

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
#
########################################################################


_SCREAMING_SNAKE_CASE  :						List[Any]         =    16
_SCREAMING_SNAKE_CASE  :						List[Any]         =    32
def        UpperCAmelCase_     (		_A   ,	_A = 16       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					AutoTokenizer.from_pretrained('''bert-base-cased'''       )
     SCREAMING_SNAKE_CASE__					=					load_dataset('''glue'''   ,	'''mrpc'''       )

     def tokenize_function(_A       ):
          # max_length=None => use the model max length (it's actually the default)
          SCREAMING_SNAKE_CASE__					=					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
     # starting with the main process first:
     with accelerator.main_process_first():
          SCREAMING_SNAKE_CASE__					=					datasets.map(
              _A   ,	batched=_A   ,	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__					=					tokenized_datasets.rename_column('''label'''   ,	'''labels'''       )

     def collate_fn(_A       ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          SCREAMING_SNAKE_CASE__					=					1_28 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__					=					16
          elif accelerator.mixed_precision != "no":
               SCREAMING_SNAKE_CASE__					=					8
          else:
               SCREAMING_SNAKE_CASE__					=					None

          return tokenizer.pad(
              _A   ,	padding='''longest'''   ,	max_length=_A   ,	pad_to_multiple_of=_A   ,	return_tensors='''pt'''   ,	)

     # Instantiate dataloaders.
     SCREAMING_SNAKE_CASE__					=					DataLoader(
         tokenized_datasets['''train''']   ,	shuffle=_A   ,	collate_fn=_A   ,	batch_size=_A       )
     SCREAMING_SNAKE_CASE__					=					DataLoader(
         tokenized_datasets['''validation''']   ,	shuffle=_A   ,	collate_fn=_A   ,	batch_size=_A       )

     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  :						Optional[Any]         =    mocked_dataloaders  # noqa: F811
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,	_A       ) == "1":
          SCREAMING_SNAKE_CASE__					=					2
     # New Code #
     SCREAMING_SNAKE_CASE__					=					int(args.gradient_accumulation_steps       )
     # Initialize accelerator
     SCREAMING_SNAKE_CASE__					=					Accelerator(
         cpu=args.cpu   ,	mixed_precision=args.mixed_precision   ,	gradient_accumulation_steps=_A       )
     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__					=					config['''lr''']
     SCREAMING_SNAKE_CASE__					=					int(config['''num_epochs''']       )
     SCREAMING_SNAKE_CASE__					=					int(config['''seed''']       )
     SCREAMING_SNAKE_CASE__					=					int(config['''batch_size''']       )

     SCREAMING_SNAKE_CASE__					=					evaluate.load('''glue'''   ,	'''mrpc'''       )

     set_seed(_A       )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					get_dataloaders(_A   ,	_A       )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     SCREAMING_SNAKE_CASE__					=					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   ,	return_dict=_A       )

     # 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__					=					model.to(accelerator.device       )

     # Instantiate optimizer
     SCREAMING_SNAKE_CASE__					=					AdamW(params=model.parameters()   ,	lr=_A       )

     # Instantiate scheduler
     SCREAMING_SNAKE_CASE__					=					get_linear_schedule_with_warmup(
         optimizer=_A   ,	num_warmup_steps=1_00   ,	num_training_steps=(len(_A       ) * 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__					=					accelerator.prepare(
         _A   ,	_A   ,	_A   ,	_A   ,	_A       )

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

          model.eval()
          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():
                    SCREAMING_SNAKE_CASE__					=					model(**_A       )
               SCREAMING_SNAKE_CASE__					=					outputs.logits.argmax(dim=-1       )
               SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
               metric.add_batch(
                   predictions=_A   ,	references=_A   ,	)

          SCREAMING_SNAKE_CASE__					=					metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''   ,	_A       )
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					argparse.ArgumentParser(description='''Simple example of training script.'''       )
     parser.add_argument(
         '''--mixed_precision'''   ,	type=_A   ,	default=_A   ,	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=_A   ,	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__					=					parser.parse_args()
     SCREAMING_SNAKE_CASE__					=					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_A   ,	_A       )


if __name__ == "__main__":
       main()





from importlib import import_module

from .logging import get_logger


_SCREAMING_SNAKE_CASE  :						Optional[int]         =    get_logger(__name__)






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Optional[int]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Any=None      )	->							int:
       SCREAMING_SNAKE_CASE__					=					attrs or []
       if module is not None:
            for key in module.__dict__:
                 if key in attrs or not key.startswith('''__'''      ):
                      setattr(self			,     __lowerCamelCase			,     getattr(__lowerCamelCase			,     __lowerCamelCase      )      )
       SCREAMING_SNAKE_CASE__					=					module._original_module if isinstance(__lowerCamelCase			,     _PatchedModuleObj      ) else module






class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      []






  def __init__(							self		:	int			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	List[Any]=None      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					obj
       SCREAMING_SNAKE_CASE__					=					target
       SCREAMING_SNAKE_CASE__					=					new
       SCREAMING_SNAKE_CASE__					=					target.split('''.'''      )[0]
       SCREAMING_SNAKE_CASE__					=					{}
       SCREAMING_SNAKE_CASE__					=					attrs or []






  def __enter__(							self		:	int      )	->							Tuple:
       *SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.target.split('''.'''      )

       # Patch modules:
       # it's used to patch attributes of submodules like "os.path.join";
       # in this case we need to patch "os" and "os.path"

       for i in range(len(__lowerCamelCase      )      ):
            try:
                 SCREAMING_SNAKE_CASE__					=					import_module('''.'''.join(submodules[: i + 1]      )      )
            except ModuleNotFoundError:
                 continue
            # We iterate over all the globals in self.obj in case we find "os" or "os.path"
            for attr in self.obj.__dir__():
                 SCREAMING_SNAKE_CASE__					=					getattr(self.obj			,     __lowerCamelCase      )
                 # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
                 # This allows to patch renamed modules like "from os import path as ospath".
                 if obj_attr is submodule or (
                     (isinstance(__lowerCamelCase			,     _PatchedModuleObj      ) and obj_attr._original_module is submodule)
                 ):
                      SCREAMING_SNAKE_CASE__					=					obj_attr
                      # patch at top level
                      setattr(self.obj			,     __lowerCamelCase			,     _PatchedModuleObj(__lowerCamelCase			,     attrs=self.attrs      )      )
                      SCREAMING_SNAKE_CASE__					=					getattr(self.obj			,     __lowerCamelCase      )
                      # construct lower levels patches
                      for key in submodules[i + 1 :]:
                           setattr(__lowerCamelCase			,     __lowerCamelCase			,     _PatchedModuleObj(getattr(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )			,     attrs=self.attrs      )      )
                           SCREAMING_SNAKE_CASE__					=					getattr(__lowerCamelCase			,     __lowerCamelCase      )
                      # finally set the target attribute
                      setattr(__lowerCamelCase			,     __lowerCamelCase			,     self.new      )

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

       if submodules:  # if it's an attribute of a submodule like "os.path.join"
            try:
                 SCREAMING_SNAKE_CASE__					=					getattr(import_module('''.'''.join(__lowerCamelCase      )      )			,     __lowerCamelCase      )
            except (AttributeError, ModuleNotFoundError):
                 return
            # We iterate over all the globals in self.obj in case we find "os.path.join"
            for attr in self.obj.__dir__():
                 # We don't check for the name of the global, but rather if its value *is* "os.path.join".
                 # This allows to patch renamed attributes like "from os.path import join as pjoin".
                 if getattr(self.obj			,     __lowerCamelCase      ) is attr_value:
                      SCREAMING_SNAKE_CASE__					=					getattr(self.obj			,     __lowerCamelCase      )
                      setattr(self.obj			,     __lowerCamelCase			,     self.new      )
       elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
            SCREAMING_SNAKE_CASE__					=					globals()['''__builtins__'''][target_attr]
            setattr(self.obj			,     __lowerCamelCase			,     self.new      )
       else:
            raise RuntimeError(f'''Tried to patch attribute {target_attr} instead of a submodule.'''      )






  def __exit__(							self		:	Optional[Any]			,     *__lowerCamelCase		:	Tuple      )	->							List[str]:
       for attr in list(self.original      ):
            setattr(self.obj			,     __lowerCamelCase			,     self.original.pop(__lowerCamelCase      )      )






  def    lowercase_	(							self		:	List[Any]      )	->							Optional[Any]:
       self.__enter__()
       self._active_patches.append(self      )







  def    lowercase_	(							self		:	int      )	->							int:
       try:
            self._active_patches.remove(self      )
       except ValueError:
            # If the patch hasn't been started this will fail
            return None

       return self.__exit__()


