'''simple docstring'''





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
#
########################################################################


SCREAMING_SNAKE_CASE_:	Union[str, Any]    =16
SCREAMING_SNAKE_CASE_:	List[str]    =32





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


     '''simple docstring'''




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

     def tokenize_function(snake_case_			:					List[Any]     ):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase_			       =  tokenizer(examples["sentence1"]   ,       examples["sentence2"]   ,       truncation=snake_case_   ,       max_length=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():
          UpperCAmelCase_			       =  datasets.map(
              snake_case_   ,       batched=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
     UpperCAmelCase_			       =  tokenized_datasets.rename_column("label"   ,       "labels"     )

     def collate_fn(snake_case_			:					List[Any]     ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          UpperCAmelCase_			       =  1_28 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(
              snake_case_   ,       padding="longest"   ,       max_length=snake_case_   ,       pad_to_multiple_of=snake_case_   ,       return_tensors="pt"   ,       )

     # Instantiate dataloaders.
     UpperCAmelCase_			       =  DataLoader(
         tokenized_datasets["train"]   ,       shuffle=snake_case_   ,       collate_fn=snake_case_   ,       batch_size=snake_case_     )
     UpperCAmelCase_			       =  DataLoader(
         tokenized_datasets["validation"]   ,       shuffle=snake_case_   ,       collate_fn=snake_case_   ,       batch_size=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

    SCREAMING_SNAKE_CASE_:	Tuple    =mocked_dataloaders  # noqa: F811





def       lowerCAmelCase_							(   snake_case_			:					Union[str, Any]   ,       snake_case_			:					Union[str, Any]     )       -> Optional[int]:


     '''simple docstring'''




     if os.environ.get("TESTING_MOCKED_DATALOADERS"   ,       snake_case_     ) == "1":
          UpperCAmelCase_			       =  2
     # New Code #
     UpperCAmelCase_			       =  int(args.gradient_accumulation_steps     )
     # Initialize accelerator
     UpperCAmelCase_			       =  Accelerator(
         cpu=args.cpu   ,       mixed_precision=args.mixed_precision   ,       gradient_accumulation_steps=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
     UpperCAmelCase_			       =  config["lr"]
     UpperCAmelCase_			       =  int(config["num_epochs"]     )
     UpperCAmelCase_			       =  int(config["seed"]     )
     UpperCAmelCase_			       =  int(config["batch_size"]     )

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

     set_seed(snake_case_     )
     UpperCAmelCase_       ,					UpperCAmelCase_			       =  get_dataloaders(snake_case_   ,       snake_case_     )
     # 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=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).
     UpperCAmelCase_			       =  model.to(accelerator.device     )

     # Instantiate optimizer
     UpperCAmelCase_			       =  AdamW(params=model.parameters()   ,       lr=snake_case_     )

     # Instantiate scheduler
     UpperCAmelCase_			       =  get_linear_schedule_with_warmup(
         optimizer=snake_case_   ,       num_warmup_steps=1_00   ,       num_training_steps=(len(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.
     UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  accelerator.prepare(
         snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_     )

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

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

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





def       lowerCAmelCase_							(   )       -> Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  argparse.ArgumentParser(description="Simple example of training script."     )
     parser.add_argument(
         "--mixed_precision"   ,       type=snake_case_   ,       default=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=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."     )
     UpperCAmelCase_			       =  parser.parse_args()
     UpperCAmelCase_			       =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(snake_case_   ,       snake_case_     )


if __name__ == "__main__":
    main()








a			:			Any												=					[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = 0
			while number:
						# Increased Speed Slightly by checking every 5 digits together.
						sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
						number //= 10_00_00

			return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
a			:			list[bool | None]												=					[None] * 10000000
a			:			int												=					True
a			:			Any												=					False


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if CHAINS[number - 1] is not None:
						return CHAINS[number - 1]  # type: ignore

			__lowercase			       = chain(next_number(_UpperCamelCase	)	)
			__lowercase			       = number_chain

			while number < 10_00_00_00:
						__lowercase			       = number_chain
						number *= 10

			return number_chain


def 		lowercase_	(  _UpperCamelCase = 10_00_00_00	):


			'''simple docstring'''







			for i in range(1							,      _UpperCamelCase	):
						if CHAINS[i] is None:
									chain(i + 1	)

			return CHAINS[:number].count(_UpperCamelCase	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{solution() = }''')


