






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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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					=	16
__A					=	32







def       lowercase__	(					A_:						Accelerator  ,      A_:						int = 16		)	->				Union[str, Any]:







       """simple docstring"""






       __UpperCAmelCase     				=AutoTokenizer.from_pretrained("""bert-base-cased"""		)
       __UpperCAmelCase     				=load_dataset("""glue"""  ,      """mrpc"""		)

       def tokenize_function(A_:						int		):
              # max_length=None => use the model max length (it's actually the default)
              __UpperCAmelCase     				=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():
              __UpperCAmelCase     				=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
       __UpperCAmelCase     				=tokenized_datasets.rename_column("""label"""  ,      """labels"""		)

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

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

       # Instantiate dataloaders.
       __UpperCAmelCase     				=DataLoader(
           tokenized_datasets["""train"""]  ,      shuffle=A_  ,      collate_fn=A_  ,      batch_size=A_		)
       __UpperCAmelCase     				=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

   __A					=	mocked_dataloaders  # noqa: F811







def       lowercase__	(					A_:						List[str]  ,      A_:						Optional[Any]		)	->				List[str]:







       """simple docstring"""






       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""  ,      A_		) == "1":
              __UpperCAmelCase     				=2
       # New Code #
       __UpperCAmelCase     				=int(args.gradient_accumulation_steps		)
       __UpperCAmelCase     				=int(args.local_sgd_steps		)
       # Initialize accelerator
       __UpperCAmelCase     				=Accelerator(
           cpu=args.cpu  ,      mixed_precision=args.mixed_precision  ,      gradient_accumulation_steps=A_		)
       if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
              raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""		)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       __UpperCAmelCase     				=config["""lr"""]
       __UpperCAmelCase     				=int(config["""num_epochs"""]		)
       __UpperCAmelCase     				=int(config["""seed"""]		)
       __UpperCAmelCase     				=int(config["""batch_size"""]		)

       __UpperCAmelCase     				=evaluate.load("""glue"""  ,      """mrpc"""		)

       set_seed(A_		)
       __UpperCAmelCase				, __UpperCAmelCase     				=get_dataloaders(A_  ,      A_		)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       __UpperCAmelCase     				=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).
       __UpperCAmelCase     				=model.to(accelerator.device		)

       # Instantiate optimizer
       __UpperCAmelCase     				=AdamW(params=model.parameters()  ,      lr=A_		)

       # Instantiate scheduler
       __UpperCAmelCase     				=get_linear_schedule_with_warmup(
           optimizer=A_  ,      num_warmup_steps=100  ,      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.
       __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=accelerator.prepare(
           A_  ,      A_  ,      A_  ,      A_  ,      A_		)

       # Now we train the model
       for epoch in range(A_		):
              model.train()
              with LocalSGD(
                  accelerator=A_  ,      model=A_  ,      local_sgd_steps=A_  ,      enabled=local_sgd_steps is not None		) as local_sgd:
                     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_		):
                                   __UpperCAmelCase     				=model(**A_		)
                                   __UpperCAmelCase     				=output.loss
                                   accelerator.backward(A_		)
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()
                                   # LocalSGD-specific line
                                   local_sgd.step()

              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():
                            __UpperCAmelCase     				=model(**A_		)
                     __UpperCAmelCase     				=outputs.logits.argmax(dim=-1		)
                     __UpperCAmelCase				, __UpperCAmelCase     				=accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
                     metric.add_batch(
                         predictions=A_  ,      references=A_  ,      )

              __UpperCAmelCase     				=metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:'''  ,      A_		)







def       lowercase__	(					)	->				Tuple:







       """simple docstring"""






       __UpperCAmelCase     				=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(
           """--local_sgd_steps"""  ,      type=A_  ,      default=8  ,      help="""Number of local SGD steps or None to disable local SGD"""		)
       parser.add_argument("""--cpu"""  ,      action="""store_true"""  ,      help="""If passed, will train on the CPU."""		)
       __UpperCAmelCase     				=parser.parse_args()
       __UpperCAmelCase     				={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(A_  ,      A_		)


if __name__ == "__main__":
   main()











from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Optional[int]       =	"""canine"""






		def __init__(							self,   snake_case__=7_68,   snake_case__=12,   snake_case__=12,   snake_case__=30_72,   snake_case__="gelu",   snake_case__=0.1,   snake_case__=0.1,   snake_case__=1_63_84,   snake_case__=16,   snake_case__=0.02,   snake_case__=1E-12,   snake_case__=0,   snake_case__=0XE_0_0_0,   snake_case__=0XE_0_0_1,   snake_case__=4,   snake_case__=4,   snake_case__=8,   snake_case__=1_63_84,   snake_case__=1_28,   **snake_case__,   )       -> Optional[int]:






									"""simple docstring"""
									super().__init__(pad_token_id=snake_case__,   bos_token_id=snake_case__,   eos_token_id=snake_case__,   **snake_case__  )

									lowercase_	:   int  				=     max_position_embeddings
									lowercase_	:   Union[str, Any]  				=     hidden_size
									lowercase_	:   Optional[Any]  				=     num_hidden_layers
									lowercase_	:   str  				=     num_attention_heads
									lowercase_	:   List[Any]  				=     intermediate_size
									lowercase_	:   Optional[Any]  				=     hidden_act
									lowercase_	:   List[str]  				=     hidden_dropout_prob
									lowercase_	:   Any  				=     attention_probs_dropout_prob
									lowercase_	:   Union[str, Any]  				=     initializer_range
									lowercase_	:   Optional[Any]  				=     type_vocab_size
									lowercase_	:   Dict  				=     layer_norm_eps

									# Character config:
									lowercase_	:   Optional[int]  				=     downsampling_rate
									lowercase_	:   List[str]  				=     upsampling_kernel_size
									lowercase_	:   int  				=     num_hash_functions
									lowercase_	:   List[str]  				=     num_hash_buckets
									lowercase_	:   List[str]  				=     local_transformer_stride