



"""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
#
########################################################################


_lowerCAmelCase							:   str				=				16
_lowerCAmelCase							:   Dict				=				32

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Accelerator		,    SCREAMING_SNAKE_CASE__      :		int = 16							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       AutoTokenizer.from_pretrained("bert-base-cased"							)
     _UpperCAmelCase     :      str														=       load_dataset("glue"		,    "mrpc"							)

     def tokenize_function(SCREAMING_SNAKE_CASE__      :		Optional[Any]							):
          # max_length=None => use the model max length (it's actually the default)
          _UpperCAmelCase     :      int														=       tokenizer(examples["sentence1"]		,    examples["sentence2"]		,    truncation=_UpperCAmelCase		,    max_length=_UpperCAmelCase							)
          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(
              _UpperCAmelCase		,    batched=_UpperCAmelCase		,    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     :      Tuple														=       tokenized_datasets.rename_column("label"		,    "labels"							)

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

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

     # Instantiate dataloaders.
     _UpperCAmelCase     :      Any														=       DataLoader(
         tokenized_datasets["train"]		,    shuffle=_UpperCAmelCase		,    collate_fn=_UpperCAmelCase		,    batch_size=_UpperCAmelCase							)
     _UpperCAmelCase     :      List[Any]														=       DataLoader(
         tokenized_datasets["validation"]		,    shuffle=_UpperCAmelCase		,    collate_fn=_UpperCAmelCase		,    batch_size=_UpperCAmelCase							)

     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

 _lowerCAmelCase							:   Optional[Any]				=				mocked_dataloaders  # noqa: F811

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  int:





     '''simple docstring'''

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

     _UpperCAmelCase     :      str														=       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     :      Dict														=       batch_size // MAX_GPU_BATCH_SIZE
          _UpperCAmelCase     :      Union[str, Any]														=       MAX_GPU_BATCH_SIZE

     set_seed(_UpperCAmelCase							)
     _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       get_dataloaders(_UpperCAmelCase		,    _UpperCAmelCase							)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _UpperCAmelCase     :      int														=       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,    return_dict=_UpperCAmelCase							)

     # 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     :      str														=       model.to(accelerator.device							)

     # Instantiate optimizer
     _UpperCAmelCase     :      List[str]														=       AdamW(params=model.parameters()		,    lr=_UpperCAmelCase							)

     # Instantiate scheduler
     _UpperCAmelCase     :      Tuple														=       get_linear_schedule_with_warmup(
         optimizer=_UpperCAmelCase		,    num_warmup_steps=100		,    num_training_steps=(len(_UpperCAmelCase							) * 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[Any]														=       accelerator.prepare(
         _UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase							)

     # Now we train the model
     for epoch in range(_UpperCAmelCase							):
          model.train()
          for step, batch in enumerate(_UpperCAmelCase							):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device							)
               _UpperCAmelCase     :      Optional[Any]														=       model(**_UpperCAmelCase							)
               _UpperCAmelCase     :      List[Any]														=       outputs.loss
               _UpperCAmelCase     :      Optional[Any]														=       loss / gradient_accumulation_steps
               accelerator.backward(_UpperCAmelCase							)
               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(_UpperCAmelCase							):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device							)
               with torch.no_grad():
                    _UpperCAmelCase     :      List[str]														=       model(**_UpperCAmelCase							)
               _UpperCAmelCase     :      Any														=       outputs.logits.argmax(dim=-1							)
               _UpperCAmelCase							,			_UpperCAmelCase     :      Any														=       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(_UpperCAmelCase							) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         _UpperCAmelCase     :      str														=       predictions[: len(eval_dataloader.dataset							) - samples_seen]
                         _UpperCAmelCase     :      Dict														=       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=_UpperCAmelCase		,    references=_UpperCAmelCase		,    )

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

def 		__snake_case		( )    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       argparse.ArgumentParser(description="Simple example of training script."							)
     parser.add_argument(
         "--mixed_precision"		,    type=_UpperCAmelCase		,    default=_UpperCAmelCase		,    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     :      int														=       parser.parse_args()
     _UpperCAmelCase     :      Tuple														=       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(_UpperCAmelCase		,    _UpperCAmelCase							)


if __name__ == "__main__":
 main()







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


a__      :     Dict         =		logging.get_logger(__name__)





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						Optional[int]							=   ["""pixel_values"""]


  def __init__(							self ,__snake_case = True ,__snake_case = None ,__snake_case = None ,__snake_case = PILImageResampling.BILINEAR ,__snake_case = True ,__snake_case = 1 / 2_5_5 ,__snake_case = True ,__snake_case = None ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       super().__init__(**__snake_case )
       A_          =       size if size is not None else {'''shortest_edge''': 3_8_4}
       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )

       A_          =       do_resize
       A_          =       size
       # Default value set here for backwards compatibility where the value in config is None
       A_          =       crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
       A_          =       resample
       A_          =       do_rescale
       A_          =       rescale_factor
       A_          =       do_normalize
       A_          =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A_          =       image_std if image_std is not None else IMAGENET_STANDARD_STD


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case = PILImageResampling.BICUBIC ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )
       if "shortest_edge" not in size:
            raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}' )
       A_          =       size['''shortest_edge''']

       if shortest_edge < 3_8_4:
            # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
            A_          =       int(shortest_edge / crop_pct )
            A_          =       get_resize_output_image_size(__snake_case ,size=__snake_case ,default_to_square=__snake_case )
            A_          =       resize(image=__snake_case ,size=__snake_case ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )
            # then crop to (shortest_edge, shortest_edge)
            return center_crop(image=__snake_case ,size=(shortest_edge, shortest_edge) ,data_format=__snake_case ,**__snake_case )
       else:
            # warping (no cropping) when evaluated at 384 or larger
            return resize(
                __snake_case ,size=(shortest_edge, shortest_edge) ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       return rescale(__snake_case ,scale=__snake_case ,data_format=__snake_case ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       return normalize(__snake_case ,mean=__snake_case ,std=__snake_case ,data_format=__snake_case ,**__snake_case )




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = ChannelDimension.FIRST ,**__snake_case ,):



       """simple docstring"""





       A_          =       do_resize if do_resize is not None else self.do_resize
       A_          =       crop_pct if crop_pct is not None else self.crop_pct
       A_          =       resample if resample is not None else self.resample
       A_          =       do_rescale if do_rescale is not None else self.do_rescale
       A_          =       rescale_factor if rescale_factor is not None else self.rescale_factor
       A_          =       do_normalize if do_normalize is not None else self.do_normalize
       A_          =       image_mean if image_mean is not None else self.image_mean
       A_          =       image_std if image_std is not None else self.image_std

       A_          =       size if size is not None else self.size
       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )

       A_          =       make_list_of_images(__snake_case )

       if not valid_images(__snake_case ):
            raise ValueError(
                '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

       if do_resize and size is None or resample is None:
            raise ValueError('''Size and resample must be specified if do_resize is True.''' )

       if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
            raise ValueError('''crop_pct must be specified if size < 384.''' )

       if do_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

       if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

       # All transformations expect numpy arrays.
       A_          =       [to_numpy_array(__snake_case ) for image in images]

       if do_resize:
            A_          =       [self.resize(image=__snake_case ,size=__snake_case ,crop_pct=__snake_case ,resample=__snake_case ) for image in images]

       if do_rescale:
            A_          =       [self.rescale(image=__snake_case ,scale=__snake_case ) for image in images]

       if do_normalize:
            A_          =       [self.normalize(image=__snake_case ,mean=__snake_case ,std=__snake_case ) for image in images]

       A_          =       [to_channel_dimension_format(__snake_case ,__snake_case ) for image in images]

       A_          =       {'''pixel_values''': images}
       return BatchFeature(data=__snake_case ,tensor_type=__snake_case )



