






'''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
#
########################################################################


__A    	=   1_6
__A    	=   3_2




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int = 16  )    -> Optional[Any]:







   """simple docstring"""
   __snake_case				:      Tuple     							= AutoTokenizer.from_pretrained('bert-base-cased'  )
   __snake_case				:      Union[str, Any]     							= load_dataset('glue'  ,		'mrpc'  )

   def tokenize_function(A  :       Optional[Any]  ):
      # max_length=None => use the model max length (it's actually the default)
      __snake_case				:      Optional[int]     							= 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():
      __snake_case				:      Union[str, 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
   __snake_case				:      int     							= tokenized_datasets.rename_column('label'  ,		'labels'  )

   def collate_fn(A  :       str  ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      __snake_case				:      List[Any]     							= 1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         __snake_case				:      Any     							= 16
      elif accelerator.mixed_precision != "no":
         __snake_case				:      Any     							= 8
      else:
         __snake_case				:      Optional[int]     							= None

      return tokenizer.pad(
          A  ,		padding='longest'  ,		max_length=A  ,		pad_to_multiple_of=A  ,		return_tensors='pt'  ,		)

   # Instantiate dataloaders.
   __snake_case				:      List[Any]     							= DataLoader(
       tokenized_datasets['train']  ,		shuffle=A  ,		collate_fn=A  ,		batch_size=A  )
   __snake_case				:      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

      __A    	=   mocked_dataloaders  # noqa: F811




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  ,		A  :       Optional[Any]  )    -> List[Any]:







   """simple docstring"""







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

   __snake_case				:      List[Any]     							= evaluate.load('glue'  ,		'mrpc'  )

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

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

   # Instantiate optimizer
   __snake_case				:      int     							= AdamW(params=model.parameters()  ,		lr=A  )

   # Instantiate scheduler
   __snake_case				:      int     							= get_linear_schedule_with_warmup(
       optimizer=A  ,		num_warmup_steps=1_00  ,		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.
   __snake_case							,__snake_case							,__snake_case							,__snake_case							,__snake_case				:      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  )
         __snake_case				:      str     							= model(**A  )
         __snake_case				:      int     							= outputs.loss
         __snake_case				:      str     							= loss / gradient_accumulation_steps
         accelerator.backward(A  )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      __snake_case				:      Tuple     							= 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():
            __snake_case				:      List[Any]     							= model(**A  )
         __snake_case				:      List[str]     							= outputs.logits.argmax(dim=-1  )
         __snake_case							,__snake_case				:      int     							= 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
               __snake_case				:      Dict     							= predictions[: len(eval_dataloader.dataset  ) - samples_seen]
               __snake_case				:      Optional[Any]     							= 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  ,		)

      __snake_case				:      Tuple     							= metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""  ,		A  )




def        _SCREAMING_SNAKE_CASE  (				)    -> int:







   """simple docstring"""
   __snake_case				:      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.'  )
   __snake_case				:      int     							= parser.parse_args()
   __snake_case				:      Optional[Any]     							= {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(A  ,		A  )


if __name__ == "__main__":
      main()







'''simple docstring'''






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    	=   logging.get_logger(__name__)




class a_    (    UpperCamelCase_							):
   _snake_case        =							["""pixel_values"""]







   def __init__(self					,   __a = True					,   __a = None					,   __a = None					,   __a = PILImageResampling.BILINEAR					,   __a = True					,   __a = 1 / 2_5_5					,   __a = True					,   __a = None					,   __a = None					,   **__a					,   )			->      None:
      """simple docstring"""






      super().__init__(**__a)
      __snake_case				:      Tuple     							= size if size is not None else {'shortest_edge': 3_8_4}
      __snake_case				:      List[Any]     							= get_size_dict(__a					,   default_to_square=__a)

      __snake_case				:      int     							= do_resize
      __snake_case				:      List[str]     							= size
      # Default value set here for backwards compatibility where the value in config is None
      __snake_case				:      Any     							= crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
      __snake_case				:      Tuple     							= resample
      __snake_case				:      Dict     							= do_rescale
      __snake_case				:      Any     							= rescale_factor
      __snake_case				:      str     							= do_normalize
      __snake_case				:      Union[str, Any]     							= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __snake_case				:      Dict     							= image_std if image_std is not None else IMAGENET_STANDARD_STD







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a = PILImageResampling.BICUBIC					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






      __snake_case				:      Dict     							= get_size_dict(__a					,   default_to_square=__a)
      if "shortest_edge" not in size:
         raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""")
      __snake_case				:      List[str]     							= size['shortest_edge']

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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a = None					,   **__a					,   )			->      Any:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = ChannelDimension.FIRST					,   **__a					,   )			->      PIL.Image.Image:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= do_resize if do_resize is not None else self.do_resize
      __snake_case				:      Dict     							= crop_pct if crop_pct is not None else self.crop_pct
      __snake_case				:      Tuple     							= resample if resample is not None else self.resample
      __snake_case				:      Any     							= do_rescale if do_rescale is not None else self.do_rescale
      __snake_case				:      Any     							= rescale_factor if rescale_factor is not None else self.rescale_factor
      __snake_case				:      Union[str, Any]     							= do_normalize if do_normalize is not None else self.do_normalize
      __snake_case				:      Optional[int]     							= image_mean if image_mean is not None else self.image_mean
      __snake_case				:      Optional[Any]     							= image_std if image_std is not None else self.image_std

      __snake_case				:      List[str]     							= size if size is not None else self.size
      __snake_case				:      Any     							= get_size_dict(__a					,   default_to_square=__a)

      __snake_case				:      Dict     							= make_list_of_images(__a)

      if not valid_images(__a):
         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.
      __snake_case				:      Tuple     							= [to_numpy_array(__a) for image in images]

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

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

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

      __snake_case				:      Dict     							= [to_channel_dimension_format(__a					,   __a) for image in images]

      __snake_case				:      Union[str, Any]     							= {'pixel_values': images}
      return BatchFeature(data=__a					,   tensor_type=__a)