

import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


__lowercase				      =						logging.getLogger(__name__)

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Optional[Any]  					=    'sequence-classification'

   def __init__(				self	:    List[Any]			,						lowerCamelCase__	:    List[str]       ) ->			Any:
         """simple docstring"""





         if type(snake_case__       ) == dict:
               A_							       =		Namespace(**snake_case__       )
         A_							       =		glue_output_modes[hparams.task]
         A_							       =		glue_tasks_num_labels[hparams.task]

         super().__init__(snake_case__			,						snake_case__			,						self.mode       )

   def     UpperCamelCase					(				self	:    Dict			,						**lowerCamelCase__	:    Any       ) ->			str:
         """simple docstring"""





         return self.model(**snake_case__       )

   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    List[str]       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

         if self.config.model_type not in ["distilbert", "bart"]:
               A_							       =		batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

         A_							       =		self(**snake_case__       )
         A_							       =		outputs[0]

         A_							       =		self.trainer.lr_schedulers[0]['''scheduler''']
         A_							       =		{'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss, "log": tensorboard_logs}

   def     UpperCamelCase					(				self	:    Dict       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.hparams
         A_							       =		processors[args.task]()
         A_							       =		processor.get_labels()

         for mode in ["train", "dev"]:
               A_							       =		self._feature_file(snake_case__       )
               if os.path.exists(snake_case__       ) and not args.overwrite_cache:
                     logger.info('''Loading features from cached file %s'''			,						snake_case__       )
               else:
                     logger.info('''Creating features from dataset file at %s'''			,						args.data_dir       )
                     A_							       =		(
                         processor.get_dev_examples(args.data_dir       )
                         if mode == '''dev'''
                         else processor.get_train_examples(args.data_dir       )
                     )
                     A_							       =		convert_examples_to_features(
                         snake_case__			,						self.tokenizer			,						max_length=args.max_seq_length			,						label_list=self.labels			,						output_mode=args.glue_output_mode			,						)
                     logger.info('''Saving features into cached file %s'''			,						snake_case__       )
                     torch.save(snake_case__			,						snake_case__       )

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    bool = False       ) ->			str:
         """simple docstring"""





         A_							       =		'''dev''' if mode == '''test''' else mode

         A_							       =		self._feature_file(snake_case__       )
         logger.info('''Loading features from cached file %s'''			,						snake_case__       )
         A_							       =		torch.load(snake_case__       )
         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       )
         A_							       =		torch.tensor([f.token_type_ids for f in features]			,						dtype=torch.long       )
         if self.hparams.glue_output_mode == "classification":
               A_							       =		torch.tensor([f.label for f in features]			,						dtype=torch.long       )
         elif self.hparams.glue_output_mode == "regression":
               A_							       =		torch.tensor([f.label for f in features]			,						dtype=torch.float       )

         return DataLoader(
             TensorDataset(snake_case__			,						snake_case__			,						snake_case__			,						snake_case__       )			,						batch_size=snake_case__			,						shuffle=snake_case__			,						)

   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Optional[int]       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

         if self.config.model_type not in ["distilbert", "bart"]:
               A_							       =		batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

         A_							       =		self(**snake_case__       )
         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     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    Dict       ) ->			Dict:
         """simple docstring"""





         A_							       =		torch.stack([x['''val_loss'''] for x in outputs]       ).mean().detach().cpu().item()
         A_							       =		np.concatenate([x['''pred'''] for x in outputs]			,						axis=0       )

         if self.hparams.glue_output_mode == "classification":
               A_							       =		np.argmax(snake_case__			,						axis=1       )
         elif self.hparams.glue_output_mode == "regression":
               A_							       =		np.squeeze(snake_case__       )

         A_							       =		np.concatenate([x['''target'''] for x in outputs]			,						axis=0       )
         A_							       =		[[] for _ in range(out_label_ids.shape[0]       )]
         A_							       =		[[] for _ in range(out_label_ids.shape[0]       )]

         A_							       =		{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task			,						snake_case__			,						snake_case__       )}

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

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    list       ) ->			Union[str, Any]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    int       ) ->			Tuple:
         """simple docstring"""





         A_       ,A_       ,A_							       =		self._eval_end(snake_case__       )
         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     UpperCamelCase					(				lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    List[str]       ) ->			Dict:
         """simple docstring"""





         BaseTransformer.add_model_specific_args(snake_case__			,						snake_case__       )
         parser.add_argument(
             '''--max_seq_length'''			,						default=1_2_8			,						type=snake_case__			,						help=(
                 '''The maximum total input sequence length after tokenization. Sequences longer '''
                 '''than this will be truncated, sequences shorter will be padded.'''
             )			,						)

         parser.add_argument(
             '''--task'''			,						default=''''''			,						type=snake_case__			,						required=snake_case__			,						help='''The GLUE task to run'''			,						)
         parser.add_argument(
             '''--gpus'''			,						default=0			,						type=snake_case__			,						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







def 							_lowerCamelCase   (			):
      '''simple docstring'''







      A_							       =		argparse.ArgumentParser()
      add_generic_args(_UpperCamelCase		,				os.getcwd()   )
      A_							       =		GLUETransformer.add_model_specific_args(_UpperCamelCase		,				os.getcwd()   )
      A_							       =		parser.parse_args()

      # If output_dir not provided, a folder will be generated in pwd
      if args.output_dir is None:
            A_							       =		os.path.join(
                '''./results'''		,				f"{args.task}_{time.strftime('%Y%m%d_%H%M%S'   )}"		,				)
            os.makedirs(args.output_dir   )

      A_							       =		GLUETransformer(_UpperCamelCase   )
      A_							       =		generic_train(_UpperCamelCase		,				_UpperCamelCase   )

      # Optionally, predict on dev set and write to output_dir
      if args.do_predict:
            A_							       =		sorted(glob.glob(os.path.join(args.output_dir		,				'''checkpoint-epoch=*.ckpt'''   )		,				recursive=_UpperCamelCase   )   )
            A_							       =		model.load_from_checkpoint(checkpoints[-1]   )
            return trainer.test(_UpperCamelCase   )


if __name__ == "__main__":
      main()








from __future__ import annotations

from math import gcd


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	int = 2		,				_UpperCamelCase		:	int = 1		,				_UpperCamelCase		:	int = 3		,				)		->				int | None:
 '''simple docstring'''


 if num < 2:
  raise ValueError('The input value cannot be less than 2'						)

 # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
 # algorithm struggles to find factors that are divisible by two.
 # As a workaround, we specifically check for two and even inputs.
 #   See: https://math.stackexchange.com/a/2856214/165820
 if num > 2 and num % 2 == 0:
  return 2

 # Pollard's Rho algorithm requires a function that returns pseudorandom
 # values between 0 <= X < ``num``.  It doesn't need to be random in the
 # sense that the output value is cryptographically secure or difficult
 # to calculate, it only needs to be random in the sense that all output
 # values should be equally likely to appear.
 # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
 # However, the success of Pollard's algorithm isn't guaranteed and is
 # determined in part by the initial seed and the chosen random function.
 # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
 # where ``C`` is a value that we can modify between each attempt.
 def rand_fn(_UpperCamelCase		:	int		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	int						) -> int:
  return (pow(_UpperCamelCase		,				2						) + step) % modulus

 for _ in range(_UpperCamelCase						):
  # These track the position within the cycle detection logic.
  SCREAMING_SNAKE_CASE		    =      seed
  SCREAMING_SNAKE_CASE		    =      seed

  while True:
   # At each iteration, the tortoise moves one step and the hare moves two.
   SCREAMING_SNAKE_CASE		    =      rand_fn(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      rand_fn(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      rand_fn(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)

   # At some point both the tortoise and the hare will enter a cycle whose
   # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
   # the tortoise and hare will end up on the same value modulo ``p``.
   # We can detect when this happens because the position difference between
   # the tortoise and the hare will share a common divisor with ``num``.
   SCREAMING_SNAKE_CASE		    =      gcd(hare - tortoise		,				_UpperCamelCase						)

   if divisor == 1:
    # No common divisor yet, just keep searching.
    continue
   else:
    # We found a common divisor!
    if divisor == num:
     # Unfortunately, the divisor is ``num`` itself and is useless.
     break
    else:
     # The divisor is a nontrivial factor of ``num``!
     return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
  SCREAMING_SNAKE_CASE		    =      hare

  # The new step value for the random function can just be incremented.
  # At first the results will be similar to what the old function would
  # have produced, but the value will quickly diverge after a bit.
  step += 1

 # We haven't found a divisor within the requested number of attempts.
 # We were unlucky or ``num`` itself is actually prime.
 return None


if __name__ == "__main__":
      import argparse

      a_     :    int     		=   argparse.ArgumentParser()
      parser.add_argument(
          "num",
          type=int,
          help="The value to find a divisor of",
      )
      parser.add_argument(
          "--attempts",
          type=int,
          default=3,
          help="The number of attempts before giving up",
      )
      a_     :    Tuple     		=   parser.parse_args()

      a_     :    int     		=   pollard_rho(args.num, attempts=args.attempts)
      if divisor is None:
            print(F"""{args.num} is probably prime""")
      else:
            a_     :    Union[str, Any]     		=   args.num // divisor
            print(F"""{args.num} = {divisor} * {quotient}""")

