






"""simple docstring"""







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       :	Union[str, Any]             = logging.getLogger(__name__)







class        _SCREAMING_SNAKE_CASE						(  _UpperCAmelCase							):





       """simple docstring"""





       _SCREAMING_SNAKE_CASE    ='sequence-classification'




       def __init__(   self:     Dict				,		__A:     List[Any]    ):

            '''simple docstring'''

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

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




       def  lowercase		(   self:     int				,		**__A:     Optional[int]    ):

            '''simple docstring'''

            return self.model(**lowercase__    )




       def  lowercase		(   self:     List[str]				,		__A:     Dict				,		__A:     Tuple    ):

            '''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(**lowercase__    )
            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  lowercase		(   self:     Dict    ):

            '''simple docstring'''

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

            for mode in ["train", "dev"]:
                 a__          =  self._feature_file(lowercase__    )
                 if os.path.exists(lowercase__    ) and not args.overwrite_cache:
                      logger.info('''Loading features from cached file %s'''				,		lowercase__    )
                 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(
                          lowercase__				,		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'''				,		lowercase__    )
                      torch.save(lowercase__				,		lowercase__    )




       def  lowercase		(   self:     str				,		__A:     Union[str, Any]				,		__A:     Optional[Any]				,		__A:     List[str] = False    ):

            '''simple docstring'''

            a__          =  "dev" if mode == "test" else mode

            a__          =  self._feature_file(lowercase__    )
            logger.info('''Loading features from cached file %s'''				,		lowercase__    )
            a__          =  torch.load(lowercase__    )
            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(lowercase__				,		lowercase__				,		lowercase__				,		lowercase__    )				,		batch_size=lowercase__				,		shuffle=lowercase__				,		)




       def  lowercase		(   self:     Optional[int]				,		__A:     Any				,		__A:     Union[str, 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(**lowercase__    )
            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  lowercase		(   self:     List[Any]				,		__A:     Optional[int]    ):

            '''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(lowercase__				,		axis=1    )
            elif self.hparams.glue_output_mode == "regression":
                 a__          =  np.squeeze(lowercase__    )

            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				,		lowercase__				,		lowercase__    )}

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




       def  lowercase		(   self:     int				,		__A:     List[str]    ):

            '''simple docstring'''

            a__          =  self._eval_end(lowercase__    )
            a__          =  ret["log"]
            return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




       def  lowercase		(   self:     Dict				,		__A:     List[str]    ):

            '''simple docstring'''

            a__          =  self._eval_end(lowercase__    )
            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  lowercase		(   __A:     Tuple				,		__A:     Dict    ):

            '''simple docstring'''

            BaseTransformer.add_model_specific_args(lowercase__				,		lowercase__    )
            parser.add_argument(
                '''--max_seq_length'''				,		default=128				,		type=lowercase__				,		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=lowercase__				,		required=lowercase__				,		help='''The GLUE task to run'''				,		)
            parser.add_argument(
                '''--gpus'''				,		default=0				,		type=lowercase__				,		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        SCREAMING_SNAKE_CASE       (      ):
     a__          =  argparse.ArgumentParser()
     add_generic_args(SCREAMING_SNAKE_CASE_ ,     os.getcwd())
     a__          =  GLUETransformer.add_model_specific_args(SCREAMING_SNAKE_CASE_ ,     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(SCREAMING_SNAKE_CASE_)
     a__          =  generic_train(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_)

     # 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=SCREAMING_SNAKE_CASE_))
          a__          =  model.load_from_checkpoint(checkpoints[-1])
          return trainer.test(SCREAMING_SNAKE_CASE_)


if __name__ == "__main__":
 main()










"""simple docstring"""







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Any    ):

												'''simple docstring'''

												a__          =  {}




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												print(self.vertex    )
												for i in self.vertex:
																	print(__A				,		''' -> '''				,		''' -> '''.join([str(__A    ) for j in self.vertex[i]]    )    )




							def  lowercase		(   self:     Union[str, Any]				,		__A:     int				,		__A:     int    ):

												'''simple docstring'''

												if from_vertex in self.vertex:
																	self.vertex[from_vertex].append(__A    )
												else:
																	# else make a new vertex
																	a__          =  [to_vertex]




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  [False] * len(self.vertex    )

												# call the recursive helper function
												for i in range(len(self.vertex    )    ):
																	if not visited[i]:
																						self.dfs_recursive(__A				,		__A    )





							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     list    ):

												'''simple docstring'''

												a__          =  True

												print(__A				,		end=''' '''    )

												# Recur for all the vertices that are adjacent to this node
												for i in self.vertex:
																	if not visited[i]:
																						self.dfs_recursive(__A				,		__A    )


if __name__ == "__main__":
	__a       :	Any             = Graph()
	g.add_edge(0, 1)
	g.add_edge(0, 2)
	g.add_edge(1, 2)
	g.add_edge(2, 0)
	g.add_edge(2, 3)
	g.add_edge(3, 3)

	g.print_graph()
	print('DFS:')
	g.dfs()

	# OUTPUT:
	# 0  ->  1 -> 2
	# 1  ->  2
	# 2  ->  0 -> 3
	# 3  ->  3
	# DFS:
	#  0 1 2 3


