



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_		:  int										=  logging.getLogger(__name__)



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



@dataclass
class 		_lowercase		:
   _UpperCAmelCase  						=      field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}						)
   _UpperCAmelCase  						=      field(
       metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}						)
   _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=UpperCAmelCase__, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}						)


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
 a		,	a		,	a							=		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."       )

 a							=		import_module("tasks"       )

 try:
  a							=		getattr(UpperCAmelCase__			,       model_args.task_type       )
  a							=		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"			,       UpperCAmelCase__       )

 # Prepare Token Classification task
 a							=		token_classification_task.get_labels(data_args.labels       )
 a							=		dict(enumerate(UpperCAmelCase__       )       )
 a							=		len(UpperCAmelCase__       )

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

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

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

  for i in range(UpperCAmelCase__       ):
   for j in range(UpperCAmelCase__       ):
    if label_ids[i, j] != -1_00:
     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(UpperCAmelCase__			:EvalPrediction       ) -> Dict:
  a		,	a							=		align_predictions(p.predictions			,       p.label_ids       )

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

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

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

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

  a							=		trainer.evaluate()
  a							=		os.path.join(training_args.output_dir			,       "eval_results.txt"       )

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

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

   results.update(UpperCAmelCase__       )

    # Predict
 if training_args.do_predict:
  a							=		TFTokenClassificationDataset(
      token_classification_task=UpperCAmelCase__			,       data_dir=data_args.data_dir			,       tokenizer=UpperCAmelCase__			,       labels=UpperCAmelCase__			,       model_type=config.model_type			,       max_seq_length=data_args.max_seq_length			,       overwrite_cache=data_args.overwrite_cache			,       mode=Split.test			,       )

  a		,	a		,	a							=		trainer.predict(test_dataset.get_dataset()       )
  a		,	a							=		align_predictions(UpperCAmelCase__			,       UpperCAmelCase__       )
  a							=		classification_report(UpperCAmelCase__			,       UpperCAmelCase__       )

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

  a							=		os.path.join(training_args.output_dir			,       "test_results.txt"       )

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

  # Save predictions
  a							=		os.path.join(training_args.output_dir			,       "test_predictions.txt"       )

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

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

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

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

 return results


if __name__ == "__main__":
     main()





import math






def 	lowerCamelCase_       (			_a			):




		"""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(_a			) + 1			)      ,  6			):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 	lowerCamelCase_       (			_a = 1_000      ,  _a = 1_000			):




		"""simple docstring"""






		lowerCAmelCase__  :     Union[str, Any]       	=   [0, 0, 0]  # length, a, b
		for a in range((a_limit * -1) + 1      ,  _a			):
				for b in range(2      ,  _a			):
						if is_prime(_a			):
								lowerCAmelCase__  :     str       	=   0
								lowerCAmelCase__  :     int       	=   0
								while is_prime((n**2) + (a * n) + b			):
										count += 1
										n += 1
								if count > longest[0]:
										lowerCAmelCase__  :     Dict       	=   [count, a, b]
		lowerCAmelCase__  :     int       	=   longest[1] * longest[2]
		return ans


if __name__ == "__main__":
		print(solution(1000, 1000))




