

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


__UpperCAmelCase =				logging.getLogger(__name__)







class      __lowercase			(    a__					):
  snake_case_				       =       "sequence-classification"

  def __init__(  self		:			Optional[int]					,A		:			List[Any]    ):






   '''simple docstring'''
   if type(lowerCamelCase_    ) == dict:
    UpperCAmelCase__					:  Dict					   =					Namespace(**lowerCamelCase_    )
   UpperCAmelCase__					:  Optional[int]					   =					glue_output_modes[hparams.task]
   UpperCAmelCase__					:  str					   =					glue_tasks_num_labels[hparams.task]

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

  def    __lowercase       (  self		:			List[Any]					,**A		:			int    ):






   '''simple docstring'''
   return self.model(**lowerCamelCase_    )

  def    __lowercase       (  self		:			Optional[Any]					,A		:			Union[str, Any]					,A		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

   UpperCAmelCase__					:  Union[str, Any]					   =					self(**lowerCamelCase_    )
   UpperCAmelCase__					:  Optional[int]					   =					outputs[0]

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

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					self.hparams
   UpperCAmelCase__					:  Tuple					   =					processors[args.task]()
   UpperCAmelCase__					:  str					   =					processor.get_labels()

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

  def    __lowercase       (  self		:			Optional[Any]					,A		:			int					,A		:			Optional[int]					,A		:			List[str] = False    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					"""dev""" if mode == """test""" else mode

   UpperCAmelCase__					:  Union[str, Any]					   =					self._feature_file(lowerCamelCase_    )
   logger.info("""Loading features from cached file %s"""					,lowerCamelCase_    )
   UpperCAmelCase__					:  Optional[int]					   =					torch.load(lowerCamelCase_    )
   UpperCAmelCase__					:  int					   =					torch.tensor([f.input_ids for f in features]					,dtype=torch.long    )
   UpperCAmelCase__					:  List[Any]					   =					torch.tensor([f.attention_mask for f in features]					,dtype=torch.long    )
   UpperCAmelCase__					:  Any					   =					torch.tensor([f.token_type_ids for f in features]					,dtype=torch.long    )
   if self.hparams.glue_output_mode == "classification":
    UpperCAmelCase__					:  List[Any]					   =					torch.tensor([f.label for f in features]					,dtype=torch.long    )
   elif self.hparams.glue_output_mode == "regression":
    UpperCAmelCase__					:  int					   =					torch.tensor([f.label for f in features]					,dtype=torch.float    )

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

  def    __lowercase       (  self		:			List[str]					,A		:			Union[str, Any]					,A		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

   UpperCAmelCase__					:  Any					   =					self(**lowerCamelCase_    )
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[Any]					   =					outputs[:2]
   UpperCAmelCase__					:  Optional[int]					   =					logits.detach().cpu().numpy()
   UpperCAmelCase__					:  List[Any]					   =					inputs["""labels"""].detach().cpu().numpy()

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

  def    __lowercase       (  self		:			str					,A		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					torch.stack([x["""val_loss"""] for x in outputs]    ).mean().detach().cpu().item()
   UpperCAmelCase__					:  Union[str, Any]					   =					np.concatenate([x["""pred"""] for x in outputs]					,axis=0    )

   if self.hparams.glue_output_mode == "classification":
    UpperCAmelCase__					:  Tuple					   =					np.argmax(lowerCamelCase_					,axis=1    )
   elif self.hparams.glue_output_mode == "regression":
    UpperCAmelCase__					:  List[Any]					   =					np.squeeze(lowerCamelCase_    )

   UpperCAmelCase__					:  List[str]					   =					np.concatenate([x["""target"""] for x in outputs]					,axis=0    )
   UpperCAmelCase__					:  Optional[Any]					   =					[[] for _ in range(out_label_ids.shape[0]    )]
   UpperCAmelCase__					:  Optional[int]					   =					[[] for _ in range(out_label_ids.shape[0]    )]

   UpperCAmelCase__					:  Optional[int]					   =					{**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task					,lowerCamelCase_					,lowerCamelCase_    )}

   UpperCAmelCase__					:  Dict					   =					dict(results.items()    )
   UpperCAmelCase__					:  Optional[int]					   =					results
   return ret, preds_list, out_label_list

  def    __lowercase       (  self		:			List[Any]					,A		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					self._eval_end(lowerCamelCase_    )
   UpperCAmelCase__					:  Optional[int]					   =					ret["""log"""]
   return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

  def    __lowercase       (  self		:			Optional[int]					,A		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					self._eval_end(lowerCamelCase_    )
   UpperCAmelCase__					:  Optional[Any]					   =					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		:			int					,A		:			Union[str, Any]    ):






   '''simple docstring'''
   BaseTransformer.add_model_specific_args(lowerCamelCase_					,lowerCamelCase_    )
   parser.add_argument(
       """--max_seq_length"""					,default=128					,type=lowerCamelCase_					,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=lowerCamelCase_					,required=lowerCamelCase_					,help="""The GLUE task to run"""					,)
   parser.add_argument(
       """--gpus"""					,default=0					,type=lowerCamelCase_					,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'''





 UpperCAmelCase__					:  Dict					   =					argparse.ArgumentParser()
 add_generic_args(__UpperCamelCase							,    os.getcwd()      )
 UpperCAmelCase__					:  Union[str, Any]					   =					GLUETransformer.add_model_specific_args(__UpperCamelCase							,    os.getcwd()      )
 UpperCAmelCase__					:  str					   =					parser.parse_args()

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

 UpperCAmelCase__					:  List[Any]					   =					GLUETransformer(__UpperCamelCase      )
 UpperCAmelCase__					:  Tuple					   =					generic_train(__UpperCamelCase							,    __UpperCamelCase      )

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


if __name__ == "__main__":
      main()








"""simple docstring"""




import random
from typing import Any
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 for _ in range(len(__UpperCamelCase      )      ):
  UpperCAmelCase__					:  Dict					   =					random.randint(0							,    len(__UpperCamelCase      ) - 1      )
  UpperCAmelCase__					:  Tuple					   =					random.randint(0							,    len(__UpperCamelCase      ) - 1      )
  UpperCAmelCase__   ,      UpperCAmelCase__					:  int					   =					data[b], data[a]
 return data


if __name__ == "__main__":
      __UpperCAmelCase =				[0, 1, 2, 3, 4, 5, 6, 7]
      __UpperCAmelCase =				['python', 'says', 'hello', '!']
      print('Fisher-Yates Shuffle:')
      print('List', integers, strings)
      print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))





