




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_									=	16
UpperCAmelCase_									=	32




def 					lowerCamelCase__       (						A__       :					Accelerator  ,			A__       :					int = 16  ,			A__       :					str = "bert-base-cased"	):





       '''simple docstring'''



       __lowerCamelCase   =						AutoTokenizer.from_pretrained(A__	)
       __lowerCamelCase   =						load_dataset("""glue"""  ,			"""mrpc"""	)

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

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

       # 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(A__       :					Optional[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(A__  ,			padding="""max_length"""  ,			max_length=128  ,			return_tensors="""pt"""	)
              return tokenizer.pad(A__  ,			padding="""longest"""  ,			return_tensors="""pt"""	)

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

       return train_dataloader, eval_dataloader




def 					lowerCamelCase__       (						A__       :					Tuple  ,			A__       :					Union[str, Any]  ,			A__       :					Tuple  ,			A__       :					Optional[Any]	):





       '''simple docstring'''



       model.eval()
       __lowerCamelCase   =						0
       for step, batch in enumerate(A__	):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              batch.to(accelerator.device	)
              with torch.no_grad():
                     __lowerCamelCase   =						model(**A__	)
              __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(A__	) - 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=A__  ,			references=A__  ,			)

       __lowerCamelCase   =						metric.compute()
       return eval_metric["accuracy"]




def 					lowerCamelCase__       (						A__       :					Optional[Any]  ,			A__       :					Optional[int]	):





       '''simple docstring'''



       __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(A__	)
       __lowerCamelCase,   __lowerCamelCase   =						get_dataloaders(A__  ,			A__  ,			A__	)

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

       # 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=A__	)

       if accelerator.state.deepspeed_plugin is not None:
              __lowerCamelCase   =						accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              __lowerCamelCase   =						1
       __lowerCamelCase   =						(len(A__	) * 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=A__  ,			num_warmup_steps=0  ,			num_training_steps=A__  ,			)
       else:
              __lowerCamelCase   =						DummyScheduler(A__  ,			total_num_steps=A__  ,			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(
           A__  ,			A__  ,			A__  ,			A__  ,			A__	)

       # 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
       __lowerCamelCase   =						evaluate.load("""glue"""  ,			"""mrpc"""	)
       __lowerCamelCase   =						num_epochs

       if args.partial_train_epoch is not None:
              __lowerCamelCase   =						args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint	)
              __lowerCamelCase   =						args.resume_from_checkpoint.split("""epoch_"""	)[1]
              __lowerCamelCase   =						""""""
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              __lowerCamelCase   =						int(A__	) + 1
              __lowerCamelCase   =						evaluation_loop(A__  ,			A__  ,			A__  ,			A__	)
              accelerator.print("""resumed checkpoint performance:"""  ,			A__	)
              accelerator.print("""resumed checkpoint's scheduler's lr:"""  ,			lr_scheduler.get_lr()[0]	)
              accelerator.print("""resumed optimizers's lr:"""  ,			optimizer.param_groups[0]["""lr"""]	)
              with open(os.path.join(args.output_dir  ,			f'state_{starting_epoch-1}.json'	)  ,			"""r"""	) as f:
                     __lowerCamelCase   =						json.load(A__	)
                     assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["lr"] == lr_scheduler.get_lr()[0]
                     ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                     ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                     assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                     return

    # Now we train the model
       __lowerCamelCase   =						{}
       for epoch in range(A__  ,			A__	):
              model.train()
              for step, batch in enumerate(A__	):
                     __lowerCamelCase   =						model(**A__	)
                     __lowerCamelCase   =						outputs.loss
                     __lowerCamelCase   =						loss / gradient_accumulation_steps
                     accelerator.backward(A__	)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              __lowerCamelCase   =						f'epoch_{epoch}'
              __lowerCamelCase   =						os.path.join(args.output_dir  ,			A__	)
              accelerator.save_state(A__	)
              __lowerCamelCase   =						evaluation_loop(A__  ,			A__  ,			A__  ,			A__	)
              __lowerCamelCase   =						accuracy
              __lowerCamelCase   =						lr_scheduler.get_lr()[0]
              __lowerCamelCase   =						optimizer.param_groups[0]["""lr"""]
              __lowerCamelCase   =						epoch
              __lowerCamelCase   =						overall_step
              accelerator.print(f'epoch {epoch}:'  ,			A__	)

              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     with open(os.path.join(args.output_dir  ,			f'state_{epoch}.json'	)  ,			"""w"""	) as f:
                            json.dump(A__  ,			A__	)




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""	)
       parser.add_argument(
           """--model_name_or_path"""  ,			type=A__  ,			default="""bert-base-cased"""  ,			help="""Path to pretrained model or model identifier from huggingface.co/models."""  ,			required=A__  ,			)
       parser.add_argument(
           """--output_dir"""  ,			type=A__  ,			default="""."""  ,			help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""  ,			)
       parser.add_argument(
           """--resume_from_checkpoint"""  ,			type=A__  ,			default=A__  ,			help="""If the training should continue from a checkpoint folder."""  ,			)
       parser.add_argument(
           """--partial_train_epoch"""  ,			type=A__  ,			default=A__  ,			help="""If passed, the training will stop after this number of epochs."""  ,			)
       parser.add_argument(
           """--num_epochs"""  ,			type=A__  ,			default=2  ,			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(A__  ,			A__	)


if __name__ == "__main__":
       main()


"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  while a != 0:
    a__							,    a__:     List[str]										=	b % a, a
  return b







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  if gcd(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) != 1:
    a__:     Dict										=	F'mod inverse of {a!r} and {m!r} does not exist'
    raise ValueError(_SCREAMING_SNAKE_CASE   )
  a__							,    a__							,    a__:     Union[str, Any]										=	1, 0, a
  a__							,    a__							,    a__:     Any										=	0, 1, m
  while va != 0:
    a__:     int										=	ua // va
    a__							,    a__							,    a__							,    a__							,    a__							,    a__:     Any										=	(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
  return ua % m


