


"""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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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
#
########################################################################


UpperCAmelCase__  							=16
UpperCAmelCase__  							=32


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any]				,   UpperCamelCase__	: Union[str, Any] = 16 ):






 """simple docstring"""







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

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

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

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

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

       UpperCAmelCase__  							=mocked_dataloaders  # noqa: F811


def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







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

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

 # New Code #
 # We now can define an inner training loop function. It should take a batch size as the only parameter,
 # and build the dataloaders in there.
 # It also gets our decorator
 @find_executable_batch_size(starting_batch_size=lowerCAmelCase_ )
 def inner_training_loop(UpperCamelCase__	: Union[str, Any] ):
  # And now just move everything below under this function
  # We need to bring in the Accelerator object from earlier
  nonlocal accelerator
  # And reset all of its attributes that could hold onto any memory:
  accelerator.free_memory()

  # Then we can declare the model, optimizer, and everything else:
  set_seed(lowerCAmelCase_ )

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

  # Instantiate optimizer
  __lowercase									=	AdamW(params=model.parameters()				,   lr=lowerCAmelCase_ )
  __lowercase									=	get_dataloaders(lowerCAmelCase_				,   lowerCAmelCase_ )

  # Instantiate scheduler
  __lowercase									=	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.
  __lowercase									=	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_ ):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device )
    __lowercase									=	model(**lowerCAmelCase_ )
    __lowercase									=	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():
     __lowercase									=	model(**lowerCAmelCase_ )
    __lowercase									=	outputs.logits.argmax(dim=-1 )
    __lowercase									=	accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
    metric.add_batch(
        predictions=lowerCAmelCase_				,   references=lowerCAmelCase_				,   )

   __lowercase									=	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 lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	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.""" )
 __lowercase									=	parser.parse_args()
 __lowercase									=	{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(lowerCAmelCase_				,   lowerCAmelCase_ )


if __name__ == "__main__":
       main()




"""simple docstring"""

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


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}





class    lowerCamelCase__						(		_a    ):
      a :     List[Any]											=		"""swinv2"""

      a :     Any											=		{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }
      def __init__(		self	:	List[str]      ,       A_	:	Any=2_2_4      ,       A_	:	int=4      ,       A_	:	Optional[int]=3      ,       A_	:	List[Any]=9_6      ,       A_	:	List[Any]=[2, 2, 6, 2]      ,       A_	:	List[str]=[3, 6, 1_2, 2_4]      ,       A_	:	Union[str, Any]=7      ,       A_	:	int=4.0      ,       A_	:	List[str]=True      ,       A_	:	str=0.0      ,       A_	:	Any=0.0      ,       A_	:	Union[str, Any]=0.1      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	int=False      ,       A_	:	List[Any]=0.02      ,       A_	:	Tuple=1e-5      ,       A_	:	Tuple=3_2      ,       **A_	:	int      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)

       __lowercase									=	image_size
       __lowercase									=	patch_size
       __lowercase									=	num_channels
       __lowercase									=	embed_dim
       __lowercase									=	depths
       __lowercase									=	len(A_							)
       __lowercase									=	num_heads
       __lowercase									=	window_size
       __lowercase									=	mlp_ratio
       __lowercase									=	qkv_bias
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	drop_path_rate
       __lowercase									=	hidden_act
       __lowercase									=	use_absolute_embeddings
       __lowercase									=	layer_norm_eps
       __lowercase									=	initializer_range
       __lowercase									=	encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       __lowercase									=	int(embed_dim * 2 ** (len(A_							) - 1)							)
       __lowercase									=	(0, 0, 0, 0)
