

"""simple docstring"""




import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase =				16
__UpperCAmelCase =				32
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase = 16      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Any					   =					AutoTokenizer.from_pretrained("""bert-base-cased"""      )
 UpperCAmelCase__					:  str					   =					load_dataset("""glue"""							,    """mrpc"""      )

 def tokenize_function(__UpperCamelCase      ):
  # max_length=None => use the model max length (it's actually the default)
  UpperCAmelCase__					:  Tuple					   =					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
 # starting with the main process first:
 with accelerator.main_process_first():
  UpperCAmelCase__					:  Any					   =					datasets.map(
      __UpperCamelCase							,    batched=__UpperCamelCase							,    remove_columns=["""idx""", """sentence1""", """sentence2"""]							,    )

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

 def collate_fn(__UpperCamelCase      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  UpperCAmelCase__					:  Dict					   =					128 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   UpperCAmelCase__					:  int					   =					16
  elif accelerator.mixed_precision != "no":
   UpperCAmelCase__					:  List[str]					   =					8
  else:
   UpperCAmelCase__					:  str					   =					None

  return tokenizer.pad(
      __UpperCamelCase							,    padding="""longest"""							,    max_length=__UpperCamelCase							,    pad_to_multiple_of=__UpperCamelCase							,    return_tensors="""pt"""							,    )

 # Instantiate dataloaders.
 UpperCAmelCase__					:  str					   =					DataLoader(
     tokenized_datasets["""train"""]							,    shuffle=__UpperCamelCase							,    collate_fn=__UpperCamelCase							,    batch_size=__UpperCamelCase      )
 UpperCAmelCase__					:  Union[str, Any]					   =					DataLoader(
     tokenized_datasets["""validation"""]							,    shuffle=__UpperCamelCase							,    collate_fn=__UpperCamelCase							,    batch_size=__UpperCamelCase      )

 return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      __UpperCAmelCase =				mocked_dataloaders  # noqa: F811
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,    __UpperCamelCase      ) == "1":
  UpperCAmelCase__					:  List[str]					   =					2
 # New Code #
 UpperCAmelCase__					:  Union[str, Any]					   =					int(args.gradient_accumulation_steps      )
 # Initialize accelerator
 UpperCAmelCase__					:  Dict					   =					Accelerator(
     cpu=args.cpu							,    mixed_precision=args.mixed_precision							,    gradient_accumulation_steps=__UpperCamelCase      )
 if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
  raise NotImplementedError(
      """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""      )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 UpperCAmelCase__					:  Optional[int]					   =					config["""lr"""]
 UpperCAmelCase__					:  List[Any]					   =					int(config["""num_epochs"""]      )
 UpperCAmelCase__					:  Any					   =					int(config["""seed"""]      )
 UpperCAmelCase__					:  Any					   =					int(config["""batch_size"""]      )

 UpperCAmelCase__					:  Tuple					   =					evaluate.load("""glue"""							,    """mrpc"""      )

 set_seed(__UpperCamelCase      )
 UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					get_dataloaders(__UpperCamelCase							,    __UpperCamelCase      )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCAmelCase__					:  Dict					   =					AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,    return_dict=__UpperCamelCase      )

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 UpperCAmelCase__					:  Union[str, Any]					   =					model.to(accelerator.device      )

 # Instantiate optimizer
 UpperCAmelCase__					:  Optional[int]					   =					AdamW(params=model.parameters()							,    lr=__UpperCamelCase      )

 # Instantiate scheduler
 UpperCAmelCase__					:  Tuple					   =					get_linear_schedule_with_warmup(
     optimizer=__UpperCamelCase							,    num_warmup_steps=100							,    num_training_steps=(len(__UpperCamelCase      ) * num_epochs)							,    )

 # 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__					:  List[Any]					   =					accelerator.prepare(
     __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      )

 # Now we train the model
 for epoch in range(__UpperCamelCase      ):
  model.train()
  for step, batch in enumerate(__UpperCamelCase      ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device      )
   # New code #
   # We use the new `accumulate` context manager to perform gradient accumulation
   # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
   with accelerator.accumulate(__UpperCamelCase      ):
    UpperCAmelCase__					:  Optional[Any]					   =					model(**__UpperCamelCase      )
    UpperCAmelCase__					:  Optional[Any]					   =					output.loss
    accelerator.backward(__UpperCamelCase      )
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  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():
    UpperCAmelCase__					:  Optional[Any]					   =					model(**__UpperCamelCase      )
   UpperCAmelCase__					:  Optional[int]					   =					outputs.logits.argmax(dim=-1      )
   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					accelerator.gather_for_metrics((predictions, batch["""labels"""])      )
   metric.add_batch(
       predictions=__UpperCamelCase							,    references=__UpperCamelCase							,    )

  UpperCAmelCase__					:  Optional[int]					   =					metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"							,    __UpperCamelCase      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					argparse.ArgumentParser(description="""Simple example of training script."""      )
 parser.add_argument(
     """--mixed_precision"""							,    type=__UpperCamelCase							,    default=__UpperCamelCase							,    choices=["""no""", """fp16""", """bf16""", """fp8"""]							,    help="""Whether to use mixed precision. Choose"""
     """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
     """and an Nvidia Ampere GPU."""							,    )
 # New Code #
 parser.add_argument(
     """--gradient_accumulation_steps"""							,    type=__UpperCamelCase							,    default=1							,    help="""The number of minibatches to be ran before gradients are accumulated."""							,    )
 parser.add_argument("""--cpu"""							,    action="""store_true"""							,    help="""If passed, will train on the CPU."""      )
 UpperCAmelCase__					:  int					   =					parser.parse_args()
 UpperCAmelCase__					:  Optional[int]					   =					{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(__UpperCamelCase							,    __UpperCamelCase      )


if __name__ == "__main__":
      main()








"""simple docstring"""




__UpperCAmelCase =				'Tobias Carryer'

from time import time







class      __lowercase			:

  def __init__(  self		:			Union[str, Any]					,A		:			Dict					,A		:			Optional[int]					,A		:			Union[str, Any]					,A		:			int=int(time()    )    ):  # noqa: B008






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					multiplier
   UpperCAmelCase__					:  Union[str, Any]					   =					increment
   UpperCAmelCase__					:  Dict					   =					modulo
   UpperCAmelCase__					:  Tuple					   =					seed




  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					(self.multiplier * self.seed + self.increment) % self.modulo
   return self.seed


if __name__ == "__main__":
      # Show the LCG in action.
      __UpperCAmelCase =				LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
      while True:
            print(lcg.next_number())





