

import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


lowerCAmelCase_	     =     logging.getLogger(__name__)





@dataclass
class      snake_case_       :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       str						      =    field(
			    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
			SCREAMING_SNAKE_CASE					:       Optional[str]						      =    field(
			    default=__A     ,	metadata={"help": "Pretrained config name or path if not the same as model_name"} )
			SCREAMING_SNAKE_CASE					:       Optional[str]						      =    field(
			    default="NER"     ,	metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
			SCREAMING_SNAKE_CASE					:       Optional[str]						      =    field(
			    default=__A     ,	metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
			SCREAMING_SNAKE_CASE					:       bool						      =    field(default=__A     ,	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.
			SCREAMING_SNAKE_CASE					:       Optional[str]						      =    field(
			    default=__A     ,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}     ,	)






@dataclass
class      snake_case_       :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       str						      =    field(
			    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
			SCREAMING_SNAKE_CASE					:       Optional[str]						      =    field(
			    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} )
			SCREAMING_SNAKE_CASE					:       int						      =    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."
			        )
			    }     ,	)
			SCREAMING_SNAKE_CASE					:       bool						      =    field(
			    default=__A     ,	metadata={"help": "Overwrite the cached training and evaluation sets"} )






def   __SCREAMING_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.
						snake_case_       =     HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
						snake_case_,   snake_case_,   snake_case_       =     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.'''	)

						snake_case_       =     import_module('''tasks'''	)

						try:
												snake_case_       =     getattr(SCREAMING_SNAKE_CASE__  ,		model_args.task_type	)
												snake_case_       =     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  ,		)
						logger.info(
						    '''n_replicas: %s, distributed training: %s, 16-bits training: %s'''  ,		training_args.n_replicas  ,		bool(training_args.n_replicas > 1	)  ,		training_args.fpaa  ,		)
						logger.info('''Training/evaluation parameters %s'''  ,		SCREAMING_SNAKE_CASE__	)

						# Prepare Token Classification task
						snake_case_       =     token_classification_task.get_labels(data_args.labels	)
						snake_case_       =     dict(enumerate(SCREAMING_SNAKE_CASE__	)	)
						snake_case_       =     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.

						snake_case_       =     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  ,		)
						snake_case_       =     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  ,		)

						with training_args.strategy.scope():
												snake_case_       =     TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path  ,		from_pt=bool('''.bin''' in model_args.model_name_or_path	)  ,		config=SCREAMING_SNAKE_CASE__  ,		cache_dir=model_args.cache_dir  ,		)

						# Get datasets
						snake_case_       =     (
						    TFTokenClassificationDataset(
						        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
						)
						snake_case_       =     (
						    TFTokenClassificationDataset(
						        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__  ,		SCREAMING_SNAKE_CASE__	) -> Tuple[List[int], List[int]]:
												snake_case_       =     np.argmax(SCREAMING_SNAKE_CASE__  ,		axis=2	)
												snake_case_,   snake_case_       =     preds.shape
												snake_case_       =     [[] for _ in range(SCREAMING_SNAKE_CASE__	)]
												snake_case_       =     [[] 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] != -100:
																														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__	) -> Dict:
												snake_case_,   snake_case_       =     align_predictions(p.predictions  ,		p.label_ids	)

												return {
												    "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__	),
												}

						# Initialize our Trainer
						snake_case_       =     TFTrainer(
						    model=SCREAMING_SNAKE_CASE__  ,		args=SCREAMING_SNAKE_CASE__  ,		train_dataset=train_dataset.get_dataset() if train_dataset else None  ,		eval_dataset=eval_dataset.get_dataset() if eval_dataset else None  ,		compute_metrics=SCREAMING_SNAKE_CASE__  ,		)

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir	)

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

												snake_case_       =     trainer.evaluate()
												snake_case_       =     os.path.join(training_args.output_dir  ,		'''eval_results.txt'''	)

												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:
												snake_case_       =     TFTokenClassificationDataset(
												    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  ,		)

												snake_case_,   snake_case_,   snake_case_       =     trainer.predict(test_dataset.get_dataset()	)
												snake_case_,   snake_case_       =     align_predictions(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)
												snake_case_       =     classification_report(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)

												logger.info('''\n%s'''  ,		SCREAMING_SNAKE_CASE__	)

												snake_case_       =     os.path.join(training_args.output_dir  ,		'''test_results.txt'''	)

												with open(SCREAMING_SNAKE_CASE__  ,		'''w'''	) as writer:
																		writer.write('''%s\n''' % report	)

												# Save predictions
												snake_case_       =     os.path.join(training_args.output_dir  ,		'''test_predictions.txt'''	)

												with open(SCREAMING_SNAKE_CASE__  ,		'''w'''	) as writer:
																		with open(os.path.join(data_args.data_dir  ,		'''test.txt'''	)  ,		'''r'''	) as f:
																								snake_case_       =     0

																								for line in f:
																														if line.startswith('''-DOCSTART-'''	) or line == "" or line == "\n":
																																				writer.write(SCREAMING_SNAKE_CASE__	)

																																				if not preds_list[example_id]:
																																										example_id += 1
																														elif preds_list[example_id]:
																																				snake_case_       =     line.split()[0] + ''' ''' + preds_list[example_id].pop(0	) + '''\n'''

																																				writer.write(SCREAMING_SNAKE_CASE__	)
																														else:
																																				logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''  ,		line.split()[0]	)

						return results


if __name__ == "__main__":
					main()


from __future__ import annotations

from random import randrange


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						if len(SCREAMING_SNAKE_CASE__	) < 2:
												return collection
						snake_case_       =     randrange(len(SCREAMING_SNAKE_CASE__	)	)  # Use random element as pivot
						snake_case_       =     collection[pivot_index]
						snake_case_       =     []  # All elements greater than pivot
						snake_case_       =     []  # All elements less than or equal to pivot

						for element in collection[:pivot_index]:
												(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE__	)

						for element in collection[pivot_index + 1 :]:
												(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE__	)

						return [*quick_sort(SCREAMING_SNAKE_CASE__	), pivot, *quick_sort(SCREAMING_SNAKE_CASE__	)]


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