





"""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()


lowerCamelCase_			    =	logging.getLogger(__name__)






@dataclass
class       _SCREAMING_SNAKE_CASE:
       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       _SCREAMING_SNAKE_CASE:
       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   __lowerCamelCase     (							)    ->   str:
      # 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     :List[Any]	       =						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
      __SCREAMING_SNAKE_CASE     :Optional[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.'''							)

      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						import_module('''tasks'''							)

      try:
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						getattr(a_				,     model_args.task_type							)
            __SCREAMING_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'''				,     a_							)

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

      # Load pretrained model and tokenizer
      #
      # 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=a_				,     idalabel=a_				,     labelaid={label: i for i, label in enumerate(a_							)}				,     cache_dir=model_args.cache_dir				,     )
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						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():
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path				,     from_pt=bool('''.bin''' in model_args.model_name_or_path							)				,     config=a_				,     cache_dir=model_args.cache_dir				,     )

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

            for i in range(a_							):
                  for j in range(a_							):
                        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(a_  :	EvalPrediction							) -> Dict:
            __SCREAMING_SNAKE_CASE     :Dict	       =						align_predictions(p.predictions				,     p.label_ids							)

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

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

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

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

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

            with open(a_				,     '''w'''							) as writer:
                  logger.info('''***** Eval results *****'''							)

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

                  results.update(a_							)

    # Predict
      if training_args.do_predict:
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						TFTokenClassificationDataset(
                token_classification_task=a_				,     data_dir=data_args.data_dir				,     tokenizer=a_				,     labels=a_				,     model_type=config.model_type				,     max_seq_length=data_args.max_seq_length				,     overwrite_cache=data_args.overwrite_cache				,     mode=Split.test				,     )

            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						trainer.predict(test_dataset.get_dataset()							)
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						align_predictions(a_				,     a_							)
            __SCREAMING_SNAKE_CASE     :str	       =						classification_report(a_				,     a_							)

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

            __SCREAMING_SNAKE_CASE     :str	       =						os.path.join(training_args.output_dir				,     '''test_results.txt'''							)

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

            # Save predictions
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						os.path.join(training_args.output_dir				,     '''test_predictions.txt'''							)

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

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

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

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

      return results


if __name__ == "__main__":
  main()






"""simple docstring"""
import math
from collections.abc import Generator



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)							))
      for i in (n for n in numbers if n > 1):
            for j in range(2				,     a_							):
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)							))
      for i in (n for n in numbers if n > 1):
            # only need to check for factors up to sqrt(i)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						int(math.sqrt(a_							)							) + 1
            for j in range(2				,     a_							):
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)				,     2							))
      # It's useless to test even numbers as they will not be prime
      if max_n > 2:
            yield 2  # Because 2 will not be tested, it's necessary to yield it now
      for i in (n for n in numbers if n > 1):
            __SCREAMING_SNAKE_CASE     :int	       =						int(math.sqrt(a_							)							) + 1
            for j in range(3				,     a_				,     2							):
                  # As we removed the even numbers, we don't need them now
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							)    ->   Union[str, Any]:
      from timeit import timeit

      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						'''from __main__ import slow_primes, primes, fast_primes'''
      print(timeit('''slow_primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)
      print(timeit('''primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)
      print(timeit('''fast_primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)


if __name__ == "__main__":
  lowerCamelCase_          =	int(input("Calculate primes up to:\n>> ").strip())
  for ret in primes(number):
    print(ret)
  benchmark()