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


SCREAMING_SNAKE_CASE :	str							     =      logging.getLogger(__name__)
@dataclass
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=_A			,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default="""NER"""			,       metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=_A			,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
  _SCREAMING_SNAKE_CASE    				=			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    				=			field(
      default=_A			,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,       )
@dataclass
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			field(
      metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=_A			,       metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}			,       )
  _SCREAMING_SNAKE_CASE    				=			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."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=_A			,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )



def   __A		(			):



 """simple docstring"""


 __a       =  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.
  __a       ,     __a       ,     __a       =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
 else:
  __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(A_							,				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 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"							,				A_					)

 # Set seed
 set_seed(training_args.seed					)

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

 # 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=A_							,				idalabel=A_							,				labelaid={label: i for i, label in enumerate(A_					)}							,				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							,				)
 __a       =  AutoModelForTokenClassification.from_pretrained(
     model_args.model_name_or_path							,				from_tf=bool(".ckpt" in model_args.model_name_or_path					)							,				config=A_							,				cache_dir=model_args.cache_dir							,				)

 # Get datasets
 __a       =  (
     TokenClassificationDataset(
         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
 )
 __a       =  (
     TokenClassificationDataset(
         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							,				_A					) -> Tuple[List[int], List[int]]:
  __a       =  np.argmax(A_							,				axis=2					)

  __a       ,     __a       =  preds.shape

  __a       =  [[] for _ in range(A_					)]
  __a       =  [[] for _ in range(A_					)]

  for i in range(A_					):
   for j in range(A_					):
    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(_A					) -> Dict:
  __a       ,     __a       =  align_predictions(p.predictions							,				p.label_ids					)
  return {
      "accuracy_score": accuracy_score(A_							,				A_					),
      "precision": precision_score(A_							,				A_					),
      "recall": recall_score(A_							,				A_					),
      "f1": fa_score(A_							,				A_					),
  }

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

 # Initialize our Trainer
 __a       =  Trainer(
     model=A_							,				args=A_							,				train_dataset=A_							,				eval_dataset=A_							,				compute_metrics=A_							,				data_collator=A_							,				)

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

  __a       =  trainer.evaluate()

  __a       =  os.path.join(training_args.output_dir							,				"eval_results.txt"					)
  if trainer.is_world_process_zero():
   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:
  __a       =  TokenClassificationDataset(
      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							,				)

  __a       ,     __a       ,     __a       =  trainer.predict(A_					)
  __a       ,     __a       =  align_predictions(A_							,				A_					)

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

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

 return results



def   __A		(			_A					):



 """simple docstring"""


 main()


if __name__ == "__main__":
  main()



from __future__ import annotations



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [True] * limit
 __a       =  False
 __a       =  False
 __a       =  True

 for i in range(3							,				int(limit**0.5 + 1					)							,				2					):
  __a       =  i * 2
  while index < limit:
   __a       =  False
   __a       =  index + i

 __a       =  [2]

 for i in range(3							,				_A							,				2					):
  if is_prime[i]:
   primes.append(_A					)

 return primes



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  prime_sieve(_A					)
 __a       =  0
 __a       =  0

 for i in range(len(_A					)					):
  for j in range(i + length							,				len(_A					)					):
   __a       =  sum(primes[i:j]					)
   if sol >= ceiling:
    break

   if sol in primes:
    __a       =  j - i
    __a       =  sol

 return largest


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


