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			:							Optional[int]     =16
lowerCAmelCase			:							int     =32







def 						A__  (					__A   , __A   , __A   , __A   , __A = 16	):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						AutoTokenizer.from_pretrained("""bert-base-cased"""	)
    _lowerCamelCase     :		List[Any] 						=						DatasetDict(
        {
            """train""": dataset["""train"""].select(__A	),
            """validation""": dataset["""train"""].select(__A	),
            """test""": dataset["""validation"""],
        }	)

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

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

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

    # Instantiate dataloaders.
    _lowerCamelCase     :		Any 						=						DataLoader(
        tokenized_datasets["""train"""]   , shuffle=__A   , collate_fn=__A   , batch_size=__A	)
    _lowerCamelCase     :		Union[str, Any] 						=						DataLoader(
        tokenized_datasets["""validation"""]   , shuffle=__A   , collate_fn=__A   , batch_size=__A	)

    _lowerCamelCase     :		Union[str, Any] 						=						DataLoader(
        tokenized_datasets["""test"""]   , shuffle=__A   , collate_fn=__A   , batch_size=__A	)

    return train_dataloader, eval_dataloader, test_dataloader







def 						A__  (					__A   , __A	):







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

    _lowerCamelCase     :		Union[str, Any] 						=						evaluate.load("""glue"""   , """mrpc"""	)

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

    set_seed(__A	)

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

        # Instantiate optimizer
        _lowerCamelCase     :		Any 						=						AdamW(params=model.parameters()   , lr=__A	)

        # Instantiate scheduler
        _lowerCamelCase     :		Optional[Any] 						=						get_linear_schedule_with_warmup(
            optimizer=__A   , num_warmup_steps=100   , 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.
        _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Any 						=						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	)
                _lowerCamelCase     :		Optional[int] 						=						model(**__A	)
                _lowerCamelCase     :		Tuple 						=						outputs.loss
                _lowerCamelCase     :		int 						=						loss / gradient_accumulation_steps
                accelerator.backward(__A	)
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

            model.eval()
            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():
                    _lowerCamelCase     :		List[str] 						=						model(**__A	)
                _lowerCamelCase     :		Tuple 						=						outputs.logits.argmax(dim=-1	)
                _lowerCamelCase						,							_lowerCamelCase     :		int 						=						accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
                metric.add_batch(
                    predictions=__A   , references=__A   , )

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

        # New Code #
        # We also run predictions on the test set at the very end
        _lowerCamelCase     :		Optional[Any] 						=						[]
        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():
                _lowerCamelCase     :		Union[str, Any] 						=						model(**__A	)
            _lowerCamelCase     :		Tuple 						=						outputs.logits
            _lowerCamelCase						,							_lowerCamelCase     :		int 						=						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(__A   , 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:
    _lowerCamelCase     :		Any 						=						torch.cat(__A   , dim=0	)
    _lowerCamelCase     :		Dict 						=						torch.stack(__A   , dim=0	).sum(dim=0	).div(int(args.num_folds	)	).argmax(dim=-1	)
    _lowerCamelCase     :		List[Any] 						=						metric.compute(predictions=__A   , references=__A	)
    accelerator.print("""Average test metrics from all folds:"""   , __A	)







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Any 						=						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."""	)
    # New Code #
    parser.add_argument("""--num_folds"""   , type=__A   , default=3   , help="""The number of splits to perform across the dataset"""	)
    _lowerCamelCase     :		Tuple 						=						parser.parse_args()
    _lowerCamelCase     :		Any 						=						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(__A   , __A	)


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, 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


lowerCAmelCase			:							Tuple     =logging.get_logger(__name__)




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       ['pixel_values']



      def __init__(					self     :				Optional[Any]    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				PILImageResampling = PIL.Image.BICUBIC    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				Union[int, float] = 1 / 255    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  **_UpperCamelCase     :				str    ,  )				->None:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						size if size is not None else {"""height""": 256, """width""": 256}
          _lowerCamelCase     :		Optional[Any] 						=						get_size_dict(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          _lowerCamelCase     :		Any 						=						get_size_dict(_UpperCamelCase    ,  param_name="""crop_size""")

          _lowerCamelCase     :		int 						=						do_resize
          _lowerCamelCase     :		int 						=						size
          _lowerCamelCase     :		Optional[int] 						=						resample
          _lowerCamelCase     :		int 						=						do_center_crop
          _lowerCamelCase     :		Optional[Any] 						=						crop_size
          _lowerCamelCase     :		Union[str, Any] 						=						do_rescale
          _lowerCamelCase     :		List[str] 						=						rescale_factor
          _lowerCamelCase     :		List[Any] 						=						do_normalize
          _lowerCamelCase     :		Dict 						=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _lowerCamelCase     :		int 						=						image_std if image_std is not None else IMAGENET_STANDARD_STD



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Dict[str, int]    ,  _UpperCamelCase     :				PILImageResampling = PIL.Image.BICUBIC    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						get_size_dict(_UpperCamelCase)
          if "height" not in size or "width" not in size:
              raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
          return resize(
              _UpperCamelCase    ,  size=(size["""height"""], size["""width"""])    ,  resample=_UpperCamelCase    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Dict[str, int]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				List[str]    ,  )				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						get_size_dict(_UpperCamelCase)
          if "height" not in size or "width" not in size:
              raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
          return center_crop(_UpperCamelCase    ,  size=(size["""height"""], size["""width"""])    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Union[int, float]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->str:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Union[float, List[float]]    ,  _UpperCamelCase     :				Union[float, List[float]]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->np.ndarray:


          """simple docstring"""


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




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				ImageInput    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				Tuple=None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				float = None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[str, TensorType]] = None    ,  _UpperCamelCase     :				ChannelDimension = ChannelDimension.FIRST    ,  **_UpperCamelCase     :				List[Any]    ,  )				->PIL.Image.Image:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						do_resize if do_resize is not None else self.do_resize
          _lowerCamelCase     :		List[str] 						=						resample if resample is not None else self.resample
          _lowerCamelCase     :		Dict 						=						do_center_crop if do_center_crop is not None else self.do_center_crop
          _lowerCamelCase     :		Optional[Any] 						=						do_rescale if do_rescale is not None else self.do_rescale
          _lowerCamelCase     :		Dict 						=						rescale_factor if rescale_factor is not None else self.rescale_factor
          _lowerCamelCase     :		Dict 						=						do_normalize if do_normalize is not None else self.do_normalize
          _lowerCamelCase     :		int 						=						image_mean if image_mean is not None else self.image_mean
          _lowerCamelCase     :		Dict 						=						image_std if image_std is not None else self.image_std

          _lowerCamelCase     :		Optional[Any] 						=						size if size is not None else self.size
          _lowerCamelCase     :		Optional[int] 						=						get_size_dict(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						crop_size if crop_size is not None else self.crop_size
          _lowerCamelCase     :		Dict 						=						get_size_dict(_UpperCamelCase    ,  param_name="""crop_size""")

          _lowerCamelCase     :		int 						=						make_list_of_images(_UpperCamelCase)

          if not valid_images(_UpperCamelCase):
              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_center_crop and crop_size is None:
              raise ValueError("""Crop size must be specified if do_center_crop 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("""Image mean and std must be specified if do_normalize is True.""")

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

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

          if do_center_crop:
              _lowerCamelCase     :		str 						=						[self.center_crop(image=_UpperCamelCase    ,  size=_UpperCamelCase) for image in images]

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

          if do_normalize:
              _lowerCamelCase     :		List[str] 						=						[self.normalize(image=_UpperCamelCase    ,  mean=_UpperCamelCase    ,  std=_UpperCamelCase) for image in images]

          _lowerCamelCase     :		List[str] 						=						[to_channel_dimension_format(_UpperCamelCase    ,  _UpperCamelCase) for image in images]

          _lowerCamelCase     :		int 						=						{"""pixel_values""": images}
          return BatchFeature(data=_UpperCamelCase    ,  tensor_type=_UpperCamelCase)




