





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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	:					Any			  =					16
__lowerCamelCase	:					str			  =					32



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase = 16					)						->			int:
 UpperCamelCase     : Union[str, Any]	     =       AutoTokenizer.from_pretrained("bert-base-cased"					)
 UpperCamelCase     : Optional[Any]	     =       DatasetDict(
     {
         "train": dataset["train"].select(_lowerCAmelCase					),
         "validation": dataset["train"].select(_lowerCAmelCase					),
         "test": dataset["validation"],
     }					)

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

 def collate_fn(_lowerCAmelCase					):
  # 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     : Optional[int]	     =       16
  elif accelerator.mixed_precision != "no":
   UpperCamelCase     : Tuple	     =       8
  else:
   UpperCamelCase     : int	     =       None

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

 # Instantiate dataloaders.
 UpperCamelCase     : Dict	     =       DataLoader(
     tokenized_datasets["train"]							,       shuffle=_lowerCAmelCase							,       collate_fn=_lowerCAmelCase							,       batch_size=_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       DataLoader(
     tokenized_datasets["validation"]							,       shuffle=_lowerCAmelCase							,       collate_fn=_lowerCAmelCase							,       batch_size=_lowerCAmelCase					)

 UpperCamelCase     : Tuple	     =       DataLoader(
     tokenized_datasets["test"]							,       shuffle=_lowerCAmelCase							,       collate_fn=_lowerCAmelCase							,       batch_size=_lowerCAmelCase					)

 return train_dataloader, eval_dataloader, test_dataloader



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Union[str, Any]:
 # New Code #
 UpperCamelCase     : Optional[int]	     =       []
 # Download the dataset
 UpperCamelCase     : Optional[int]	     =       load_dataset("glue"							,       "mrpc"					)
 # Create our splits
 UpperCamelCase     : Optional[Any]	     =       StratifiedKFold(n_splits=int(args.num_folds					)					)
 # Initialize accelerator
 UpperCamelCase     : Tuple	     =       Accelerator(cpu=args.cpu							,       mixed_precision=args.mixed_precision					)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 UpperCamelCase     : Tuple	     =       config["lr"]
 UpperCamelCase     : Optional[Any]	     =       int(config["num_epochs"]					)
 UpperCamelCase     : List[str]	     =       int(config["seed"]					)
 UpperCamelCase     : Optional[Any]	     =       int(config["batch_size"]					)

 UpperCamelCase     : List[str]	     =       evaluate.load("glue"							,       "mrpc"					)

 # If the batch size is too big we use gradient accumulation
 UpperCamelCase     : Tuple	     =       1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  UpperCamelCase     : Union[str, Any]	     =       batch_size // MAX_GPU_BATCH_SIZE
  UpperCamelCase     : Optional[Any]	     =       MAX_GPU_BATCH_SIZE

 set_seed(_lowerCAmelCase					)

 # New Code #
 # Create our folds:
 UpperCamelCase     : Any	     =       kfold.split(np.zeros(datasets["train"].num_rows					)							,       datasets["train"]["label"]					)
 UpperCamelCase     : Optional[int]	     =       []
 # Iterate over them
 for i, (train_idxs, valid_idxs) in enumerate(_lowerCAmelCase					):
  UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Union[str, Any]	     =       get_fold_dataloaders(
      _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       )
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCamelCase     : Optional[int]	     =       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).
  UpperCamelCase     : Union[str, Any]	     =       model.to(accelerator.device					)

  # Instantiate optimizer
  UpperCamelCase     : Optional[int]	     =       AdamW(params=model.parameters()							,       lr=_lowerCAmelCase					)

  # Instantiate scheduler
  UpperCamelCase     : Optional[Any]	     =       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.
  UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : List[Any]	     =       accelerator.prepare(
      _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)

  # Now we train the model
  for epoch in range(_lowerCAmelCase					):
   model.train()
   for step, batch in enumerate(_lowerCAmelCase					):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device					)
    UpperCamelCase     : Union[str, Any]	     =       model(**_lowerCAmelCase					)
    UpperCamelCase     : str	     =       outputs.loss
    UpperCamelCase     : List[str]	     =       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`.
    batch.to(accelerator.device					)
    with torch.no_grad():
     UpperCamelCase     : int	     =       model(**_lowerCAmelCase					)
    UpperCamelCase     : List[Any]	     =       outputs.logits.argmax(dim=-1					)
    UpperCamelCase     ,   UpperCamelCase     : Dict	     =       accelerator.gather_for_metrics((predictions, batch["labels"])					)
    metric.add_batch(
        predictions=_lowerCAmelCase							,       references=_lowerCAmelCase							,       )

   UpperCamelCase     : Optional[Any]	     =       metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(F"""epoch {epoch}:"""							,       _lowerCAmelCase					)

  # New Code #
  # We also run predictions on the test set at the very end
  UpperCamelCase     : List[Any]	     =       []
  for step, batch in enumerate(_lowerCAmelCase					):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device					)
   with torch.no_grad():
    UpperCamelCase     : Any	     =       model(**_lowerCAmelCase					)
   UpperCamelCase     : Optional[int]	     =       outputs.logits
   UpperCamelCase     ,   UpperCamelCase     : Tuple	     =       accelerator.gather_for_metrics((predictions, batch["labels"])					)
   fold_predictions.append(predictions.cpu()					)
   if i == 0:
    # We need all of the test predictions
    test_references.append(references.cpu()					)
        # Use accelerator.print to print only on the main process.
  test_predictions.append(torch.cat(_lowerCAmelCase							,       dim=0					)					)
  # We now need to release all our memory and get rid of the current model, optimizer, etc
  accelerator.free_memory()
 # New Code #
 # Finally we check the accuracy of our folded results:
 UpperCamelCase     : List[Any]	     =       torch.cat(_lowerCAmelCase							,       dim=0					)
 UpperCamelCase     : Optional[int]	     =       torch.stack(_lowerCAmelCase							,       dim=0					).sum(dim=0					).div(int(args.num_folds					)					).argmax(dim=-1					)
 UpperCamelCase     : Optional[Any]	     =       metric.compute(predictions=_lowerCAmelCase							,       references=_lowerCAmelCase					)
 accelerator.print("Average test metrics from all folds:"							,       _lowerCAmelCase					)



def 		A_    (					)						->			List[str]:
 UpperCamelCase     : Union[str, Any]	     =       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."					)
 # New Code #
 parser.add_argument("--num_folds"							,       type=_lowerCAmelCase							,       default=3							,       help="The number of splits to perform across the dataset"					)
 UpperCamelCase     : Union[str, Any]	     =       parser.parse_args()
 UpperCamelCase     : Tuple	     =       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(_lowerCAmelCase							,       _lowerCAmelCase					)


if __name__ == "__main__":
    main()







from pathlib import Path

import numpy as np
from PIL import Image



def 		A_    (					_lowerCAmelCase					)						->			np.ndarray:
 UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Optional[Any]	     =       rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
 return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b



def 		A_    (					_lowerCAmelCase					)						->			np.ndarray:
 return (gray > 127) & (gray <= 255)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			np.ndarray:
 UpperCamelCase     : Dict	     =       np.zeros_like(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       np.zeros(
     (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

 # Copy image to padded image
 UpperCamelCase     : Tuple	     =       image

 # Iterate over image & apply kernel
 for x in range(image.shape[1]					):
  for y in range(image.shape[0]					):
   UpperCamelCase     : List[str]	     =       (
       kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
   ).sum()
   UpperCamelCase     : int	     =       int(summation > 0					)
 return output


if __name__ == "__main__":
    # read original image
    __lowerCamelCase	:					int			  =					Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
    __lowerCamelCase	:					Tuple			  =					np.array(Image.open(lena_path))
    # kernel to be applied
    __lowerCamelCase	:					Optional[Any]			  =					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
    __lowerCamelCase	:					Union[str, Any]			  =					dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
    # Save the output image
    __lowerCamelCase	:					Any			  =					Image.fromarray(output).convert("""RGB""")
    pil_img.save("""result_dilation.png""")
