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


UpperCAmelCase_			    =							1_6
UpperCAmelCase_			    =							3_2




def       lowerCAmelCase_     (		lowercase:							Accelerator  ,					lowercase:							int = 16  ,					lowercase:							str = "bert-base-cased"		)    ->				List[Any]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= AutoTokenizer.from_pretrained(snake_case__		)
 _UpperCamelCase:     List[Any]							= load_dataset('''glue'''  ,					'''mrpc'''		)

 def tokenize_function(lowercase:							List[str]		):
  # max_length=None => use the model max length (it's actually the default)
  _UpperCamelCase:     Optional[int]							= tokenizer(examples['''sentence1''']  ,					examples['''sentence2''']  ,					truncation=snake_case__  ,					max_length=snake_case__		)
  return outputs

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

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

 def collate_fn(lowercase:							int		):
  # 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(snake_case__  ,					padding='''max_length'''  ,					max_length=128  ,					return_tensors='''pt'''		)
  return tokenizer.pad(snake_case__  ,					padding='''longest'''  ,					return_tensors='''pt'''		)

 # Instantiate dataloaders.
 _UpperCamelCase:     Any							= DataLoader(
     tokenized_datasets['''train''']  ,					shuffle=snake_case__  ,					collate_fn=snake_case__  ,					batch_size=snake_case__		)
 _UpperCamelCase:     Any							= DataLoader(
     tokenized_datasets['''validation''']  ,					shuffle=snake_case__  ,					collate_fn=snake_case__  ,					batch_size=snake_case__		)

 return train_dataloader, eval_dataloader




def       lowerCAmelCase_     (		lowercase:							Dict  ,					lowercase:							List[Any]		)    ->				List[Any]:







 '''simple docstring'''
 # Initialize accelerator
 _UpperCamelCase:     int							= Accelerator()

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _UpperCamelCase:     List[Any]							= config['''lr''']
 _UpperCamelCase:     Optional[int]							= int(config['''num_epochs''']		)
 _UpperCamelCase:     str							= int(config['''seed''']		)
 _UpperCamelCase:     str							= int(config['''batch_size''']		)
 _UpperCamelCase:     Dict							= args.model_name_or_path

 set_seed(snake_case__		)
 _UpperCamelCase	,							_UpperCamelCase:     int							= get_dataloaders(snake_case__  ,					snake_case__  ,					snake_case__		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _UpperCamelCase:     Union[str, Any]							= AutoModelForSequenceClassification.from_pretrained(snake_case__  ,					return_dict=snake_case__		)

 # Instantiate optimizer
 _UpperCamelCase:     Any							= (
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 _UpperCamelCase:     Dict							= optimizer_cls(params=model.parameters()  ,					lr=snake_case__		)

 if accelerator.state.deepspeed_plugin is not None:
  _UpperCamelCase:     Any							= accelerator.state.deepspeed_plugin.deepspeed_config[
      '''gradient_accumulation_steps'''
  ]
 else:
  _UpperCamelCase:     Optional[Any]							= 1
 _UpperCamelCase:     int							= (len(snake_case__		) * 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:     int							= get_linear_schedule_with_warmup(
      optimizer=snake_case__  ,					num_warmup_steps=0  ,					num_training_steps=snake_case__  ,					)
 else:
  _UpperCamelCase:     List[str]							= DummyScheduler(snake_case__  ,					total_num_steps=snake_case__  ,					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:     Optional[Any]							= accelerator.prepare(
     snake_case__  ,					snake_case__  ,					snake_case__  ,					snake_case__  ,					snake_case__		)

 # We need to keep track of how many total steps we have iterated over
 _UpperCamelCase:     List[str]							= 0
 # We also need to keep track of the stating epoch so files are named properly
 _UpperCamelCase:     str							= 0

 # Now we train the model
 _UpperCamelCase:     str							= evaluate.load('''glue'''  ,					'''mrpc'''		)
 _UpperCamelCase:     List[Any]							= 0
 _UpperCamelCase:     List[Any]							= {}
 for epoch in range(snake_case__  ,					snake_case__		):
  model.train()
  for step, batch in enumerate(snake_case__		):
   _UpperCamelCase:     Union[str, Any]							= model(**snake_case__		)
   _UpperCamelCase:     List[str]							= outputs.loss
   _UpperCamelCase:     List[Any]							= loss / gradient_accumulation_steps
   accelerator.backward(snake_case__		)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1

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

  _UpperCamelCase:     Tuple							= metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""  ,					snake_case__		)
  _UpperCamelCase:     Optional[int]							= eval_metric['''accuracy''']

  if best_performance < eval_metric["accuracy"]:
   _UpperCamelCase:     str							= 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(snake_case__  ,					snake_case__		)




def       lowerCAmelCase_     (		)    ->				List[Any]:







 '''simple docstring'''
 _UpperCamelCase:     int							= argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
 parser.add_argument(
     '''--model_name_or_path'''  ,					type=snake_case__  ,					default='''bert-base-cased'''  ,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,					required=snake_case__  ,					)
 parser.add_argument(
     '''--output_dir'''  ,					type=snake_case__  ,					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=snake_case__  ,					default=snake_case__  ,					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=snake_case__  ,					default=3  ,					help='''Number of train epochs.'''  ,					)
 _UpperCamelCase:     Optional[Any]							= parser.parse_args()
 _UpperCamelCase:     Optional[Any]							= {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
 training_function(snake_case__  ,					snake_case__		)


if __name__ == "__main__":
  main()





from __future__ import annotations




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  Any		,  _lowercase					:  Dict=None							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= data
    _UpperCamelCase:     int							= None



   def __repr__(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     int							= []
    _UpperCamelCase:     str							= self
    while temp:
     string_rep.append(f"""{temp.data}"""							)
     _UpperCamelCase:     Optional[Any]							= temp.next
    return "->".join(_lowercase							)




def       lowerCAmelCase_     (		lowercase:							list		)    ->				Optional[int]:







 '''simple docstring'''
 if not elements_list:
  raise Exception('''The Elements List is empty'''		)

 _UpperCamelCase:     Optional[Any]							= Node(elements_list[0]		)
 for i in range(1  ,					len(lowercase		)		):
  _UpperCamelCase:     Union[str, Any]							= Node(elements_list[i]		)
  _UpperCamelCase:     Optional[int]							= current.next
 return head




def       lowerCAmelCase_     (		lowercase:							Node		)    ->				None:







 '''simple docstring'''
 if head_node is not None and isinstance(lowercase  ,					lowercase		):
  print_reverse(head_node.next		)
  print(head_node.data		)




def       lowerCAmelCase_     (		)    ->				Tuple:







 '''simple docstring'''
 from doctest import testmod

 testmod()

 _UpperCamelCase:     Any							= make_linked_list([14, 52, 14, 12, 43]		)
 print('''Linked List:'''		)
 print(lowercase		)
 print('''Elements in Reverse:'''		)
 print_reverse(lowercase		)


if __name__ == "__main__":
  main()