


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


snake_case__				:	Optional[int]					       =					16
snake_case__				:	Any					       =					32


def    __lowerCamelCase   (      A__						: Accelerator						,						A__						: int = 16					)     ->    List[Any]:
   lowerCamelCase_						:   Optional[int]		= AutoTokenizer.from_pretrained("""bert-base-cased"""					)
   lowerCamelCase_						:   Optional[Any]		= load_dataset("""glue"""						,						"""mrpc"""					)

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

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

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

   # Instantiate dataloaders.
   lowerCamelCase_						:   str		= DataLoader(
       tokenized_datasets["""train"""]						,						shuffle=A__						,						collate_fn=A__						,						batch_size=A__					)
   lowerCamelCase_						:   Optional[Any]		= 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

      snake_case__				:	Tuple					       =					mocked_dataloaders  # noqa: F811


def    __lowerCamelCase   (      A__						: Optional[int]						,						A__						: Union[str, Any]					)     ->    Union[str, Any]:
   # For testing only
   if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,						A__					) == "1":
      lowerCamelCase_						:   Optional[int]		= 2
   # Initialize accelerator
   lowerCamelCase_						:   Any		= Accelerator(cpu=args.cpu						,						mixed_precision=args.mixed_precision					)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCamelCase_						:   Union[str, Any]		= config["""lr"""]
   lowerCamelCase_						:   List[Any]		= int(config["""num_epochs"""]					)
   lowerCamelCase_						:   Optional[int]		= int(config["""seed"""]					)
   lowerCamelCase_						:   List[str]		= int(config["""batch_size"""]					)

   lowerCamelCase_						:   List[Any]		= 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=A__					)
   def inner_training_loop(A__						: List[str]					):
      # 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(A__					)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCamelCase_						:   List[Any]		= 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).
      lowerCamelCase_						:   str		= model.to(accelerator.device					)

      # Instantiate optimizer
      lowerCamelCase_						:   Tuple		= AdamW(params=model.parameters()						,						lr=A__					)
      lowerCamelCase_,	lowerCamelCase_						:   Union[str, Any]		= get_dataloaders(A__						,						A__					)

      # Instantiate scheduler
      lowerCamelCase_						:   List[str]		= get_linear_schedule_with_warmup(
          optimizer=A__						,						num_warmup_steps=100						,						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.
      lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   List[Any]		= 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__					):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device					)
            lowerCamelCase_						:   Any		= model(**A__					)
            lowerCamelCase_						:   Dict		= 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():
               lowerCamelCase_						:   Union[str, Any]		= model(**A__					)
            lowerCamelCase_						:   List[Any]		= outputs.logits.argmax(dim=-1					)
            lowerCamelCase_,	lowerCamelCase_						:   Tuple		= accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
            metric.add_batch(
                predictions=A__						,						references=A__						,						)

         lowerCamelCase_						:   Any		= 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    __lowerCamelCase   (      )     ->    Optional[Any]:
   lowerCamelCase_						:   Optional[int]		= 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."""					)
   lowerCamelCase_						:   Optional[Any]		= parser.parse_args()
   lowerCamelCase_						:   List[Any]		= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(A__						,						A__					)


if __name__ == "__main__":
      main()









def    __lowerCamelCase   (      A__						: float						,						A__						: float						,						A__						: float						,						A__						: float						,						A__						: float						,						)     ->    float:
   lowerCamelCase_						:   List[str]		= [redshift, radiation_density, matter_density, dark_energy]
   if any(p < 0 for p in parameters					):
      raise ValueError("""All input parameters must be positive"""					)

   if any(p > 1 for p in parameters[1:4]					):
      raise ValueError("""Relative densities cannot be greater than one"""					)
   else:
      lowerCamelCase_						:   Union[str, Any]		= 1 - (matter_density + radiation_density + dark_energy)

      lowerCamelCase_						:   int		= (
          radiation_density * (redshift + 1) ** 4
          + matter_density * (redshift + 1) ** 3
          + curvature * (redshift + 1) ** 2
          + dark_energy
      )

      lowerCamelCase_						:   Dict		= hubble_constant * e_a ** (1 / 2)
      return hubble


if __name__ == "__main__":
      import doctest

      # run doctest
      doctest.testmod()

      # demo LCDM approximation
      snake_case__				:	str					       =					0.3

      print(
          hubble_parameter(
              hubble_constant=6_8.3,
              radiation_density=1e-4,
              matter_density=matter_density,
              dark_energy=1 - matter_density,
              redshift=0,
          )
      )





