


from typing import Any
class       UpperCAmelCase_   :

   '''simple docstring'''




   def __init__(						self		,							__A					):






     """simple docstring"""





     lowerCamelCase					:   List[Any]       =							data
     lowerCamelCase					:   Optional[Any]       =							None






   def __repr__(						self					):






     """simple docstring"""





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



class       UpperCAmelCase_   :

   '''simple docstring'''




   def __init__(						self					):






     """simple docstring"""





     lowerCamelCase					:   Any       =							None






   def __iter__(						self					):






     """simple docstring"""





     lowerCamelCase					:   Optional[int]       =							self.head
     while node:
       yield node.data
       lowerCamelCase					:   Dict       =							node.next






   def __len__(						self					):






     """simple docstring"""





     return sum(1 for _ in self					)






   def __repr__(						self					):






     """simple docstring"""





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






   def __getitem__(						self		,							__A					):






     """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		,							__A		,							__A					):






     """simple docstring"""





     if not 0 <= index < len(self					):
       raise ValueError("list index out of range."					)
     lowerCamelCase					:   List[str]       =							self.head
     for _ in range(A_					):
       lowerCamelCase					:   Any       =							current.next
     lowerCamelCase					:   List[str]       =							data






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     self.insert_nth(len(self					)		,							A_					)






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     self.insert_nth(0		,							A_					)






   def _snake_case       (						self		,							__A		,							__A					):






     """simple docstring"""





     if not 0 <= index <= len(self					):
       raise IndexError("list index out of range"					)
     lowerCamelCase					:   List[str]       =							Node(A_					)
     if self.head is None:
       lowerCamelCase					:   List[str]       =							new_node
     elif index == 0:
       lowerCamelCase					:   List[Any]       =							self.head  # link new_node to head
       lowerCamelCase					:   List[str]       =							new_node
     else:
       lowerCamelCase					:   Optional[Any]       =							self.head
       for _ in range(index - 1					):
         lowerCamelCase					:   Tuple       =							temp.next
       lowerCamelCase					:   int       =							temp.next
       lowerCamelCase					:   Union[str, Any]       =							new_node






   def _snake_case       (						self					):  # print every node data






     """simple docstring"""





     print(self					)






   def _snake_case       (						self					):






     """simple docstring"""





     return self.delete_nth(0					)






   def _snake_case       (						self					):  # delete from tail






     """simple docstring"""





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






   def _snake_case       (						self		,							__A = 0					):






     """simple docstring"""





     if not 0 <= index <= len(self					) - 1:  # test if index is valid
       raise IndexError("List index out of range."					)
     lowerCamelCase					:   Optional[int]       =							self.head  # default first node
     if index == 0:
       lowerCamelCase					:   Optional[int]       =							self.head.next
     else:
       lowerCamelCase					:   int       =							self.head
       for _ in range(index - 1					):
         lowerCamelCase					:   List[Any]       =							temp.next
       lowerCamelCase					:   List[str]       =							temp.next
       lowerCamelCase					:   Optional[Any]       =							temp.next.next
     return delete_node.data






   def _snake_case       (						self					):






     """simple docstring"""





     return self.head is None






   def _snake_case       (						self					):






     """simple docstring"""





     lowerCamelCase					:   Union[str, Any]       =							None
     lowerCamelCase					:   str       =							self.head

     while current:
       # Store the current node's next node.
       lowerCamelCase					:   str       =							current.next
       # Make the current node's next point backwards
       lowerCamelCase					:   int       =							prev
       # Make the previous node be the current node
       lowerCamelCase					:   str       =							current
       # Make the current node the next node (to progress iteration)
       lowerCamelCase					:   Union[str, Any]       =							next_node
     # Return prev in order to put the head at the end
     lowerCamelCase					:   Union[str, Any]       =							prev






def 						lowercase_(    ):


  '''simple docstring'''

  lowerCamelCase					:   Union[str, Any]       =							LinkedList()
  assert linked_list.is_empty() is True
  assert str(_lowercase      ) == ""

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

  linked_list.insert_head(0      )
  linked_list.insert_tail(11      )
  assert str(_lowercase      ) == "->".join(str(_lowercase      ) 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(_lowercase      ) == 9
  assert str(_lowercase      ) == "->".join(str(_lowercase      ) 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					:   Dict       =							-i
  assert all(linked_list[i] == -i for i in range(0 ,		9      )      ) is True

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






def 						lowercase_(    ):


  '''simple docstring'''

  lowerCamelCase					:   Union[str, Any]       =							[
      -9,
      100,
      Node(77345112      ),
      "dlrow olleH",
      7,
      5555,
      0,
      -192.55555,
      "Hello, world!",
      77.9,
      Node(10      ),
      None,
      None,
      12.20,
  ]
  lowerCamelCase					:   Optional[int]       =							LinkedList()

  for i in test_input:
    linked_list.insert_tail(_lowercase      )

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

  # Delete the tail
  lowerCamelCase					:   str       =							linked_list.delete_tail()
  assert result == 12.2
  assert (
      str(_lowercase      ) == "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					:   List[str]       =							linked_list.delete_nth(10      )
  assert result is None
  assert (
      str(_lowercase      ) == "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(_lowercase      )
      == "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(_lowercase      )
  assert (
      str(_lowercase      )
      == "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(_lowercase      )
      == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
      "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
  )






def 						lowercase_(    ):


  '''simple docstring'''

  from doctest import testmod

  testmod()

  lowerCamelCase					:   List[Any]       =							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(_lowercase      )
  print("\nReading/changing Node data using indexing:"      )
  print(f"""Element at Position 1: {linked_list[1]}"""      )
  lowerCamelCase					:   Union[str, Any]       =							input("Enter New Value: "      ).strip()
  print("New list:"      )
  print(_lowercase      )
  print(f"""length of linked_list is : {len(_lowercase      )}"""      )


if __name__ == "__main__":
   main()







import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__snake_case   							=						1_6
__snake_case   							=						3_2






def 		_A		(		_lowercase					,  _lowercase = 16					,  _lowercase = "bert-base-cased" )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			AutoTokenizer.from_pretrained(_lowercase )
     __UpperCamelCase =			load_dataset('glue'					,  'mrpc' )

     def tokenize_function(_lowercase ):
          # max_length=None => use the model max length (it's actually the default)
          __UpperCamelCase =			tokenizer(examples['sentence1']					,  examples['sentence2']					,  truncation=_lowercase					,  max_length=_lowercase )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     __UpperCamelCase =			datasets.map(
         _lowercase					,  batched=_lowercase					,  remove_columns=['idx', 'sentence1', 'sentence2']					,  load_from_cache_file=_lowercase )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     __UpperCamelCase =			tokenized_datasets.rename_column('label'					,  'labels' )

     def collate_fn(_lowercase ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(_lowercase					,  padding='max_length'					,  max_length=1_28					,  return_tensors='pt' )
          return tokenizer.pad(_lowercase					,  padding='longest'					,  return_tensors='pt' )

     # Instantiate dataloaders.
     __UpperCamelCase =			DataLoader(
         tokenized_datasets['train']					,  shuffle=_lowercase					,  collate_fn=_lowercase					,  batch_size=_lowercase )
     __UpperCamelCase =			DataLoader(
         tokenized_datasets['validation']					,  shuffle=_lowercase					,  collate_fn=_lowercase					,  batch_size=_lowercase )

     return train_dataloader, eval_dataloader






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __UpperCamelCase =			config['lr']
     __UpperCamelCase =			int(config['num_epochs'] )
     __UpperCamelCase =			int(config['seed'] )
     __UpperCamelCase =			int(config['batch_size'] )
     __UpperCamelCase =			args.model_name_or_path

     set_seed(_lowercase )
     __UpperCamelCase,    __UpperCamelCase =			get_dataloaders(_lowercase					,  _lowercase					,  _lowercase )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __UpperCamelCase =			AutoModelForSequenceClassification.from_pretrained(_lowercase					,  return_dict=_lowercase )

     # Instantiate optimizer
     __UpperCamelCase =			(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     __UpperCamelCase =			optimizer_cls(params=model.parameters()					,  lr=_lowercase )

     if accelerator.state.deepspeed_plugin is not None:
          __UpperCamelCase =			accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          __UpperCamelCase =			1
     __UpperCamelCase =			(len(_lowercase ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          __UpperCamelCase =			get_linear_schedule_with_warmup(
              optimizer=_lowercase					,  num_warmup_steps=0					,  num_training_steps=_lowercase					,  )
     else:
          __UpperCamelCase =			DummyScheduler(_lowercase					,  total_num_steps=_lowercase					,  warmup_num_steps=0 )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			accelerator.prepare(
         _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )

     # We need to keep track of how many total steps we have iterated over
     __UpperCamelCase =			0
     # We also need to keep track of the stating epoch so files are named properly
     __UpperCamelCase =			0

     # Now we train the model
     __UpperCamelCase =			evaluate.load('glue'					,  'mrpc' )
     __UpperCamelCase =			0
     __UpperCamelCase =			{}
     for epoch in range(_lowercase					,  _lowercase ):
          model.train()
          for step, batch in enumerate(_lowercase ):
               __UpperCamelCase =			model(**_lowercase )
               __UpperCamelCase =			outputs.loss
               __UpperCamelCase =			loss / gradient_accumulation_steps
               accelerator.backward(_lowercase )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          __UpperCamelCase =			0
          for step, batch in enumerate(_lowercase ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device )
               with torch.no_grad():
                    __UpperCamelCase =			model(**_lowercase )
               __UpperCamelCase =			outputs.logits.argmax(dim=-1 )
               # It is slightly faster to call this once, than multiple times
               __UpperCamelCase,    __UpperCamelCase =			accelerator.gather(
                   (predictions, batch['labels']) )  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(_lowercase ) - 1:
                         __UpperCamelCase =			predictions[: len(eval_dataloader.dataset ) - samples_seen]
                         __UpperCamelCase =			references[: len(eval_dataloader.dataset ) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=_lowercase					,  references=_lowercase					,  )

          __UpperCamelCase =			metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''					,  _lowercase )
          __UpperCamelCase =			eval_metric['accuracy']

          if best_performance < eval_metric["accuracy"]:
               __UpperCamelCase =			eval_metric['accuracy']

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir					,  'all_results.json' )					,  'w' ) as f:
               json.dump(_lowercase					,  _lowercase )






def 		_A		(		)					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
     parser.add_argument(
         '--model_name_or_path'					,  type=_lowercase					,  default='bert-base-cased'					,  help='Path to pretrained model or model identifier from huggingface.co/models.'					,  required=_lowercase					,  )
     parser.add_argument(
         '--output_dir'					,  type=_lowercase					,  default='.'					,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'					,  )
     parser.add_argument(
         '--performance_lower_bound'					,  type=_lowercase					,  default=_lowercase					,  help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'					,  )
     parser.add_argument(
         '--num_epochs'					,  type=_lowercase					,  default=3					,  help='Number of train epochs.'					,  )
     __UpperCamelCase =			parser.parse_args()
     __UpperCamelCase =			{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
     training_function(_lowercase					,  _lowercase )


if __name__ == "__main__":
    main()


