'''simple docstring'''




import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

__SCREAMING_SNAKE_CASE :  str  =						32

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Accelerator    ,			_UpperCAmelCase						:   int = 16					)					->					List[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						List[str]							   =					AutoTokenizer.from_pretrained("bert-base-cased"					)
       _UpperCAmelCase  :						int							   =					load_dataset("glue"    ,			"mrpc"					)

       def tokenize_function(_UpperCAmelCase						:   Dict					):
              # 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  :						List[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  :						Dict							   =					tokenized_datasets.rename_column("label"    ,			"labels"					)

       def collate_fn(_UpperCAmelCase						:   Dict					):
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     _UpperCAmelCase  :						Tuple							   =					16
              elif accelerator.mixed_precision != "no":
                     _UpperCAmelCase  :						str							   =					8
              else:
                     _UpperCAmelCase  :						str							   =					None

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

       # Instantiate dataloaders.
       _UpperCAmelCase  :						Dict							   =					DataLoader(
           tokenized_datasets["train"]    ,			shuffle=_UpperCAmelCase    ,			collate_fn=_UpperCAmelCase    ,			batch_size=_UpperCAmelCase					)
       _UpperCAmelCase  :						Optional[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

  __SCREAMING_SNAKE_CASE :  List[str]  =						mocked_dataloaders  # noqa: F811

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Any    ,			_UpperCAmelCase						:   Optional[int]					)					->					Tuple:


       """simple docstring"""







       if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,			_UpperCAmelCase					) == "1":
              _UpperCAmelCase  :						Tuple							   =					2
       # Initialize accelerator
       _UpperCAmelCase  :						Tuple							   =					Accelerator(cpu=args.cpu    ,			mixed_precision=args.mixed_precision					)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _UpperCAmelCase  :						Optional[int]							   =					config["lr"]
       _UpperCAmelCase  :						Union[str, Any]							   =					int(config["num_epochs"]					)
       _UpperCAmelCase  :						List[Any]							   =					int(config["seed"]					)
       _UpperCAmelCase  :						str							   =					int(config["batch_size"]					)

       _UpperCAmelCase  :						Dict							   =					evaluate.load("glue"    ,			"mrpc"					)

       # New Code #
       # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
       # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
       # half each time. From this, we can calculate the number of gradient accumulation steps needed
       # and modify the Accelerator object as a result
       @find_executable_batch_size(starting_batch_size=int(_UpperCAmelCase					)					)
       def inner_training_loop(_UpperCAmelCase						:   Union[str, Any]					):
              # Since we need to modify the outside accelerator object, we need to bring it
              # to the local scope
              nonlocal accelerator

              # We can calculate the number of gradient accumulation steps based on the current
              # batch size vs the starting batch size
              _UpperCAmelCase  :						Optional[int]							   =					observed_batch_size // batch_size

              # And then set it in the Accelerator directly:
              _UpperCAmelCase  :						Optional[int]							   =					num_gradient_accumulation_steps

              # Next we need to free all of the stored model references in the Accelerator each time
              accelerator.free_memory()

              # And set the seed so our results are reproducable each reset
              set_seed(_UpperCAmelCase					)

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              _UpperCAmelCase  :						List[Any]							   =					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  :						int							   =					model.to(accelerator.device					)

              # Instantiate optimizer
              _UpperCAmelCase  :						List[Any]							   =					AdamW(params=model.parameters()    ,			lr=_UpperCAmelCase					)
              _UpperCAmelCase  :						str							   =					get_dataloaders(_UpperCAmelCase    ,			_UpperCAmelCase					)

              # Instantiate scheduler
              _UpperCAmelCase  :						Optional[int]							   =					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  :						Dict							   =					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					):
                            # And perform gradient accumulation
                            with accelerator.accumulate(_UpperCAmelCase					):
                                   # We could avoid this line since we set the accelerator with `device_placement=True`.
                                   batch.to(accelerator.device					)
                                   _UpperCAmelCase  :						List[Any]							   =					model(**_UpperCAmelCase					)
                                   _UpperCAmelCase  :						List[str]							   =					outputs.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  :						int							   =					model(**_UpperCAmelCase					)
                            _UpperCAmelCase  :						List[Any]							   =					outputs.logits.argmax(dim=-1					)
                            _UpperCAmelCase  :						Tuple							   =					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					)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
       inner_training_loop()

def 			UpperCamelCase_							(      )					->					Any:


       """simple docstring"""







       _UpperCAmelCase  :						Dict							   =					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."    ,			)
       parser.add_argument("--cpu"    ,			action="store_true"    ,			help="If passed, will train on the CPU."					)
       _UpperCAmelCase  :						Any							   =					parser.parse_args()
       # New Code #
       # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
       _UpperCAmelCase  :						Any							   =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
       training_function(_UpperCAmelCase    ,			_UpperCAmelCase					)


if __name__ == "__main__":
  main()


'''simple docstring'''




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


__SCREAMING_SNAKE_CASE :  Any  =						TypeVar("""T""", bound="""TaskTemplate""")

@dataclass(frozen=snake_case__					)
class 			lowerCamelCase_			(abc.ABC					):
      '''simple docstring'''

      __UpperCamelCase:						str
      __UpperCamelCase:						ClassVar[Features]
      __UpperCamelCase:						ClassVar[Features]




      def   _A			( self		:       T		,      A		:       Features ):
             return copy.deepcopy(self )




      @property
      def   _A			( self		:       List[str] ):
             return Features(**self.input_schema		,      **self.label_schema )




      @property
      @abc.abstractmethod
      def   _A			( self		:       Optional[int] ):
             raise NotImplementedError





      @classmethod
      def   _A			( cls		:       Type[T]		,      A		:       dict ):
             _UpperCAmelCase  :						Optional[Any]							   =					{f.name for f in dataclasses.fields(cls )}
             return cls(**{k: v for k, v in template_dict.items() if k in field_names} )

