





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 the experiment tracking capability,
# 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
#
########################################################################

_snake_case    :       Tuple		       =    16
_snake_case    :       int		       =    32

def     a_       (					lowerCAmelCase_ :     Accelerator,      lowerCAmelCase_ :     int = 16      ):
   __lowerCAmelCase				      =      AutoTokenizer.from_pretrained('bert-base-cased'      )
   __lowerCAmelCase				      =      load_dataset('glue',      'mrpc'      )

   def tokenize_function(lowerCAmelCase_ :     str      ):
      # max_length=None => use the model max length (it's actually the default)
      __lowerCAmelCase				      =      tokenizer(examples['sentence1'],      examples['sentence2'],      truncation=lowerCAmelCase_,      max_length=lowerCAmelCase_      )
      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				      =      datasets.map(
          lowerCAmelCase_,      batched=lowerCAmelCase_,      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				      =      tokenized_datasets.rename_column('label',      'labels'      )

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

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

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

   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    :       Optional[Any]		       =    mocked_dataloaders  # noqa: F811

def     a_       (					lowerCAmelCase_ :     Dict,      lowerCAmelCase_ :     List[Any]      ):
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS',      lowerCAmelCase_      ) == "1":
      __lowerCAmelCase				      =      2
   # Initialize Accelerator

   # New Code #
   # We pass in "all" to `log_with` to grab all available trackers in the environment
   # Note: If using a custom `Tracker` class, should be passed in here such as:
   # >>> log_with = ["all", MyCustomTrackerClassInstance()]
   if args.with_tracking:
      __lowerCAmelCase				      =      Accelerator(
          cpu=args.cpu,      mixed_precision=args.mixed_precision,      log_with='all',      project_dir=args.project_dir      )
   else:
      __lowerCAmelCase				      =      Accelerator(cpu=args.cpu,      mixed_precision=args.mixed_precision      )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __lowerCAmelCase				      =      config['lr']
   __lowerCAmelCase				      =      int(config['num_epochs']      )
   __lowerCAmelCase				      =      int(config['seed']      )
   __lowerCAmelCase				      =      int(config['batch_size']      )
   set_seed(lowerCAmelCase_      )

   __lowerCAmelCase      ,      __lowerCAmelCase				      =      get_dataloaders(lowerCAmelCase_,      lowerCAmelCase_      )
   __lowerCAmelCase				      =      evaluate.load('glue',      'mrpc'      )

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

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowerCAmelCase				      =      AutoModelForSequenceClassification.from_pretrained('bert-base-cased',      return_dict=lowerCAmelCase_      )

   # 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				      =      model.to(accelerator.device      )

   # Instantiate optimizer
   __lowerCAmelCase				      =      AdamW(params=model.parameters(),      lr=lowerCAmelCase_      )

   # Instantiate scheduler
   __lowerCAmelCase				      =      get_linear_schedule_with_warmup(
       optimizer=lowerCAmelCase_,      num_warmup_steps=100,      num_training_steps=(len(lowerCAmelCase_      ) * 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.
   __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      accelerator.prepare(
       lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )

   # New Code #
   # We need to initialize the trackers we use. Overall configurations can also be stored
   if args.with_tracking:
      __lowerCAmelCase				      =      os.path.split(lowerCAmelCase_      )[-1].split('.'      )[0]
      accelerator.init_trackers(lowerCAmelCase_,      lowerCAmelCase_      )

   # Now we train the model
   for epoch in range(lowerCAmelCase_      ):
      model.train()
      # New Code #
      # For our tracking example, we will log the total loss of each epoch
      if args.with_tracking:
         __lowerCAmelCase				      =      0
      for step, batch in enumerate(lowerCAmelCase_      ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device      )
         __lowerCAmelCase				      =      model(**lowerCAmelCase_      )
         __lowerCAmelCase				      =      outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         __lowerCAmelCase				      =      loss / gradient_accumulation_steps
         accelerator.backward(lowerCAmelCase_      )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(lowerCAmelCase_      ):
         # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
         batch.to(accelerator.device      )
         with torch.no_grad():
            __lowerCAmelCase				      =      model(**lowerCAmelCase_      )
         __lowerCAmelCase				      =      outputs.logits.argmax(dim=-1      )
         __lowerCAmelCase      ,      __lowerCAmelCase				      =      accelerator.gather_for_metrics((predictions, batch['labels'])      )
         metric.add_batch(
             predictions=lowerCAmelCase_,      references=lowerCAmelCase_,      )

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

      # New Code #
      # To actually log, we call `Accelerator.log`
      # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
      if args.with_tracking:
         accelerator.log(
             {
                 'accuracy': eval_metric['accuracy'],
                 'f1': eval_metric['f1'],
                 'train_loss': total_loss.item() / len(lowerCAmelCase_      ),
                 'epoch': epoch,
             },      step=lowerCAmelCase_,      )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
   if args.with_tracking:
      accelerator.end_training()

def     a_       (					):
   __lowerCAmelCase				      =      argparse.ArgumentParser(description='Simple example of training script.'      )
   parser.add_argument(
       '--mixed_precision',      type=lowerCAmelCase_,      default=lowerCAmelCase_,      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.'      )
   parser.add_argument(
       '--with_tracking',      action='store_true',      help='Whether to load in all available experiment trackers from the environment and use them for logging.',      )
   parser.add_argument(
       '--project_dir',      type=lowerCAmelCase_,      default='logs',      help='Location on where to store experiment tracking logs` and relevent project information',      )
   __lowerCAmelCase				      =      parser.parse_args()
   __lowerCAmelCase				      =      {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(lowerCAmelCase_,      lowerCAmelCase_      )


if __name__ == "__main__":
     main()







'''simple docstring'''







from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
      import PIL

# soft dependency
if is_pytesseract_available():
      import pytesseract

lowercase      :   Optional[Any]      =       logging.get_logger(__name__)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     return [
         int(1_000 * (box[0] / width)				),
         int(1_000 * (box[1] / height)				),
         int(1_000 * (box[2] / width)				),
         int(1_000 * (box[3] / height)				),
     ]



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      List[Any]        =			to_pil_image(__lowercase				)
     snake_case ,		snake_case			:      Union[str, Any]        =			pil_image.size
     snake_case			:      Optional[Any]        =			pytesseract.image_to_data(__lowercase     ,   lang=__lowercase     ,   output_type="""dict"""     ,   config=__lowercase				)
     snake_case ,		snake_case ,		snake_case ,		snake_case ,		snake_case			:      Dict        =			data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

     # filter empty words and corresponding coordinates
     snake_case			:      Tuple        =			[idx for idx, word in enumerate(__lowercase				) if not word.strip()]
     snake_case			:      Tuple        =			[word for idx, word in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      str        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      Tuple        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      Optional[Any]        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      Optional[Any]        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]

     # turn coordinates into (left, top, left+width, top+height) format
     snake_case			:      Dict        =			[]
     for x, y, w, h in zip(__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
          snake_case			:      Tuple        =			[x, y, x + w, y + h]
          actual_boxes.append(__lowercase				)

     # finally, normalize the bounding boxes
     snake_case			:      str        =			[]
     for box in actual_boxes:
          normalized_boxes.append(normalize_box(__lowercase     ,   __lowercase     ,   __lowercase				)				)

     assert len(__lowercase				) == len(__lowercase				), "Not as many words as there are bounding boxes"

     return words, normalized_boxes







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					["""pixel_values"""]







     def __init__(				self							,__a = True							,__a = None							,__a = PILImageResampling.BILINEAR							,__a = True							,__a = 1 / 255							,__a = True							,__a = None							,__a = None							,__a = True							,__a = None							,__a = ""							,**__a							,)      ->				None:
          super().__init__(**__a							)
          snake_case			:      int        =			size if size is not None else {"""height""": 224, """width""": 224}
          snake_case			:      Union[str, Any]        =			get_size_dict(__a							)

          snake_case			:      List[str]        =			do_resize
          snake_case			:      Dict        =			size
          snake_case			:      Dict        =			resample
          snake_case			:      Union[str, Any]        =			do_rescale
          snake_case			:      Union[str, Any]        =			rescale_value
          snake_case			:      List[str]        =			do_normalize
          snake_case			:      int        =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          snake_case			:      int        =			image_std if image_std is not None else IMAGENET_STANDARD_STD
          snake_case			:      List[Any]        =			apply_ocr
          snake_case			:      Any        =			ocr_lang
          snake_case			:      Optional[int]        =			tesseract_config







     def 			snake_case_      (				self							,__a							,__a							,__a = PILImageResampling.BILINEAR							,__a = None							,**__a							,)      ->				np.ndarray:
          snake_case			:      List[str]        =			get_size_dict(__a							)
          if "height" not in size or "width" not in size:
               raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''							)
          snake_case			:      Tuple        =			(size["""height"""], size["""width"""])
          return resize(__a							,size=__a							,resample=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return rescale(__a							,scale=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return normalize(__a							,mean=__a							,std=__a							,data_format=__a							,**__a							)







     def 			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 = None							,__a = None							,__a = ChannelDimension.FIRST							,**__a							,)      ->				PIL.Image.Image:
          snake_case			:      int        =			do_resize if do_resize is not None else self.do_resize
          snake_case			:      List[Any]        =			size if size is not None else self.size
          snake_case			:      Any        =			get_size_dict(__a							)
          snake_case			:      Any        =			resample if resample is not None else self.resample
          snake_case			:      str        =			do_rescale if do_rescale is not None else self.do_rescale
          snake_case			:      List[str]        =			rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case			:      Dict        =			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			:      Tuple        =			image_std if image_std is not None else self.image_std
          snake_case			:      List[str]        =			apply_ocr if apply_ocr is not None else self.apply_ocr
          snake_case			:      str        =			ocr_lang if ocr_lang is not None else self.ocr_lang
          snake_case			:      Dict        =			tesseract_config if tesseract_config is not None else self.tesseract_config

          snake_case			:      Any        =			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:
               raise ValueError("""Size must be specified if do_resize is True."""							)

          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("""If do_normalize is True, image_mean and image_std must be specified."""							)

          # All transformations expect numpy arrays.
          snake_case			:      Union[str, Any]        =			[to_numpy_array(__a							) for image in images]

          # Tesseract OCR to get words + normalized bounding boxes
          if apply_ocr:
               requires_backends(self							,"""pytesseract"""							)
               snake_case			:      int        =			[]
               snake_case			:      Optional[Any]        =			[]
               for image in images:
                    snake_case ,		snake_case			:      Tuple        =			apply_tesseract(__a							,__a							,__a							)
                    words_batch.append(__a							)
                    boxes_batch.append(__a							)

          if do_resize:
               snake_case			:      Any        =			[self.resize(image=__a							,size=__a							,resample=__a							) for image in images]

          if do_rescale:
               snake_case			:      Tuple        =			[self.rescale(image=__a							,scale=__a							) for image in images]

          if do_normalize:
               snake_case			:      Optional[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			:      List[Any]        =			BatchFeature(data={"""pixel_values""": images}							,tensor_type=__a							)

          if apply_ocr:
               snake_case			:      Optional[Any]        =			words_batch
               snake_case			:      Optional[int]        =			boxes_batch
          return data



