






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


snake_case     					=		logging.getLogger(__name__)


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		List[str]								=						'''sequence-classification'''


     def __init__(					self							:						Optional[int]       ,			UpperCAmelCase_							:						Tuple   ):
       if type(UpperCAmelCase_   ) == dict:
         SCREAMING_SNAKE_CASE							:   List[str]						    =     Namespace(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     glue_output_modes[hparams.task]
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     glue_tasks_num_labels[hparams.task]

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


     def 					_A							(					self							:						List[str]       ,			**UpperCAmelCase_							:						str   ):
       return self.model(**UpperCAmelCase_   )


     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     outputs[0]

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


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.hparams
       SCREAMING_SNAKE_CASE							:   Tuple						    =     processors[args.task]()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     processor.get_labels()

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


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						bool = False   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     "dev" if mode == "test" else mode

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

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


     def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

       SCREAMING_SNAKE_CASE							:   str						    =     self(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Any						    =     outputs[:2]
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     logits.detach().cpu().numpy()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     inputs["labels"].detach().cpu().numpy()

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


     def 					_A							(					self							:						List[Any]       ,			UpperCAmelCase_							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     torch.stack([x["val_loss"] for x in outputs]   ).mean().detach().cpu().item()
       SCREAMING_SNAKE_CASE							:   Tuple						    =     np.concatenate([x["pred"] for x in outputs]       ,			axis=0   )

       if self.hparams.glue_output_mode == "classification":
         SCREAMING_SNAKE_CASE							:   List[str]						    =     np.argmax(UpperCAmelCase_       ,			axis=1   )
       elif self.hparams.glue_output_mode == "regression":
         SCREAMING_SNAKE_CASE							:   str						    =     np.squeeze(UpperCAmelCase_   )

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

       SCREAMING_SNAKE_CASE							:   Any						    =     {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )}

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     dict(results.items()   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     results
       return ret, preds_list, out_label_list


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						list   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self._eval_end(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     ret["log"]
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						List[str]   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   int						    =     self._eval_end(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   str						    =     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							(					UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						int   ):
       BaseTransformer.add_model_specific_args(UpperCAmelCase_       ,			UpperCAmelCase_   )
       parser.add_argument(
           "--max_seq_length"       ,			default=128       ,			type=UpperCAmelCase_       ,			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=UpperCAmelCase_       ,			required=UpperCAmelCase_       ,			help="The GLUE task to run"       ,			)
       parser.add_argument(
           "--gpus"       ,			default=0       ,			type=UpperCAmelCase_       ,			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"""






  SCREAMING_SNAKE_CASE							:   Tuple						    =     argparse.ArgumentParser()
  add_generic_args(lowercase    ,  os.getcwd()					)
  SCREAMING_SNAKE_CASE							:   int						    =     GLUETransformer.add_model_specific_args(lowercase    ,  os.getcwd()					)
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     parser.parse_args()

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

  SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     GLUETransformer(lowercase					)
  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     generic_train(lowercase    ,  lowercase					)

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


if __name__ == "__main__":
   main()













"""simple docstring"""
from typing import Any







class       a							:



 def __init__(     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= data
  lowerCAmelCase    				= None



 def __repr__(     self     ):







  """simple docstring"""






  return F'Node({self.data})'







class       a							:



 def __init__(     self     ):







  """simple docstring"""






  lowerCAmelCase    				= None



 def __iter__(     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.head
  while node:
   yield node.data
   lowerCAmelCase    				= node.next



 def __len__(     self     ):







  """simple docstring"""






  return sum(1 for _ in self     )



 def __repr__(     self     ):







  """simple docstring"""






  return "->".join([str(_snake_case     ) for item in self]     )



 def __getitem__(     self				,       _snake_case     ):







  """simple docstring"""






  if not 0 <= index < len(self     ):
   raise ValueError('list index out of range.'     )
  for i, node in enumerate(self     ):
   if i == index:
    return node
  return None



 def __setitem__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  if not 0 <= index < len(self     ):
   raise ValueError('list index out of range.'     )
  lowerCAmelCase    				= self.head
  for _ in range(_snake_case     ):
   lowerCAmelCase    				= current.next
  lowerCAmelCase    				= data



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  self.insert_nth(len(self     )				,       _snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  self.insert_nth(0				,       _snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  if not 0 <= index <= len(self     ):
   raise IndexError('list index out of range'     )
  lowerCAmelCase    				= Node(_snake_case     )
  if self.head is None:
   lowerCAmelCase    				= new_node
  elif index == 0:
   lowerCAmelCase    				= self.head  # link new_node to head
   lowerCAmelCase    				= new_node
  else:
   lowerCAmelCase    				= self.head
   for _ in range(index - 1     ):
    lowerCAmelCase    				= temp.next
   lowerCAmelCase    				= temp.next
   lowerCAmelCase    				= new_node



 def    UpperCamelCase__  (     self     ):  # print every node data







  """simple docstring"""






  print(self     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self.delete_nth(0     )



 def    UpperCamelCase__  (     self     ):  # delete from tail







  """simple docstring"""






  return self.delete_nth(len(self     ) - 1     )



 def    UpperCamelCase__  (     self				,       _snake_case = 0     ):







  """simple docstring"""






  if not 0 <= index <= len(self     ) - 1:  # test if index is valid
   raise IndexError('List index out of range.'     )
  lowerCAmelCase    				= self.head  # default first node
  if index == 0:
   lowerCAmelCase    				= self.head.next
  else:
   lowerCAmelCase    				= self.head
   for _ in range(index - 1     ):
    lowerCAmelCase    				= temp.next
   lowerCAmelCase    				= temp.next
   lowerCAmelCase    				= temp.next.next
  return delete_node.data



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self.head is None



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= None
  lowerCAmelCase    				= self.head

  while current:
   # Store the current node's next node.
   lowerCAmelCase    				= current.next
   # Make the current node's next point backwards
   lowerCAmelCase    				= prev
   # Make the previous node be the current node
   lowerCAmelCase    				= current
   # Make the current node the next node (to progress iteration)
   lowerCAmelCase    				= next_node
  # Return prev in order to put the head at the end
  lowerCAmelCase    				= prev


def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= LinkedList()
 assert linked_list.is_empty() is True
 assert str(_UpperCAmelCase			) == ""

 try:
  linked_list.delete_head()
  raise AssertionError  # This should not happen.
 except IndexError:
  assert True  # This should happen.

 try:
  linked_list.delete_tail()
  raise AssertionError  # This should not happen.
 except IndexError:
  assert True  # This should happen.

 for i in range(10			):
  assert len(_UpperCAmelCase			) == i
  linked_list.insert_nth(_UpperCAmelCase  ,     i + 1			)
 assert str(_UpperCAmelCase			) == "->".join(str(_UpperCAmelCase			) for i in range(1  ,     11			)			)

 linked_list.insert_head(0			)
 linked_list.insert_tail(11			)
 assert str(_UpperCAmelCase			) == "->".join(str(_UpperCAmelCase			) for i in range(0  ,     12			)			)

 assert linked_list.delete_head() == 0
 assert linked_list.delete_nth(9			) == 10
 assert linked_list.delete_tail() == 11
 assert len(_UpperCAmelCase			) == 9
 assert str(_UpperCAmelCase			) == "->".join(str(_UpperCAmelCase			) for i in range(1  ,     10			)			)

 assert all(linked_list[i] == i + 1 for i in range(0  ,     9			)			) is True

 for i in range(0  ,     9			):
  lowerCAmelCase    				= -i
 assert all(linked_list[i] == -i for i in range(0  ,     9			)			) is True

 linked_list.reverse()
 assert str(_UpperCAmelCase			) == "->".join(str(_UpperCAmelCase			) for i in range(-8  ,     1			)			)

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= [
     -9,
     100,
     Node(7734_5112			),
     'dlrow olleH',
     7,
     5555,
     0,
     -192.5_5555,
     'Hello, world!',
     77.9,
     Node(10			),
     None,
     None,
     12.20,
 ]
 lowerCAmelCase    				= LinkedList()

 for i in test_input:
  linked_list.insert_tail(_UpperCAmelCase			)

 # Check if it's empty or not
 assert linked_list.is_empty() is False
 assert (
     str(_UpperCAmelCase			) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
     "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
 )

 # Delete the head
 lowerCAmelCase    				= linked_list.delete_head()
 assert result == -9
 assert (
     str(_UpperCAmelCase			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
     "Hello, world!->77.9->Node(10)->None->None->12.2"
 )

 # Delete the tail
 lowerCAmelCase    				= linked_list.delete_tail()
 assert result == 12.2
 assert (
     str(_UpperCAmelCase			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
     "Hello, world!->77.9->Node(10)->None->None"
 )

 # Delete a node in specific location in linked list
 lowerCAmelCase    				= linked_list.delete_nth(10			)
 assert result is None
 assert (
     str(_UpperCAmelCase			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
     "Hello, world!->77.9->Node(10)->None"
 )

 # Add a Node instance to its head
 linked_list.insert_head(Node('Hello again, world!'			)			)
 assert (
     str(_UpperCAmelCase			)
     == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
     "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
 )

 # Add None to its tail
 linked_list.insert_tail(_UpperCAmelCase			)
 assert (
     str(_UpperCAmelCase			)
     == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
     "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
 )

 # Reverse the linked list
 linked_list.reverse()
 assert (
     str(_UpperCAmelCase			)
     == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
     "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
 )

def 		_SCREAMING_SNAKE_CASE			():
 from doctest import testmod

 testmod()

 lowerCAmelCase    				= LinkedList()
 linked_list.insert_head(input('Inserting 1st at head '			).strip()			)
 linked_list.insert_head(input('Inserting 2nd at head '			).strip()			)
 print('\nPrint list:'			)
 linked_list.print_list()
 linked_list.insert_tail(input('\nInserting 1st at tail '			).strip()			)
 linked_list.insert_tail(input('Inserting 2nd at tail '			).strip()			)
 print('\nPrint list:'			)
 linked_list.print_list()
 print('\nDelete head'			)
 linked_list.delete_head()
 print('Delete tail'			)
 linked_list.delete_tail()
 print('\nPrint list:'			)
 linked_list.print_list()
 print('\nReverse linked list'			)
 linked_list.reverse()
 print('\nPrint list:'			)
 linked_list.print_list()
 print('\nString representation of linked list:'			)
 print(_UpperCAmelCase			)
 print('\nReading/changing Node data using indexing:'			)
 print(F'Element at Position 1: {linked_list[1]}'			)
 lowerCAmelCase    				= input('Enter New Value: '			).strip()
 print('New list:'			)
 print(_UpperCAmelCase			)
 print(F'length of linked_list is : {len(_UpperCAmelCase			)}'			)


if __name__ == "__main__":
       main()





