



'''simple docstring'''





import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


lowercase_		       =  logging.getLogger(__name__)






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       int          =       'token-classification'



 def __init__(self ,   A       )    ->     Any:


      """simple docstring"""



      if type(A       ) == dict:
           _a      =  Namespace(**A       )
      _a      =  import_module('''tasks'''       )
      try:
           _a      =  getattr(A ,   hparams.task_type       )
           _a      =  token_classification_task_clazz()
      except AttributeError:
           raise ValueError(
               f'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
               f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''       )
      _a      =  self.token_classification_task.get_labels(hparams.labels       )
      _a      =  CrossEntropyLoss().ignore_index
      super().__init__(A ,   len(self.labels       ) ,   self.mode       )



 def 					a__  (self ,   **A       )    ->     Dict:


      """simple docstring"""



      return self.model(**A       )



 def 					a__  (self ,   A ,   A       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
      if self.config.model_type != "distilbert":
           _a      =  (
               batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
           )  # XLM and RoBERTa don"t use token_type_ids

      _a      =  self(**A       )
      _a      =  outputs[0]
      # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
      return {"loss": loss}



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  self.hparams
      for mode in ["train", "dev", "test"]:
           _a      =  self._feature_file(A       )
           if os.path.exists(A       ) and not args.overwrite_cache:
                logger.info('''Loading features from cached file %s''' ,   A       )
                _a      =  torch.load(A       )
           else:
                logger.info('''Creating features from dataset file at %s''' ,   args.data_dir       )
                _a      =  self.token_classification_task.read_examples_from_file(args.data_dir ,   A       )
                _a      =  self.token_classification_task.convert_examples_to_features(
                    A ,   self.labels ,   args.max_seq_length ,   self.tokenizer ,   cls_token_at_end=bool(self.config.model_type in ['''xlnet''']       ) ,   cls_token=self.tokenizer.cls_token ,   cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0 ,   sep_token=self.tokenizer.sep_token ,   sep_token_extra=A ,   pad_on_left=bool(self.config.model_type in ['''xlnet''']       ) ,   pad_token=self.tokenizer.pad_token_id ,   pad_token_segment_id=self.tokenizer.pad_token_type_id ,   pad_token_label_id=self.pad_token_label_id ,   )
                logger.info('''Saving features into cached file %s''' ,   A       )
                torch.save(A ,   A       )



 def 					a__  (self ,   A ,   A ,   A = False       )    ->     DataLoader:


      """simple docstring"""



      _a      =  self._feature_file(A       )
      logger.info('''Loading features from cached file %s''' ,   A       )
      _a      =  torch.load(A       )
      _a      =  torch.tensor([f.input_ids for f in features] ,   dtype=torch.long       )
      _a      =  torch.tensor([f.attention_mask for f in features] ,   dtype=torch.long       )
      if features[0].token_type_ids is not None:
           _a      =  torch.tensor([f.token_type_ids for f in features] ,   dtype=torch.long       )
      else:
           _a      =  torch.tensor([0 for f in features] ,   dtype=torch.long       )
           # HACK(we will not use this anymore soon)
      _a      =  torch.tensor([f.label_ids for f in features] ,   dtype=torch.long       )
      return DataLoader(
          TensorDataset(A ,   A ,   A ,   A       ) ,   batch_size=A       )



 def 					a__  (self ,   A ,   A       )    ->     Union[str, Any]:


      """simple docstring"""



      """Compute validation""" ""
      _a      =  {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
      if self.config.model_type != "distilbert":
           _a      =  (
               batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
           )  # XLM and RoBERTa don"t use token_type_ids
      _a      =  self(**A       )
      _a  ,    _a      =  outputs[:2]
      _a      =  logits.detach().cpu().numpy()
      _a      =  inputs['''labels'''].detach().cpu().numpy()
      return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



 def 					a__  (self ,   A       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  torch.stack([x['''val_loss'''] for x in outputs]       ).mean()
      _a      =  np.concatenate([x['''pred'''] for x in outputs] ,   axis=0       )
      _a      =  np.argmax(A ,   axis=2       )
      _a      =  np.concatenate([x['''target'''] for x in outputs] ,   axis=0       )

      _a      =  dict(enumerate(self.labels       )       )
      _a      =  [[] for _ in range(out_label_ids.shape[0]       )]
      _a      =  [[] for _ in range(out_label_ids.shape[0]       )]

      for i in range(out_label_ids.shape[0]       ):
           for j in range(out_label_ids.shape[1]       ):
                if out_label_ids[i, j] != self.pad_token_label_id:
                     out_label_list[i].append(label_map[out_label_ids[i][j]]       )
                     preds_list[i].append(label_map[preds[i][j]]       )

      _a      =  {
          '''val_loss''': val_loss_mean,
          '''accuracy_score''': accuracy_score(A ,   A       ),
          '''precision''': precision_score(A ,   A       ),
          '''recall''': recall_score(A ,   A       ),
          '''f1''': fa_score(A ,   A       ),
      }

      _a      =  dict(results.items()       )
      _a      =  results
      return ret, preds_list, out_label_list



 def 					a__  (self ,   A       )    ->     List[Any]:


      """simple docstring"""



      _a  ,    _a  ,    _a      =  self._eval_end(A       )
      _a      =  ret['''log''']
      return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



 def 					a__  (self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



      _a  ,    _a  ,    _a      =  self._eval_end(A       )

      # Converting to the dict required by pl
      # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
      # pytorch_lightning/trainer/logging.py#L139
      _a      =  ret['''log''']
      # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
      return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



 @staticmethod
 def 					a__  (A ,   A       )    ->     Dict:


      """simple docstring"""



      BaseTransformer.add_model_specific_args(A ,   A       )
      parser.add_argument(
          '''--task_type''' ,   default='''NER''' ,   type=A ,   help='''Task type to fine tune in training (e.g. NER, POS, etc)'''       )
      parser.add_argument(
          '''--max_seq_length''' ,   default=128 ,   type=A ,   help=(
              '''The maximum total input sequence length after tokenization. Sequences longer '''
              '''than this will be truncated, sequences shorter will be padded.'''
          ) ,   )

      parser.add_argument(
          '''--labels''' ,   default='''''' ,   type=A ,   help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.''' ,   )
      parser.add_argument(
          '''--gpus''' ,   default=0 ,   type=A ,   help='''The number of GPUs allocated for this, it is by default 0 meaning none''' ,   )

      parser.add_argument(
          '''--overwrite_cache''' ,   action='''store_true''' ,   help='''Overwrite the cached training and evaluation sets'''       )

      return parser


if __name__ == "__main__":
  lowercase_		       =  argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  lowercase_		       =  NERTransformer.add_model_specific_args(parser, os.getcwd())
  lowercase_		       =  parser.parse_args()
  lowercase_		       =  NERTransformer(args)
  lowercase_		       =  generic_train(model, args)

  if args.do_predict:
    # See https://github.com/huggingface/transformers/issues/3159
    # pl use this default format to create a checkpoint:
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
    # /pytorch_lightning/callbacks/model_checkpoint.py#L322
    lowercase_		       =  sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
    lowercase_		       =  model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)








'''simple docstring'''





from math import sqrt




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A) and (
         number >= 0
     ), "'number' must been an int and positive"

     _a      =  True

     # 0 and 1 are none primes.
     if number <= 1:
          _a      =  False

     for divisor in range(2  ,	int(round(sqrt(__A))) + 1):
          # if 'number' divisible by 'divisor' then sets 'status'
          # of false and break up the loop.
          if number % divisor == 0:
               _a      =  False
               break

    # precondition
     assert isinstance(__A  ,	__A), "'status' must been from type bool"

     return status




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A) and (n > 2), "'N' must been an int and > 2"

     # beginList: contains all natural numbers from 2 up to N
     _a      =  list(range(2  ,	n + 1))

     _a      =  []  # this list will be returns.

     # actual sieve of erathostenes
     for i in range(len(__A)):
          for j in range(i + 1  ,	len(__A)):
               if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
                    _a      =  0

    # filters actual prime numbers.
     _a      =  [x for x in begin_list if x != 0]

     # precondition
     assert isinstance(__A  ,	__A), "'ans' must been from type list"

     return ans




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A) and (n > 2), "'N' must been an int and > 2"

     _a      =  []

     # iterates over all numbers between 2 up to N+1
     # if a number is prime then appends to list 'ans'
     for number in range(2  ,	n + 1):
          if is_prime(__A):
               ans.append(__A)

    # precondition
     assert isinstance(__A  ,	__A), "'ans' must been from type list"

     return ans




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A) and number >= 0, "'number' must been an int and >= 0"

     _a      =  []  # this list will be returns of the function.

     # potential prime number factors.

     _a      =  2

     _a      =  number

     if number == 0 or number == 1:
          ans.append(__A)

     # if 'number' not prime then builds the prime factorization of 'number'
     elif not is_prime(__A):
          while quotient != 1:
               if is_prime(__A) and (quotient % factor == 0):
                    ans.append(__A)
                    quotient /= factor
               else:
                    factor += 1

     else:
          ans.append(__A)

     # precondition
     assert isinstance(__A  ,	__A), "'ans' must been from type list"

     return ans




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A) and (
         number >= 0
     ), "'number' bust been an int and >= 0"

     _a      =  0

     # prime factorization of 'number'
     _a      =  prime_factorization(__A)

     _a      =  max(__A)

     # precondition
     assert isinstance(__A  ,	__A), "'ans' must been from type int"

     return ans




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A) and (
         number >= 0
     ), "'number' bust been an int and >= 0"

     _a      =  0

     # prime factorization of 'number'
     _a      =  prime_factorization(__A)

     _a      =  min(__A)

     # precondition
     assert isinstance(__A  ,	__A), "'ans' must been from type int"

     return ans




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A), "'number' must been an int"
     assert isinstance(number % 2 == 0  ,	__A), "compare bust been from type bool"

     return number % 2 == 0




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A), "'number' must been an int"
     assert isinstance(number % 2 != 0  ,	__A), "compare bust been from type bool"

     return number % 2 != 0




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert (
         isinstance(__A  ,	__A) and (number > 2) and is_even(__A)
     ), "'number' must been an int, even and > 2"

     _a      =  []  # this list will returned

     # creates a list of prime numbers between 2 up to 'number'
     _a      =  get_prime_numbers(__A)
     _a      =  len(__A)

     # run variable for while-loops.
     _a      =  0
     _a      =  None

     # exit variable. for break up the loops
     _a      =  True

     while i < len_pn and loop:
          _a      =  i + 1

          while j < len_pn and loop:
               if prime_numbers[i] + prime_numbers[j] == number:
                    _a      =  False
                    ans.append(prime_numbers[i])
                    ans.append(prime_numbers[j])

               j += 1

          i += 1

     # precondition
     assert (
         isinstance(__A  ,	__A)
         and (len(__A) == 2)
         and (ans[0] + ans[1] == number)
         and is_prime(ans[0])
         and is_prime(ans[1])
     ), "'ans' must contains two primes. And sum of elements must been eq 'number'"

     return ans




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     assert (
         isinstance(__A  ,	__A)
         and isinstance(__A  ,	__A)
         and (numbera >= 0)
         and (numbera >= 0)
     ), "'number1' and 'number2' must been positive integer."

     _a      =  0

     while numbera != 0:
          _a      =  numbera % numbera
          _a      =  numbera
          _a      =  rest

     # precondition
     assert isinstance(__A  ,	__A) and (
         numbera >= 0
     ), "'number' must been from type int and positive"

     return numbera




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     assert (
         isinstance(__A  ,	__A)
         and isinstance(__A  ,	__A)
         and (numbera >= 1)
         and (numbera >= 1)
     ), "'number1' and 'number2' must been positive integer."

     _a      =  1  # actual answer that will be return.

     # for kgV (x,1)
     if numbera > 1 and numbera > 1:
          # builds the prime factorization of 'number1' and 'number2'
          _a      =  prime_factorization(__A)
          _a      =  prime_factorization(__A)

     elif numbera == 1 or numbera == 1:
          _a      =  []
          _a      =  []
          _a      =  max(__A  ,	__A)

     _a      =  0
     _a      =  0

     _a      =  []  # captured numbers int both 'primeFac1' and 'primeFac2'

     # iterates through primeFac1
     for n in prime_fac_a:
          if n not in done:
               if n in prime_fac_a:
                    _a      =  prime_fac_a.count(__A)
                    _a      =  prime_fac_a.count(__A)

                    for _ in range(max(__A  ,	__A)):
                         ans *= n

               else:
                    _a      =  prime_fac_a.count(__A)

                    for _ in range(__A):
                         ans *= n

               done.append(__A)

    # iterates through primeFac2
     for n in prime_fac_a:
          if n not in done:
               _a      =  prime_fac_a.count(__A)

               for _ in range(__A):
                    ans *= n

               done.append(__A)

    # precondition
     assert isinstance(__A  ,	__A) and (
         ans >= 0
     ), "'ans' must been from type int and positive"

     return ans




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A) and (n >= 0), "'number' must been a positive int"

     _a      =  0
     _a      =  2  # this variable holds the answer

     while index < n:
          index += 1

          ans += 1  # counts to the next number

          # if ans not prime then
          # runs to the next prime number.
          while not is_prime(__A):
               ans += 1

    # precondition
     assert isinstance(__A  ,	__A) and is_prime(
         __A), "'ans' must been a prime number and from type int"

     return ans




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     assert (
         is_prime(__A) and is_prime(__A) and (p_number_a < p_number_a)
     ), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

     _a      =  p_number_a + 1  # jump to the next number

     _a      =  []  # this list will be returns.

     # if number is not prime then
     # fetch the next prime number.
     while not is_prime(__A):
          number += 1

     while number < p_number_a:
          ans.append(__A)

          number += 1

          # fetch the next prime number.
          while not is_prime(__A):
               number += 1

    # precondition
     assert (
         isinstance(__A  ,	__A)
         and ans[0] != p_number_a
         and ans[len(__A) - 1] != p_number_a
     ), "'ans' must been a list without the arguments"

     # 'ans' contains not 'pNumber1' and 'pNumber2' !
     return ans




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A) and (n >= 1), "'n' must been int and >= 1"

     _a      =  []  # will be returned.

     for divisor in range(1  ,	n + 1):
          if n % divisor == 0:
               ans.append(__A)

    # precondition
     assert ans[0] == 1 and ans[len(__A) - 1] == n, "Error in function getDivisiors(...)"

     return ans




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A) and (
         number > 1
     ), "'number' must been an int and >= 1"

     _a      =  get_divisors(__A)

     # precondition
     assert (
         isinstance(__A  ,	__A)
         and (divisors[0] == 1)
         and (divisors[len(__A) - 1] == number)
     ), "Error in help-function getDivisiors(...)"

     # summed all divisors up to 'number' (exclusive), hence [:-1]
     return sum(divisors[:-1]) == number




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     assert (
         isinstance(__A  ,	__A)
         and isinstance(__A  ,	__A)
         and (denominator != 0)
     ), "The arguments must been from type int and 'denominator' != 0"

     # build the greatest common divisor of numerator and denominator.
     _a      =  gcd(abs(__A)  ,	abs(__A))

     # precondition
     assert (
         isinstance(__A  ,	__A)
         and (numerator % gcd_of_fraction == 0)
         and (denominator % gcd_of_fraction == 0)
     ), "Error in function gcd(...,...)"

     return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A) and (n >= 0), "'n' must been a int and >= 0"

     _a      =  1  # this will be return.

     for factor in range(1  ,	n + 1):
          ans *= factor

     return ans




def lowerCAmelCase  (__A):



     """simple docstring"""



     assert isinstance(__A  ,	__A) and (n >= 0), "'n' must been an int and >= 0"

     _a      =  0
     _a      =  1
     _a      =  1  # this will be return

     for _ in range(n - 1):
          _a      =  ans
          ans += fiba
          _a      =  tmp

     return ans



