






import argparse

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


UpperCamelCase    							=      16
UpperCamelCase    							=      32


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Accelerator					,							__lowerCAmelCase							:					int = 16							)     ->  str:
   __UpperCamelCase	:			Union[str, Any]					=					AutoTokenizer.from_pretrained("""bert-base-cased"""							)
   __UpperCamelCase	:			Optional[int]					=					load_dataset("""glue"""					,							"""mrpc"""							)

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

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

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

   # Instantiate dataloaders.
   __UpperCamelCase	:			List[str]					=					DataLoader(
       tokenized_datasets["""train"""]					,							shuffle=__lowerCAmelCase					,							collate_fn=__lowerCAmelCase					,							batch_size=__lowerCAmelCase					,							drop_last=__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					DataLoader(
       tokenized_datasets["""validation"""]					,							shuffle=__lowerCAmelCase					,							collate_fn=__lowerCAmelCase					,							batch_size=__lowerCAmelCase					,							drop_last=(accelerator.mixed_precision == """fp8""")					,							)

   return train_dataloader, eval_dataloader


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Optional[int]							)     ->  int:
   # 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	:			int					=					config["""lr"""]
   __UpperCamelCase	:			int					=					int(config["""num_epochs"""]							)
   __UpperCamelCase	:			int					=					int(config["""seed"""]							)
   __UpperCamelCase	:			Any					=					int(config["""batch_size"""]							)

   __UpperCamelCase	:			Optional[int]					=					evaluate.load("""glue"""					,							"""mrpc"""							)

   # If the batch size is too big we use gradient accumulation
   __UpperCamelCase	:			str					=					1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      __UpperCamelCase	:			str					=					batch_size // MAX_GPU_BATCH_SIZE
      __UpperCamelCase	:			List[str]					=					MAX_GPU_BATCH_SIZE

   set_seed(__lowerCAmelCase							)
   __UpperCamelCase	,     __UpperCamelCase	:			Dict					=					get_dataloaders(__lowerCAmelCase					,							__lowerCAmelCase							)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __UpperCamelCase	:			Dict					=					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	:			Optional[Any]					=					model.to(accelerator.device							)
   # Instantiate optimizer
   __UpperCamelCase	:			Union[str, Any]					=					AdamW(params=model.parameters()					,							lr=__lowerCAmelCase							)

   # Instantiate scheduler
   __UpperCamelCase	:			int					=					get_linear_schedule_with_warmup(
       optimizer=__lowerCAmelCase					,							num_warmup_steps=100					,							num_training_steps=(len(__lowerCAmelCase							) * num_epochs) // gradient_accumulation_steps					,							)

   # 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	:			int					=					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							)
         __UpperCamelCase	:			Tuple					=					model(**__lowerCAmelCase							)
         __UpperCamelCase	:			Optional[int]					=					outputs.loss
         __UpperCamelCase	:			str					=					loss / gradient_accumulation_steps
         accelerator.backward(__lowerCAmelCase							)
         if step % gradient_accumulation_steps == 0:
            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	:			List[Any]					=					model(**__lowerCAmelCase							)
         __UpperCamelCase	:			int					=					outputs.logits.argmax(dim=-1							)
         __UpperCamelCase	,     __UpperCamelCase	:			Optional[int]					=					accelerator.gather_for_metrics((predictions, batch["""labels"""])							)
         metric.add_batch(
             predictions=__lowerCAmelCase					,							references=__lowerCAmelCase					,							)

      __UpperCamelCase	:			str					=					metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'epoch {epoch}:'					,							__lowerCAmelCase							)


def 				__lowerCamelCase     (							)     ->  Tuple:
   __UpperCamelCase	:			Union[str, Any]					=					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	:			List[str]					=					parser.parse_args()
   __UpperCamelCase	:			Optional[Any]					=					{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(__lowerCAmelCase					,							__lowerCAmelCase							)


if __name__ == "__main__":
       main()











from collections.abc import Iterable
from typing import Generic, TypeVar

UpperCamelCase    							=      TypeVar('_T')







class 		_A						( Generic[_T] ):

  def __init__(    self						:	int   ,				lowerCamelCase__						:	Iterable[_T] | None = None			):
     """simple docstring"""

     __UpperCamelCase	:			list[_T]					=					list(iterable or []			)
     __UpperCamelCase	:			list[_T]					=					[]

  def __len__(    self						:	str			):
     """simple docstring"""

     return len(self._stacka			) + len(self._stacka			)

  def __repr__(    self						:	Dict			):
     """simple docstring"""

     return f'Queue({tuple(self._stacka[::-1] + self._stacka			)})'

  def    a					(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	_T			):
     """simple docstring"""

     self._stacka.append(lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					self._stacka.pop
     __UpperCamelCase	:			int					=					self._stacka.append

     if not self._stacka:
        while self._stacka:
           stacka_append(stacka_pop()			)

     if not self._stacka:
        raise IndexError("""Queue is empty"""			)
     return self._stacka.pop()


if __name__ == "__main__":
       from doctest import testmod

       testmod()



