamelCase__       ) != 0:
											_UpperCAmelCase						:				List[Any]	    =					stack[len(lowerCamelCase__       ) - 1]
							else:
									_UpperCAmelCase						:				Optional[Any]	    =					False
									indirect_parents.append(lowerCamelCase__       )
									_UpperCAmelCase						:				List[str]	    =					s
									_UpperCAmelCase						:				List[Any]	    =					ss

							# check if se have reached the starting point
							if len(lowerCamelCase__       ) == 0:
									return False




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->int:



					'''simple docstring'''







					return list(self.graph       )




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Dict=-2		,     lowerCamelCase__     :   Dict=-1       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					time()
					self.dfs(lowerCamelCase__		,     lowerCamelCase__       )
					_UpperCAmelCase						:				int	    =					time()
					return end - begin






			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Any=-2       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					time()
					self.bfs(lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					time()
					return end - begin



