
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


_A   =			logging.getLogger(__name__)







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							List[Any]									=		"sequence-classification"





     def __init__(self				:   Dict    ,   _A				:   Optional[int]       )						->						Optional[Any]:
         if type(_A       ) == dict:
             snake_case								= Namespace(**_A       )
         snake_case								= glue_output_modes[hparams.task]
         snake_case								= glue_tasks_num_labels[hparams.task]

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





     def 		UpperCAmelCase(self				:   List[Any]    ,   **_A				:   int       )						->						Dict:
         return self.model(**_A       )





     def 		UpperCAmelCase(self				:   str    ,   _A				:   List[str]    ,   _A				:   List[Any]       )						->						str:
         snake_case								= {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

         snake_case								= self(**_A       )
         snake_case								= outputs[0]

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





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						Tuple:
         snake_case								= self.hparams
         snake_case								= processors[args.task]()
         snake_case								= processor.get_labels()

         for mode in ["train", "dev"]:
             snake_case								= self._feature_file(_A       )
             if os.path.exists(_A       ) and not args.overwrite_cache:
                 logger.info("Loading features from cached file %s"    ,   _A       )
             else:
                 logger.info("Creating features from dataset file at %s"    ,   args.data_dir       )
                 snake_case								= (
                     processor.get_dev_examples(args.data_dir       )
                     if mode == "dev"
                     else processor.get_train_examples(args.data_dir       )
                 )
                 snake_case								= convert_examples_to_features(
                     _A    ,   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"    ,   _A       )
                 torch.save(_A    ,   _A       )





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   str    ,   _A				:   int    ,   _A				:   bool = False       )						->						DataLoader:
         snake_case								= "dev" if mode == "test" else mode

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

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





     def 		UpperCAmelCase(self				:   int    ,   _A				:   Optional[Any]    ,   _A				:   int       )						->						int:
         snake_case								= {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

         snake_case								= self(**_A       )
         snake_case					,							snake_case								= outputs[:2]
         snake_case								= logits.detach().cpu().numpy()
         snake_case								= inputs["labels"].detach().cpu().numpy()

         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





     def 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   List[Any]       )						->						tuple:
         snake_case								= torch.stack([x["val_loss"] for x in outputs]       ).mean().detach().cpu().item()
         snake_case								= np.concatenate([x["pred"] for x in outputs]    ,   axis=0       )

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

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

         snake_case								= {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task    ,   _A    ,   _A       )}

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





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   list       )						->						dict:
         snake_case					,							snake_case					,							snake_case								= self._eval_end(_A       )
         snake_case								= ret["log"]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





     def 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   Tuple       )						->						dict:
         snake_case					,							snake_case					,							snake_case								= self._eval_end(_A       )
         snake_case								= 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(_A				:   List[str]    ,   _A				:   int       )						->						int:
         BaseTransformer.add_model_specific_args(_A    ,   _A       )
         parser.add_argument(
             "--max_seq_length"    ,   default=1_2_8    ,   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(
             "--task"    ,   default=""    ,   type=_A    ,   required=_A    ,   help="The GLUE task to run"    ,   )
         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



def 						lowercase_      (       )							->							Tuple:
    """simple docstring"""

    snake_case								= argparse.ArgumentParser()
    add_generic_args(A__			,		os.getcwd()		)
    snake_case								= GLUETransformer.add_model_specific_args(A__			,		os.getcwd()		)
    snake_case								= parser.parse_args()

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

    snake_case								= GLUETransformer(A__		)
    snake_case								= generic_train(A__			,		A__		)

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


if __name__ == "__main__":
   main()



from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def 						lowercase_      (       A__		)							->							bool:
    """simple docstring"""

    snake_case								= int(number**0.5		)
    return number == sq * sq



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__			,		A__		)							->							tuple[int, int]:
    """simple docstring"""

    snake_case								= x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    snake_case								= x_den * y_den * z_den
    snake_case								= gcd(A__			,		A__		)
    top //= hcf
    bottom //= hcf
    return top, bottom



def 						lowercase_      (       A__ = 35		)							->							int:
    """simple docstring"""

    snake_case								= set()
    snake_case								= 42
    snake_case								= Fraction(0		)
    snake_case								= 42

    for x_num in range(1			,		order + 1		):
        for x_den in range(x_num + 1			,		order + 1		):
            for y_num in range(1			,		order + 1		):
                for y_den in range(y_num + 1			,		order + 1		):
                    # n=1
                    snake_case								= x_num * y_den + x_den * y_num
                    snake_case								= x_den * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= (
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    snake_case								= x_den * x_den * y_den * y_den
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

                    # n=-1
                    snake_case								= x_num * y_num
                    snake_case								= x_den * y_num + x_num * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= x_num * x_num * y_num * y_num
                    snake_case								= (
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

    for num, den in unique_s:
        total += Fraction(A__			,		A__		)

    return total.denominator + total.numerator


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

