






'''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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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							=16
UpperCamelCase							=32



def 							snake_case					(     a_							: Accelerator    ,   a_							: int = 16	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Union[str, Any]					    =							AutoTokenizer.from_pretrained("""bert-base-cased"""	)
      UpperCamelCase_				:   Any					    =							load_dataset("""glue"""    ,   """mrpc"""	)

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

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

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

      # Instantiate dataloaders.
      UpperCamelCase_				:   Dict					    =							DataLoader(
          tokenized_datasets["""train"""]    ,   shuffle=a_    ,   collate_fn=a_    ,   batch_size=a_	)
      UpperCamelCase_				:   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

      UpperCamelCase							=mocked_dataloaders  # noqa: F811



def 							snake_case					(     a_							: Dict    ,   a_							: List[str]	)       ->				Any:





      """simple docstring"""

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

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

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

      set_seed(a_	)
      UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							get_dataloaders(a_    ,   a_	)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCamelCase_				:   Optional[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).
      UpperCamelCase_				:   Tuple					    =							model.to(accelerator.device	)

      # Instantiate optimizer
      UpperCamelCase_				:   Any					    =							AdamW(params=model.parameters()    ,   lr=a_	)

      # Instantiate scheduler
      UpperCamelCase_				:   Optional[Any]					    =							get_linear_schedule_with_warmup(
          optimizer=a_    ,   num_warmup_steps=100    ,   num_training_steps=(len(a_	) * 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_				:   Optional[int]					    =							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	)
                  UpperCamelCase_				:   str					    =							model(**a_	)
                  UpperCamelCase_				:   str					    =							outputs.loss
                  UpperCamelCase_				:   Union[str, Any]					    =							loss / gradient_accumulation_steps
                  accelerator.backward(a_	)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            UpperCamelCase_				:   Optional[Any]					    =							0
            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():
                        UpperCamelCase_				:   Dict					    =							model(**a_	)
                  UpperCamelCase_				:   Optional[Any]					    =							outputs.logits.argmax(dim=-1	)
                  UpperCamelCase_    ,			UpperCamelCase_				:   List[str]					    =							accelerator.gather((predictions, batch["""labels"""])	)
                  # New Code #
                  # First we check if it's a distributed system
                  if accelerator.use_distributed:
                        # Then see if we're on the last batch of our eval dataloader
                        if step == len(a_	) - 1:
                              # Last batch needs to be truncated on distributed systems as it contains additional samples
                              UpperCamelCase_				:   int					    =							predictions[: len(eval_dataloader.dataset	) - samples_seen]
                              UpperCamelCase_				:   str					    =							references[: len(eval_dataloader.dataset	) - samples_seen]
                        else:
                              # Otherwise we add the number of samples seen
                              samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
                  metric.add_batch(
                      predictions=a_    ,   references=a_    ,   )

            UpperCamelCase_				:   Union[str, Any]					    =							metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:"    ,   a_	)



def 							snake_case					(     )       ->				str:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							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."""	)
      UpperCamelCase_				:   Tuple					    =							parser.parse_args()
      UpperCamelCase_				:   Any					    =							{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(a_    ,   a_	)


if __name__ == "__main__":
      main()














'''simple docstring'''

from datetime import datetime

import matplotlib.pyplot as plt
import torch



def 							snake_case					(     a_							: List[Any]	)       ->				Any:





      """simple docstring"""

      for param in module.parameters():
            UpperCamelCase_				:   Dict					    =							False



def 							snake_case					(     )       ->				List[str]:





      """simple docstring"""

      UpperCamelCase_				:   List[str]					    =							"""cuda""" if torch.cuda.is_available() else """cpu"""
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            UpperCamelCase_				:   int					    =							"""mps"""
      if device == "mps":
            print(
                """WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
                """ errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
                """ with generations."""	)
      return device



def 							snake_case					(     a_							: str	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							plt.imshow(a_	)
      fig.axes.get_xaxis().set_visible(a_	)
      fig.axes.get_yaxis().set_visible(a_	)
      plt.show()



def 							snake_case					(     )       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							datetime.now()
      UpperCamelCase_				:   int					    =							current_time.strftime("""%H:%M:%S"""	)
      return timestamp






