



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_		:  List[Any]										=  32


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Accelerator			,       UpperCAmelCase__			:int = 16       ):






 '''simple docstring'''

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

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

 def collate_fn(UpperCAmelCase__			:List[str]       ):
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   a							=		16
  elif accelerator.mixed_precision != "no":
   a							=		8
  else:
   a							=		None

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

 # Instantiate dataloaders.
 a							=		DataLoader(
     tokenized_datasets["train"]			,       shuffle=_a			,       collate_fn=_a			,       batch_size=_a       )
 a							=		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_		:  Optional[int]										=  mocked_dataloaders  # noqa: F811


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Optional[int]       ):






 '''simple docstring'''

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

 a							=		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(_a       )       )
 def inner_training_loop(UpperCAmelCase__			:str       ):
  # 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
  a							=		observed_batch_size // batch_size

  # And then set it in the Accelerator directly:
  a							=		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(_a       )

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

  # Instantiate optimizer
  a							=		AdamW(params=model.parameters()			,       lr=_a       )
  a		,	a							=		get_dataloaders(_a			,       _a       )

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

   a							=		metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(F"""epoch {epoch}:"""			,       _a       )

    # 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 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		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."			,       )
 parser.add_argument("--cpu"			,       action="store_true"			,       help="If passed, will train on the CPU."       )
 a							=		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
 a							=		{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 2_56}
 training_function(_a			,       _a       )


if __name__ == "__main__":
     main()







'''simple docstring'''







A					       =[[0, 1, 1], [0, 0, 1], [1, 0, 0]]


class      _a    :
       def __init__(      self		:      Optional[int]  ,							lowercase		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						name
           UpperCAmelCase     			=						[]
           UpperCAmelCase     			=						[]
       def        A (      self		:      Union[str, Any]  ,							lowercase		:      int							):



           '''simple docstring'''

           self.inbound.append(lowercase							)
       def        A (      self		:      List[str]  ,							lowercase		:      Optional[Any]							):



           '''simple docstring'''

           self.outbound.append(lowercase							)







       def __repr__(      self		:      Any							):



           '''simple docstring'''

           return f"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"






def 		snake_case_				(_a      :				str       ,  _a      :				str=3       ,  _a      :				Any=0.85	):
    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						1

    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						len(node.outbound	)

    for i in range(_a	):
        print(F"======= Iteration {i + 1} ======="	)
        for _, node in enumerate(_a	):
            UpperCAmelCase     			=						(1 - d) + d * sum(
                ranks[ib] / outbounds[ib] for ib in node.inbound	)
        print(_a	)





def 		snake_case_				():
    UpperCAmelCase     			=						list(input('''Enter Names of the Nodes: '''	).split()	)

    UpperCAmelCase     			=						[Node(_a	) for name in names]

    for ri, row in enumerate(_a	):
        for ci, col in enumerate(_a	):
            if col == 1:
                nodes[ci].add_inbound(names[ri]	)
                nodes[ri].add_outbound(names[ci]	)

    print('''======= Nodes ======='''	)
    for node in nodes:
        print(_a	)

    page_rank(_a	)


if __name__ == "__main__":
   main()




