

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 						_lowercase					(	lowercase__  ,      lowercase__ = 1_6		):
      __lowerCAmelCase   :		Tuple   =   AutoTokenizer.from_pretrained('''bert-base-cased'''		)
      __lowerCAmelCase   :		Tuple   =   load_dataset('''glue'''  ,      '''mrpc'''		)

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

      def collate_fn(lowercase__		):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            __lowerCAmelCase   :		Optional[int]   =   1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  __lowerCAmelCase   :		int   =   1_6
            elif accelerator.mixed_precision != "no":
                  __lowerCAmelCase   :		Tuple   =   8
            else:
                  __lowerCAmelCase   :		Tuple   =   None

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

      # Instantiate dataloaders.
      __lowerCAmelCase   :		Any   =   DataLoader(
          tokenized_datasets['''train''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE		)
      __lowerCAmelCase   :		Dict   =   DataLoader(
          tokenized_datasets['''validation''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE		)

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

      __lowerCAmelCase   :		Tuple   =   evaluate.load('''glue'''  ,      '''mrpc'''		)

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

      # 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).
      __lowerCAmelCase   :		Dict   =   model.to(accelerator.device		)

      # Instantiate optimizer
      __lowerCAmelCase   :		int   =   AdamW(params=model.parameters()  ,      lr=__SCREAMING_SNAKE_CASE		)

      # Instantiate scheduler
      __lowerCAmelCase   :		str   =   get_linear_schedule_with_warmup(
          optimizer=__SCREAMING_SNAKE_CASE  ,      num_warmup_steps=1_0_0  ,      num_training_steps=(len(__SCREAMING_SNAKE_CASE		) * 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.
      __lowerCAmelCase   :		str   =   accelerator.prepare(
          __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE		)

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

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

            __lowerCAmelCase   :		int   =   metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"""epoch {epoch}:"""  ,      __SCREAMING_SNAKE_CASE		)







def 						_lowercase					(	):
      __lowerCAmelCase   :		Tuple   =   argparse.ArgumentParser(description='''Simple example of training script.'''		)
      parser.add_argument(
          '''--mixed_precision'''  ,      type=__SCREAMING_SNAKE_CASE  ,      default=__SCREAMING_SNAKE_CASE  ,      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=__SCREAMING_SNAKE_CASE  ,      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.'''		)
      __lowerCAmelCase   :		Any   =   parser.parse_args()
      __lowerCAmelCase   :		Dict   =   {'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
      training_function(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
 main()








'''simple docstring'''






from ..utils import DummyObject, requires_backends






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					Optional[Any]     ,							*__lowerCamelCase     :					Tuple     ,							**__lowerCamelCase     :					Optional[int]			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Tuple     ,							*__lowerCamelCase     :					str     ,							**__lowerCamelCase     :					Optional[int]			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Any     ,							*__lowerCamelCase     :					Dict     ,							**__lowerCamelCase     :					List[str]			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					int     ,							*__lowerCamelCase     :					str     ,							**__lowerCamelCase     :					Union[str, Any]			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Optional[Any]     ,							*__lowerCamelCase     :					List[str]     ,							**__lowerCamelCase     :					Optional[int]			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Optional[Any]     ,							*__lowerCamelCase     :					Optional[Any]     ,							**__lowerCamelCase     :					Optional[int]			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					List[str]     ,							*__lowerCamelCase     :					str     ,							**__lowerCamelCase     :					str			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Union[str, Any]     ,							*__lowerCamelCase     :					str     ,							**__lowerCamelCase     :					str			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Any     ,							*__lowerCamelCase     :					Tuple     ,							**__lowerCamelCase     :					Optional[int]			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					Optional[Any]     ,							*__lowerCamelCase     :					Optional[Any]     ,							**__lowerCamelCase     :					str			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					str     ,							*__lowerCamelCase     :					Dict     ,							**__lowerCamelCase     :					int			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Optional[Any]     ,							*__lowerCamelCase     :					Union[str, Any]     ,							**__lowerCamelCase     :					Union[str, Any]			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					Optional[int]     ,							*__lowerCamelCase     :					Tuple     ,							**__lowerCamelCase     :					Optional[Any]			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					List[Any]     ,							*__lowerCamelCase     :					Dict     ,							**__lowerCamelCase     :					int			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Tuple     ,							*__lowerCamelCase     :					List[str]     ,							**__lowerCamelCase     :					str			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					List[Any]     ,							*__lowerCamelCase     :					Dict     ,							**__lowerCamelCase     :					Union[str, Any]			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Any     ,							*__lowerCamelCase     :					Union[str, Any]     ,							**__lowerCamelCase     :					Optional[int]			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					List[Any]     ,							*__lowerCamelCase     :					Optional[Any]     ,							**__lowerCamelCase     :					Optional[int]			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					Dict     ,							*__lowerCamelCase     :					Union[str, Any]     ,							**__lowerCamelCase     :					Any			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					List[Any]     ,							*__lowerCamelCase     :					Optional[int]     ,							**__lowerCamelCase     :					List[str]			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Union[str, Any]     ,							*__lowerCamelCase     :					str     ,							**__lowerCamelCase     :					Any			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					Tuple     ,							*__lowerCamelCase     :					Union[str, Any]     ,							**__lowerCamelCase     :					Dict			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					List[Any]     ,							*__lowerCamelCase     :					Tuple     ,							**__lowerCamelCase     :					List[Any]			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					int     ,							*__lowerCamelCase     :					Union[str, Any]     ,							**__lowerCamelCase     :					str			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					Union[str, Any]     ,							*__lowerCamelCase     :					Optional[Any]     ,							**__lowerCamelCase     :					int			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Any     ,							*__lowerCamelCase     :					List[str]     ,							**__lowerCamelCase     :					int			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					List[str]     ,							*__lowerCamelCase     :					Optional[Any]     ,							**__lowerCamelCase     :					List[str]			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					Union[str, Any]     ,							*__lowerCamelCase     :					Optional[Any]     ,							**__lowerCamelCase     :					List[Any]			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Any     ,							*__lowerCamelCase     :					Optional[int]     ,							**__lowerCamelCase     :					int			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					str     ,							*__lowerCamelCase     :					Tuple     ,							**__lowerCamelCase     :					List[Any]			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					Optional[Any]     ,							*__lowerCamelCase     :					Union[str, Any]     ,							**__lowerCamelCase     :					Union[str, Any]			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Union[str, Any]     ,							*__lowerCamelCase     :					Tuple     ,							**__lowerCamelCase     :					Optional[Any]			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					str     ,							*__lowerCamelCase     :					Dict     ,							**__lowerCamelCase     :					Optional[int]			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					Union[str, Any]     ,							*__lowerCamelCase     :					List[Any]     ,							**__lowerCamelCase     :					Optional[int]			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Optional[int]     ,							*__lowerCamelCase     :					Tuple     ,							**__lowerCamelCase     :					Tuple			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Dict     ,							*__lowerCamelCase     :					Dict     ,							**__lowerCamelCase     :					Optional[Any]			):
           requires_backends(cls     ,							['flax']			)






class        lowercase_      (						metaclass=lowerCAmelCase_							):
     A_											=			["flax"]





     def __init__(     self     :					str     ,							*__lowerCamelCase     :					int     ,							**__lowerCamelCase     :					Dict			):
           requires_backends(self     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					List[str]     ,							*__lowerCamelCase     :					Optional[int]     ,							**__lowerCamelCase     :					Tuple			):
           requires_backends(cls     ,							['flax']			)





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Dict     ,							*__lowerCamelCase     :					Optional[int]     ,							**__lowerCamelCase     :					Optional[Any]			):
           requires_backends(cls     ,							['flax']			)
