





import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a__      :     Union[str, Any]         =		16
a__      :     Optional[int]         =		32

def      UpperCAmelCase_    (    _UpperCAmelCase							:Accelerator  ,						_UpperCAmelCase							:int = 16  ,						_UpperCAmelCase							:str = "bert-base-cased"				)	->				Tuple:
     '''simple docstring'''


     A_          =       AutoTokenizer.from_pretrained(_UpperCAmelCase				)
     A_          =       load_dataset('''glue'''  ,						'''mrpc'''				)

     def tokenize_function(_UpperCAmelCase							:Dict				):
          # max_length=None => use the model max length (it's actually the default)
          A_          =       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
     A_          =       datasets.map(
         _UpperCAmelCase  ,						batched=_UpperCAmelCase  ,						remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,						load_from_cache_file=_UpperCAmelCase				)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     A_          =       tokenized_datasets.rename_column('''label'''  ,						'''labels'''				)

     def collate_fn(_UpperCAmelCase							:Optional[Any]				):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(_UpperCAmelCase  ,						padding='''max_length'''  ,						max_length=128  ,						return_tensors='''pt'''				)
          return tokenizer.pad(_UpperCAmelCase  ,						padding='''longest'''  ,						return_tensors='''pt'''				)

     # Instantiate dataloaders.
     A_          =       DataLoader(
         tokenized_datasets['''train''']  ,						shuffle=_UpperCAmelCase  ,						collate_fn=_UpperCAmelCase  ,						batch_size=_UpperCAmelCase				)
     A_          =       DataLoader(
         tokenized_datasets['''validation''']  ,						shuffle=_UpperCAmelCase  ,						collate_fn=_UpperCAmelCase  ,						batch_size=_UpperCAmelCase				)

     return train_dataloader, eval_dataloader

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[str]  ,						_UpperCAmelCase							:Any				)	->				Any:
     '''simple docstring'''


     A_          =       Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     A_          =       config['''lr''']
     A_          =       int(config['''num_epochs''']				)
     A_          =       int(config['''seed''']				)
     A_          =       int(config['''batch_size''']				)
     A_          =       args.model_name_or_path

     set_seed(_UpperCAmelCase				)
     A_   ,    A_          =       get_dataloaders(_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     A_          =       AutoModelForSequenceClassification.from_pretrained(_UpperCAmelCase  ,						return_dict=_UpperCAmelCase				)

     # Instantiate optimizer
     A_          =       (
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     A_          =       optimizer_cls(params=model.parameters()  ,						lr=_UpperCAmelCase				)

     if accelerator.state.deepspeed_plugin is not None:
          A_          =       accelerator.state.deepspeed_plugin.deepspeed_config[
              '''gradient_accumulation_steps'''
          ]
     else:
          A_          =       1
     A_          =       (len(_UpperCAmelCase				) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          A_          =       get_linear_schedule_with_warmup(
              optimizer=_UpperCAmelCase  ,						num_warmup_steps=0  ,						num_training_steps=_UpperCAmelCase  ,						)
     else:
          A_          =       DummyScheduler(_UpperCAmelCase  ,						total_num_steps=_UpperCAmelCase  ,						warmup_num_steps=0				)

     # 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.
     A_   ,    A_   ,    A_   ,    A_   ,    A_          =       accelerator.prepare(
         _UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)

     # We need to keep track of how many total steps we have iterated over
     A_          =       0
     # We also need to keep track of the stating epoch so files are named properly
     A_          =       0

     # Now we train the model
     A_          =       evaluate.load('''glue'''  ,						'''mrpc'''				)
     A_          =       0
     A_          =       {}
     for epoch in range(_UpperCAmelCase  ,						_UpperCAmelCase				):
          model.train()
          for step, batch in enumerate(_UpperCAmelCase				):
               A_          =       model(**_UpperCAmelCase				)
               A_          =       outputs.loss
               A_          =       loss / gradient_accumulation_steps
               accelerator.backward(_UpperCAmelCase				)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          A_          =       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():
                    A_          =       model(**_UpperCAmelCase				)
               A_          =       outputs.logits.argmax(dim=-1				)
               # It is slightly faster to call this once, than multiple times
               A_   ,    A_          =       accelerator.gather(
                   (predictions, batch['''labels'''])				)  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(_UpperCAmelCase				) - 1:
                         A_          =       predictions[: len(eval_dataloader.dataset				) - samples_seen]
                         A_          =       references[: len(eval_dataloader.dataset				) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=_UpperCAmelCase  ,						references=_UpperCAmelCase  ,						)

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

          if best_performance < eval_metric["accuracy"]:
               A_          =       eval_metric['''accuracy''']

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir  ,						'''all_results.json'''				)  ,						'''w'''				) as f:
               json.dump(_UpperCAmelCase  ,						_UpperCAmelCase				)

def      UpperCAmelCase_    (    )	->				Optional[Any]:
     '''simple docstring'''


     A_          =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''				)
     parser.add_argument(
         '''--model_name_or_path'''  ,						type=_UpperCAmelCase  ,						default='''bert-base-cased'''  ,						help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,						required=_UpperCAmelCase  ,						)
     parser.add_argument(
         '''--output_dir'''  ,						type=_UpperCAmelCase  ,						default='''.'''  ,						help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,						)
     parser.add_argument(
         '''--performance_lower_bound'''  ,						type=_UpperCAmelCase  ,						default=_UpperCAmelCase  ,						help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''  ,						)
     parser.add_argument(
         '''--num_epochs'''  ,						type=_UpperCAmelCase  ,						default=3  ,						help='''Number of train epochs.'''  ,						)
     A_          =       parser.parse_args()
     A_          =       {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''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 )



