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

UpperCamelCase    				=		32

def      lowercase_							(     _lowerCamelCase				:			Accelerator						,      _lowerCamelCase				:			int = 16):
    lowercase__					:  Union[str, Any]          =							AutoTokenizer.from_pretrained("bert-base-cased")
    lowercase__					:  List[str]          =							load_dataset("glue"						,      "mrpc")

    def tokenize_function(_lowerCamelCase				:			Dict):
        # max_length=None => use the model max length (it's actually the default)
        lowercase__					:  List[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():
        lowercase__					:  Any          =							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__					:  Optional[int]          =							tokenized_datasets.rename_column("label"						,      "labels")

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

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

    # Instantiate dataloaders.
    lowercase__					:  Optional[Any]          =							DataLoader(
        tokenized_datasets["train"]						,      shuffle=_lowerCamelCase						,      collate_fn=_lowerCamelCase						,      batch_size=_lowerCamelCase)
    lowercase__					:  Dict          =							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      lowercase_							(     _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			str):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"						,      _lowerCamelCase) == "1":
        lowercase__					:  Union[str, Any]          =							2
    # Initialize accelerator
    lowercase__					:  Union[str, Any]          =							Accelerator(cpu=args.cpu						,      mixed_precision=args.mixed_precision)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowercase__					:  Tuple          =							config["lr"]
    lowercase__					:  Optional[int]          =							int(config["num_epochs"])
    lowercase__					:  Dict          =							int(config["seed"])
    lowercase__					:  Optional[Any]          =							int(config["batch_size"])

    lowercase__					:  Optional[int]          =							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				:			Optional[Any]):
        # 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
        lowercase__					:  Tuple          =							observed_batch_size // batch_size

        # And then set it in the Accelerator directly:
        lowercase__					:  Any          =							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)
        lowercase__					:  int          =							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__					:  List[Any]          =							model.to(accelerator.device)

        # Instantiate optimizer
        lowercase__					:  Tuple          =							AdamW(params=model.parameters()						,      lr=_lowerCamelCase)
        lowercase__   ,  lowercase__					:  Optional[Any]          =							get_dataloaders(_lowerCamelCase						,      _lowerCamelCase)

        # Instantiate scheduler
        lowercase__					:  str          =							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__   ,  lowercase__   ,  lowercase__   ,  lowercase__   ,  lowercase__					:  Tuple          =							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)
                    lowercase__					:  str          =							model(**_lowerCamelCase)
                    lowercase__					:  Tuple          =							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__					:  Any          =							model(**_lowerCamelCase)
                lowercase__					:  Tuple          =							outputs.logits.argmax(dim=-1)
                lowercase__   ,  lowercase__					:  int          =							accelerator.gather_for_metrics((predictions, batch["labels"]))
                metric.add_batch(
                    predictions=_lowerCamelCase						,      references=_lowerCamelCase						,      )

            lowercase__					:  str          =							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      lowercase_							(     ):
    lowercase__					:  List[str]          =							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__					:  str          =							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
    lowercase__					:  str          =							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
    training_function(_lowerCamelCase						,      _lowerCamelCase)


if __name__ == "__main__":
      main()




from .number_theory.prime_numbers import next_prime



class     snake_case_			:






   def __init__( self							:	Tuple      ,		lowercase_							:	int      ,		lowercase_							:	int | None = None      ,		lowercase_							:	float | None = None      ,		)   ->		None:
       lowercase__					:  Optional[Any]          =							size_table
       lowercase__					:  Tuple          =							[None] * self.size_table
       lowercase__					:  List[str]          =							0.75 if lim_charge is None else lim_charge
       lowercase__					:  Optional[Any]          =							1 if charge_factor is None else charge_factor
       lowercase__					:  list          =							[]
       lowercase__					:  dict          =							{}






   def  __UpperCamelCase	( self							:	List[Any]    )   ->		Optional[Any]:
       return self._keys






   def  __UpperCamelCase	( self							:	Any    )   ->		List[Any]:
       return sum(1 for slot in self.values if slot is not None    ) / (
           self.size_table * self.charge_factor
       )






   def  __UpperCamelCase	( self							:	int      ,		lowercase_							:	Union[str, Any]    )   ->		Dict:
       return key % self.size_table






   def  __UpperCamelCase	( self							:	Union[str, Any]      ,		lowercase_							:	Any    )   ->		List[str]:
       print(F'''step {step_ord}'''    )
       print(list(range(len(self.values    )    )    )    )
       print(self.values    )






   def  __UpperCamelCase	( self							:	Tuple      ,		lowercase_							:	List[Any]    )   ->		Union[str, Any]:
       lowercase__					:  Any          =							1
       lowercase__					:  Dict          =							values
       for value in values:
           self.insert_data(lowercase_    )
           self._step_by_step(lowercase_    )
           i += 1






   def  __UpperCamelCase	( self							:	Tuple      ,		lowercase_							:	Dict      ,		lowercase_							:	Optional[Any]    )   ->		Any:
       lowercase__					:  str          =							data
       lowercase__					:  List[str]          =							data






   def  __UpperCamelCase	( self							:	int      ,		lowercase_							:	Any      ,		lowercase_							:	int=None    )   ->		int:
       lowercase__					:  Dict          =							self.hash_function(key + 1    )

       while self.values[new_key] is not None and self.values[new_key] != key:
           if self.values.count(lowercase_    ) > 0:
               lowercase__					:  List[Any]          =							self.hash_function(new_key + 1    )
           else:
               lowercase__					:  List[str]          =							None
               break

       return new_key






   def  __UpperCamelCase	( self							:	Union[str, Any]    )   ->		Optional[int]:
       lowercase__					:  Tuple          =							[value for value in self.values if value is not None]
       lowercase__					:  Any          =							next_prime(self.size_table      ,		factor=2    )
       self._keys.clear()
       lowercase__					:  Tuple          =							[None] * self.size_table  # hell's pointers D: don't DRY ;/
       for value in survivor_values:
           self.insert_data(lowercase_    )







   def  __UpperCamelCase	( self							:	Dict      ,		lowercase_							:	Optional[int]    )   ->		int:
       lowercase__					:  List[str]          =							self.hash_function(lowercase_    )

       if self.values[key] is None:
           self._set_value(lowercase_      ,		lowercase_    )

       elif self.values[key] == data:
           pass

       else:
           lowercase__					:  List[Any]          =							self._collision_resolution(lowercase_      ,		lowercase_    )
           if collision_resolution is not None:
               self._set_value(lowercase_      ,		lowercase_    )
           else:
               self.rehashing()
               self.insert_data(lowercase_    )



