

"""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
#
########################################################################


a					:int    = 16
a					:List[str]    = 32




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase = 16							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Any      		=	AutoTokenizer.from_pretrained("""bert-base-cased"""							)
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	load_dataset("""glue"""  ,					"""mrpc"""							)

      def tokenize_function(__lowerCAmelCase							):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE__   :      int      		=	tokenizer(examples["""sentence1"""]  ,					examples["""sentence2"""]  ,					truncation=__lowerCAmelCase  ,					max_length=__lowerCAmelCase							)
            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():
            SCREAMING_SNAKE_CASE__   :      Dict      		=	datasets.map(
                __lowerCAmelCase  ,					batched=__lowerCAmelCase  ,					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
      SCREAMING_SNAKE_CASE__   :      str      		=	tokenized_datasets.rename_column("""label"""  ,					"""labels"""							)

      def collate_fn(__lowerCAmelCase							):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	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":
                  SCREAMING_SNAKE_CASE__   :      int      		=	16
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	8
            else:
                  SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	None

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

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

      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

  a					:Union[str, Any]    = mocked_dataloaders  # noqa: F811




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				List[str]:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""  ,					__lowerCAmelCase							) == "1":
            SCREAMING_SNAKE_CASE__   :      str      		=	2
      # New Code #
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	int(args.gradient_accumulation_steps							)
      # Initialize accelerator
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	Accelerator(
          cpu=args.cpu  ,					mixed_precision=args.mixed_precision  ,					gradient_accumulation_steps=__lowerCAmelCase							)
      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
      SCREAMING_SNAKE_CASE__   :      int      		=	config["""lr"""]
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	int(config["""num_epochs"""]							)
      SCREAMING_SNAKE_CASE__   :      int      		=	int(config["""seed"""]							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	int(config["""batch_size"""]							)

      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	evaluate.load("""glue"""  ,					"""mrpc"""							)

      set_seed(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[str]      		=	get_dataloaders(__lowerCAmelCase  ,					__lowerCAmelCase							)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE__   :      int      		=	AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""  ,					return_dict=__lowerCAmelCase							)

      # 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).
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model.to(accelerator.device							)

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	AdamW(params=model.parameters()  ,					lr=__lowerCAmelCase							)

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	get_linear_schedule_with_warmup(
          optimizer=__lowerCAmelCase  ,					num_warmup_steps=100  ,					num_training_steps=(len(__lowerCAmelCase							) * 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.
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[Any]      		=	accelerator.prepare(
          __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)

      # Now we train the model
      for epoch in range(__lowerCAmelCase							):
            model.train()
            for step, batch in enumerate(__lowerCAmelCase							):
                  # 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(__lowerCAmelCase							):
                        SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(**__lowerCAmelCase							)
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	output.loss
                        accelerator.backward(__lowerCAmelCase							)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(__lowerCAmelCase							):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device							)
                  with torch.no_grad():
                        SCREAMING_SNAKE_CASE__   :      Any      		=	model(**__lowerCAmelCase							)
                  SCREAMING_SNAKE_CASE__   :      Dict      		=	outputs.logits.argmax(dim=-1							)
                  SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Tuple      		=	accelerator.gather_for_metrics((predictions, batch["""labels"""])							)
                  metric.add_batch(
                      predictions=__lowerCAmelCase  ,					references=__lowerCAmelCase  ,					)

            SCREAMING_SNAKE_CASE__   :      List[str]      		=	metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''  ,					__lowerCAmelCase							)




def  _lowercase					(    )       ->				Tuple:
      SCREAMING_SNAKE_CASE__   :      str      		=	argparse.ArgumentParser(description="""Simple example of training script."""							)
      parser.add_argument(
          """--mixed_precision"""  ,					type=__lowerCAmelCase  ,					default=__lowerCAmelCase  ,					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=__lowerCAmelCase  ,					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."""							)
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	parser.parse_args()
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(__lowerCAmelCase  ,					__lowerCAmelCase							)


if __name__ == "__main__":
  main()







"""simple docstring"""


class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	value
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	weight

     def __repr__(						self  )  ->      List[Any]:


           """simple docstring"""


           return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return self.name

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           return self.weight





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value / self.weight





def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for i in range(len(__lowerCAmelCase							)							):
            menu.append(Things(name[i]  ,					value[i]  ,					weight[i]							)							)
      return menu




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sorted(__lowerCAmelCase  ,					key=__lowerCAmelCase  ,					reverse=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      str      		=	[]
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0.0, 0.0
      for i in range(len(__lowerCAmelCase							)							):
            if (total_cost + items_copy[i].get_weight()) <= max_cost:
                  result.append(items_copy[i]							)
                  total_cost += items_copy[i].get_weight()
                  total_value += items_copy[i].get_value()
      return (result, total_value)




def  _lowercase					(    )       ->				List[str]:
      pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()




