



'''simple docstring'''

import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

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


UpperCAmelCase     		=   logging.getLogger(__name__)

@dataclass
class __snake_case:


						'''simple docstring'''



						UpperCAmelCase							:   str									=		field(
						    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
						UpperCAmelCase							:   Optional[str]									=		field(
						    default=_lowerCAmelCase		,							metadata={"help": "Pretrained config name or path if not the same as model_name"} )
						UpperCAmelCase							:   Optional[str]									=		field(
						    default="NER"		,							metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
						UpperCAmelCase							:   Optional[str]									=		field(
						    default=_lowerCAmelCase		,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
						UpperCAmelCase							:   bool									=		field(default=_lowerCAmelCase		,							metadata={"help": "Set this flag to use fast tokenization."} )
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						UpperCAmelCase							:   Optional[str]									=		field(
						    default=_lowerCAmelCase		,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}		,							)







@dataclass
class __snake_case:


						'''simple docstring'''



						UpperCAmelCase							:   str									=		field(
						    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
						UpperCAmelCase							:   Optional[str]									=		field(
						    default=_lowerCAmelCase		,							metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}		,							)
						UpperCAmelCase							:   int									=		field(
						    default=1_28		,							metadata={
						        "help": (
						            "The maximum total input sequence length after tokenization. Sequences longer "
						            "than this will be truncated, sequences shorter will be padded."
						        )
						    }		,							)
						UpperCAmelCase							:   bool									=		field(
						    default=_lowerCAmelCase		,							metadata={"help": "Overwrite the cached training and evaluation sets"} )







def 		_snake_case		( )      ->						Dict:







	"""simple docstring"""
	# 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.

	lowerCAmelCase   =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
	if len(sys.argv							) == 2 and sys.argv[1].endswith(""".json"""							):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
	else:
		lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     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."""							)

	lowerCAmelCase   =     import_module("""tasks"""							)
	try:
		lowerCAmelCase   =     getattr(_SCREAMING_SNAKE_CASE							,		model_args.task_type							)
		lowerCAmelCase   =     token_classification_task_clazz()
	except AttributeError:
		raise ValueError(
		    f'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
		    f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'							)

	# 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"""							,		_SCREAMING_SNAKE_CASE							)

	# Set seed
	set_seed(training_args.seed							)

	# Prepare CONLL-2003 task
	lowerCAmelCase   =     token_classification_task.get_labels(data_args.labels							)
	lowerCAmelCase   =     dict(enumerate(_SCREAMING_SNAKE_CASE							)							)
	lowerCAmelCase   =     len(_SCREAMING_SNAKE_CASE							)

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

	lowerCAmelCase   =     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=_SCREAMING_SNAKE_CASE							,		labelaid={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE							)}							,		cache_dir=model_args.cache_dir							,		)
	lowerCAmelCase   =     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							,		use_fast=model_args.use_fast							,		)
	lowerCAmelCase   =     AutoModelForTokenClassification.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							,		)

	# Get datasets
	lowerCAmelCase   =     (
	    TokenClassificationDataset(
	        token_classification_task=_SCREAMING_SNAKE_CASE							,		data_dir=data_args.data_dir							,		tokenizer=_SCREAMING_SNAKE_CASE							,		labels=_SCREAMING_SNAKE_CASE							,		model_type=config.model_type							,		max_seq_length=data_args.max_seq_length							,		overwrite_cache=data_args.overwrite_cache							,		mode=Split.train							,		)
	    if training_args.do_train
	    else None
	)
	lowerCAmelCase   =     (
	    TokenClassificationDataset(
	        token_classification_task=_SCREAMING_SNAKE_CASE							,		data_dir=data_args.data_dir							,		tokenizer=_SCREAMING_SNAKE_CASE							,		labels=_SCREAMING_SNAKE_CASE							,		model_type=config.model_type							,		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 align_predictions(_SCREAMING_SNAKE_CASE					:						np.ndarray							,		_SCREAMING_SNAKE_CASE					:						np.ndarray							) -> Tuple[List[int], List[int]]:
		lowerCAmelCase   =     np.argmax(_SCREAMING_SNAKE_CASE							,		axis=2							)

		lowerCAmelCase,       lowerCAmelCase   =     preds.shape

		lowerCAmelCase   =     [[] for _ in range(_SCREAMING_SNAKE_CASE							)]
		lowerCAmelCase   =     [[] for _ in range(_SCREAMING_SNAKE_CASE							)]

		for i in range(_SCREAMING_SNAKE_CASE							):
			for j in range(_SCREAMING_SNAKE_CASE							):
				if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
					out_label_list[i].append(label_map[label_ids[i][j]]							)
					preds_list[i].append(label_map[preds[i][j]]							)

		return preds_list, out_label_list

	def compute_metrics(_SCREAMING_SNAKE_CASE					:						EvalPrediction							) -> Dict:
		lowerCAmelCase,       lowerCAmelCase   =     align_predictions(p.predictions							,		p.label_ids							)
		return {
		    "accuracy_score": accuracy_score(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							),
		    "precision": precision_score(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							),
		    "recall": recall_score(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							),
		    "f1": fa_score(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							),
		}

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

	# Initialize our Trainer
	lowerCAmelCase   =     Trainer(
	    model=_SCREAMING_SNAKE_CASE							,		args=_SCREAMING_SNAKE_CASE							,		train_dataset=_SCREAMING_SNAKE_CASE							,		eval_dataset=_SCREAMING_SNAKE_CASE							,		compute_metrics=_SCREAMING_SNAKE_CASE							,		data_collator=_SCREAMING_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_process_zero():
			tokenizer.save_pretrained(training_args.output_dir							)

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

		lowerCAmelCase   =     trainer.evaluate()

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

			results.update(_SCREAMING_SNAKE_CASE							)

    # Predict
	if training_args.do_predict:
		lowerCAmelCase   =     TokenClassificationDataset(
		    token_classification_task=_SCREAMING_SNAKE_CASE							,		data_dir=data_args.data_dir							,		tokenizer=_SCREAMING_SNAKE_CASE							,		labels=_SCREAMING_SNAKE_CASE							,		model_type=config.model_type							,		max_seq_length=data_args.max_seq_length							,		overwrite_cache=data_args.overwrite_cache							,		mode=Split.test							,		)

		lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     trainer.predict(_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase,       lowerCAmelCase   =     align_predictions(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

		lowerCAmelCase   =     os.path.join(training_args.output_dir							,		"""test_results.txt"""							)
		if trainer.is_world_process_zero():
			with open(_SCREAMING_SNAKE_CASE							,		"""w"""							) as writer:
				for key, value in metrics.items():
					logger.info("""  %s = %s"""							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
					writer.write("""%s = %s\n""" % (key, value)							)

        # Save predictions
		lowerCAmelCase   =     os.path.join(training_args.output_dir							,		"""test_predictions.txt"""							)
		if trainer.is_world_process_zero():
			with open(_SCREAMING_SNAKE_CASE							,		"""w"""							) as writer:
				with open(os.path.join(data_args.data_dir							,		"""test.txt"""							)							,		"""r"""							) as f:
					token_classification_task.write_predictions_to_file(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	return results






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						Any:







	"""simple docstring"""
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
				main()




'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list							)      ->						list:







	"""simple docstring"""

	if len(_SCREAMING_SNAKE_CASE							) < 2:
		return collection

	def circle_sort_util(_SCREAMING_SNAKE_CASE					:						list							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						int							) -> bool:
		lowerCAmelCase   =     False

		if low == high:
			return swapped

		lowerCAmelCase   =     low
		lowerCAmelCase   =     high

		while left < right:
			if collection[left] > collection[right]:
				lowerCAmelCase,       lowerCAmelCase   =     (
				    collection[right],
				    collection[left],
				)
				lowerCAmelCase   =     True

			left += 1
			right -= 1

		if left == right and collection[left] > collection[right + 1]:
			lowerCAmelCase,       lowerCAmelCase   =     (
			    collection[right + 1],
			    collection[left],
			)

			lowerCAmelCase   =     True

		lowerCAmelCase   =     low + int((high - low) / 2							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		mid + 1							,		_SCREAMING_SNAKE_CASE							)

		return swapped or left_swap or right_swap

	lowerCAmelCase   =     True

	while is_not_sorted is True:
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		0							,		len(_SCREAMING_SNAKE_CASE							) - 1							)

	return collection


if __name__ == "__main__":
				UpperCAmelCase     		=   input('Enter numbers separated by a comma:\n').strip()
				UpperCAmelCase     		=   [int(item) for item in user_input.split(',')]
				print(circle_sort(unsorted))