'''simple docstring'''

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()


A_        =   logging.getLogger(__name__)




@dataclass
class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






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







@dataclass
class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






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







def 					UpperCAmelCase__      (							)						->		Optional[int]:



						"""simple docstring"""

						snake_case   :					List[str]							=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
						snake_case							,				snake_case							,				snake_case   :					List[Any]							=						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   :					Any							=						import_module("tasks")

						try:
												snake_case   :					Any							=						getattr(_lowerCAmelCase,			model_args.task_type)
												snake_case   :					TokenClassificationTask							=						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",			_lowerCAmelCase)

						# Prepare Token Classification task
						snake_case   :					str							=						token_classification_task.get_labels(data_args.labels)
						snake_case   :					Dict[int, str]							=						dict(enumerate(_lowerCAmelCase))
						snake_case   :					str							=						len(_lowerCAmelCase)

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

						snake_case   :					Tuple							=						AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path,			num_labels=_lowerCAmelCase,			idalabel=_lowerCAmelCase,			labelaid={label: i for i, label in enumerate(_lowerCAmelCase)},			cache_dir=model_args.cache_dir,			)
						snake_case   :					Tuple							=						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   :					Dict							=						TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path,			from_pt=bool(".bin" in model_args.model_name_or_path),			config=_lowerCAmelCase,			cache_dir=model_args.cache_dir,			)

						# Get datasets
						snake_case   :					Optional[Any]							=						(
						    TFTokenClassificationDataset(
						        token_classification_task=_lowerCAmelCase,			data_dir=data_args.data_dir,			tokenizer=_lowerCAmelCase,			labels=_lowerCAmelCase,			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   :					Optional[Any]							=						(
						    TFTokenClassificationDataset(
						        token_classification_task=_lowerCAmelCase,			data_dir=data_args.data_dir,			tokenizer=_lowerCAmelCase,			labels=_lowerCAmelCase,			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(_lowerCAmelCase						: np.ndarray,			_lowerCAmelCase						: np.ndarray) -> Tuple[List[int], List[int]]:
												snake_case   :					Tuple							=						np.argmax(_lowerCAmelCase,			axis=2)
												snake_case							,				snake_case   :					Any							=						preds.shape
												snake_case   :					Optional[int]							=						[[] for _ in range(_lowerCAmelCase)]
												snake_case   :					List[str]							=						[[] for _ in range(_lowerCAmelCase)]

												for i in range(_lowerCAmelCase):
																		for j in range(_lowerCAmelCase):
																								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(_lowerCAmelCase						: EvalPrediction) -> Dict:
												snake_case							,				snake_case   :					Optional[Any]							=						align_predictions(p.predictions,			p.label_ids)

												return {
												    "precision": precision_score(_lowerCAmelCase,			_lowerCAmelCase),
												    "recall": recall_score(_lowerCAmelCase,			_lowerCAmelCase),
												    "f1": fa_score(_lowerCAmelCase,			_lowerCAmelCase),
												}

						# Initialize our Trainer
						snake_case   :					List[Any]							=						TFTrainer(
						    model=_lowerCAmelCase,			args=_lowerCAmelCase,			train_dataset=train_dataset.get_dataset() if train_dataset else None,			eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,			compute_metrics=_lowerCAmelCase,			)

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

						# Evaluation
						snake_case   :					Optional[int]							=						{}
						if training_args.do_eval:
												logger.info("*** Evaluate ***")

												snake_case   :					List[Any]							=						trainer.evaluate()
												snake_case   :					Optional[int]							=						os.path.join(training_args.output_dir,			"eval_results.txt")

												with open(_lowerCAmelCase,			"w") as writer:
																		logger.info("***** Eval results *****")

																		for key, value in result.items():
																								logger.info("  %s = %s",			_lowerCAmelCase,			_lowerCAmelCase)
																								writer.write("%s = %s\n" % (key, value))

																		results.update(_lowerCAmelCase)

    # Predict
						if training_args.do_predict:
												snake_case   :					Optional[int]							=						TFTokenClassificationDataset(
												    token_classification_task=_lowerCAmelCase,			data_dir=data_args.data_dir,			tokenizer=_lowerCAmelCase,			labels=_lowerCAmelCase,			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   :					List[Any]							=						trainer.predict(test_dataset.get_dataset())
												snake_case							,				snake_case   :					Union[str, Any]							=						align_predictions(_lowerCAmelCase,			_lowerCAmelCase)
												snake_case   :					Optional[int]							=						classification_report(_lowerCAmelCase,			_lowerCAmelCase)

												logger.info("\n%s",			_lowerCAmelCase)

												snake_case   :					Optional[int]							=						os.path.join(training_args.output_dir,			"test_results.txt")

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

												# Save predictions
												snake_case   :					Optional[int]							=						os.path.join(training_args.output_dir,			"test_predictions.txt")

												with open(_lowerCAmelCase,			"w") as writer:
																		with open(os.path.join(data_args.data_dir,			"test.txt"),			"r") as f:
																								snake_case   :					int							=						0

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

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

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

						return results


if __name__ == "__main__":
						main()






'''simple docstring'''

import math


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5,			int(math.sqrt(_lowerCAmelCase) + 1),			6):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int = 200_0000)						->		int:



						"""simple docstring"""

						return sum(num for num in range(3,			_lowerCAmelCase,			2) if is_prime(_lowerCAmelCase)) + 2 if n > 2 else 0


if __name__ == "__main__":
						print(f"{solution() = }")





