



'''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
#
########################################################################

A_				:					int							=      3_2




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_ = 16						)->						str:





       '''simple docstring'''


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

       def tokenize_function(lowerCAmelCase_						):
              # max_length=None => use the model max length (it's actually the default)
              _UpperCAmelCase    :		Any     	=					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():
              _UpperCAmelCase    :		int     	=					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
       _UpperCAmelCase    :		Union[str, Any]     	=					tokenized_datasets.rename_column("""label"""			,  """labels"""						)

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

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

       # Instantiate dataloaders.
       _UpperCAmelCase    :		Any     	=					DataLoader(
           tokenized_datasets["""train"""]			,  shuffle=lowerCAmelCase_			,  collate_fn=lowerCAmelCase_			,  batch_size=lowerCAmelCase_						)
       _UpperCAmelCase    :		int     	=					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        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						int:





       '''simple docstring'''


       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,  lowerCAmelCase_						) == "1":
              _UpperCAmelCase    :		Optional[int]     	=					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[Any]     	=					config["""lr"""]
       _UpperCAmelCase    :		List[str]     	=					int(config["""num_epochs"""]						)
       _UpperCAmelCase    :		Any     	=					int(config["""seed"""]						)
       _UpperCAmelCase    :		str     	=					int(config["""batch_size"""]						)

       _UpperCAmelCase    :		List[str]     	=					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(lowerCAmelCase_						)						)
       def inner_training_loop(lowerCAmelCase_						):
              # 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    :		Tuple     	=					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(lowerCAmelCase_						)

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

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

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

                     _UpperCAmelCase    :		Union[str, Any]     	=					metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(F'''epoch {epoch}:'''			,  lowerCAmelCase_						)

    # 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        snake_case_   (	)->						int:





       '''simple docstring'''


       _UpperCAmelCase    :		Optional[int]     	=					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."""			,  )
       parser.add_argument("""--cpu"""			,  action="""store_true"""			,  help="""If passed, will train on the CPU."""						)
       _UpperCAmelCase    :		Union[str, 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    :		List[Any]     	=					{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 256}
       training_function(lowerCAmelCase_			,  lowerCAmelCase_						)


if __name__ == "__main__":
   main()






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


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  'yoso'



   def __init__(							self,       lowercase_=50265,       lowercase_=768,       lowercase_=12,       lowercase_=12,       lowercase_=3072,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=4096,       lowercase_=1,       lowercase_=0.02,       lowercase_=1E-12,       lowercase_="absolute",       lowercase_=True,       lowercase_=9,       lowercase_=64,       lowercase_=None,       lowercase_=True,       lowercase_=True,       lowercase_=1,       lowercase_=0,       lowercase_=2,       **lowercase_,       )				->       List[str]:






     """simple docstring"""






     super().__init__(pad_token_id=lowercase_,       bos_token_id=lowercase_,       eos_token_id=lowercase_,       **lowercase_			)

     a__    =vocab_size
     a__    =max_position_embeddings
     a__    =hidden_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size
     a__    =hidden_act
     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =initializer_range
     a__    =type_vocab_size
     a__    =layer_norm_eps
     a__    =position_embedding_type
     a__    =use_expectation
     a__    =hash_code_len
     a__    =num_hash
     a__    =conv_window
     a__    =use_fast_hash
     a__    =lsh_backward
