


'''simple docstring'''

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


a_  :				Tuple						= 16
a_  :				Tuple						= 32





def __snake_case					(      UpperCAmelCase_						:						Accelerator					,				UpperCAmelCase_						:						int = 16					,				UpperCAmelCase_						:						str = "bert-base-cased"		):
 lowerCamelCase_      =  AutoTokenizer.from_pretrained(UpperCAmelCase_		)
 lowerCamelCase_      =  load_dataset("glue"					,				"mrpc"		)

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

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

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

 def collate_fn(UpperCAmelCase_						:						str		):
  # 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(UpperCAmelCase_					,				padding="max_length"					,				max_length=128					,				return_tensors="pt"		)
  return tokenizer.pad(UpperCAmelCase_					,				padding="longest"					,				return_tensors="pt"		)

 # Instantiate dataloaders.
 lowerCamelCase_      =  DataLoader(
     tokenized_datasets["train"]					,				shuffle=UpperCAmelCase_					,				collate_fn=UpperCAmelCase_					,				batch_size=UpperCAmelCase_		)
 lowerCamelCase_      =  DataLoader(
     tokenized_datasets["validation"]					,				shuffle=UpperCAmelCase_					,				collate_fn=UpperCAmelCase_					,				batch_size=UpperCAmelCase_		)

 return train_dataloader, eval_dataloader





def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Any		):
 # Initialize accelerator
 lowerCamelCase_      =  Accelerator()

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

 set_seed(UpperCAmelCase_		)
 lowerCamelCase_ ,lowerCamelCase_      =  get_dataloaders(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

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

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

 if accelerator.state.deepspeed_plugin is not None:
  lowerCamelCase_      =  accelerator.state.deepspeed_plugin.deepspeed_config[
      "gradient_accumulation_steps"
  ]
 else:
  lowerCamelCase_      =  1
 lowerCamelCase_      =  (len(UpperCAmelCase_		) * num_epochs) // gradient_accumulation_steps

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

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

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

   overall_step += 1

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

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

  if best_performance < eval_metric["accuracy"]:
   lowerCamelCase_      =  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(UpperCAmelCase_					,				UpperCAmelCase_		)





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


if __name__ == "__main__":
     main()





'''simple docstring'''







_lowercase :  int    							=       [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       0
						while number:
												# Increased Speed Slightly by checking every 5 digits together.
												sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
												number //= 100000

						return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
_lowercase :  list[bool | None]    							=       [None] * 1_0_0_0_0_0_0_0
_lowercase :  List[str]    							=       True
_lowercase :  Optional[int]    							=       False
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						if CHAINS[number - 1] is not None:
												return CHAINS[number - 1]  # type: ignore

						lowercase_	:     Tuple													=       chain(next_number(__SCREAMING_SNAKE_CASE							)							)
						lowercase_	:     Union[str, Any]													=       number_chain

						while number < 10000000:
												lowercase_	:     int													=       number_chain
												number *= 10

						return number_chain
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int = 10000000							):



						"""simple docstring"""





						for i in range(1      ,      __SCREAMING_SNAKE_CASE							):
												if CHAINS[i] is None:
																		chain(i + 1							)

						return CHAINS[:number].count(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(f"""{solution() = }""")



