

'''simple docstring'''

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
#
########################################################################


UpperCAmelCase					 =	16
UpperCAmelCase					 =	32

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any],       lowercase__    :	int,       lowercase__    :	Optional[int],       lowercase__    :	Optional[Any],       lowercase__    :	Union[str, Any] = 16			):






     '''simple docstring'''





     __lowercase      		=AutoTokenizer.from_pretrained('bert-base-cased'			)
     __lowercase      		=DatasetDict(
         {
             'train': dataset['train'].select(lowercase__			),
             'validation': dataset['train'].select(lowercase__			),
             'test': dataset['validation'],
         }			)

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

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

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

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

     __lowercase      		=DataLoader(
         tokenized_datasets['test'],       shuffle=lowercase__,       collate_fn=lowercase__,       batch_size=lowercase__			)

     return train_dataloader, eval_dataloader, test_dataloader

def 					__UpperCamelCase				(						lowercase__    :	List[str],       lowercase__    :	Any			):






     '''simple docstring'''





     __lowercase      		=[]
     # Download the dataset
     __lowercase      		=load_dataset('glue',       'mrpc'			)
     # Create our splits
     __lowercase      		=StratifiedKFold(n_splits=int(args.num_folds			)			)
     # Initialize accelerator
     __lowercase      		=Accelerator(cpu=args.cpu,       mixed_precision=args.mixed_precision			)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __lowercase      		=config['lr']
     __lowercase      		=int(config['num_epochs']			)
     __lowercase      		=int(config['seed']			)
     __lowercase      		=int(config['batch_size']			)

     __lowercase      		=evaluate.load('glue',       'mrpc'			)

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

     set_seed(lowercase__			)

     # New Code #
     # Create our folds:
     __lowercase      		=kfold.split(np.zeros(datasets['train'].num_rows			),       datasets['train']['label']			)
     __lowercase      		=[]
     # Iterate over them
     for i, (train_idxs, valid_idxs) in enumerate(lowercase__			):
          __lowercase		,  __lowercase		,  __lowercase      		=get_fold_dataloaders(
              lowercase__,       lowercase__,       lowercase__,       lowercase__,       )
          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          __lowercase      		=AutoModelForSequenceClassification.from_pretrained('bert-base-cased',       return_dict=lowercase__			)

          # 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).
          __lowercase      		=model.to(accelerator.device			)

          # Instantiate optimizer
          __lowercase      		=AdamW(params=model.parameters(),       lr=lowercase__			)

          # Instantiate scheduler
          __lowercase      		=get_linear_schedule_with_warmup(
              optimizer=lowercase__,       num_warmup_steps=1_00,       num_training_steps=(len(lowercase__			) * 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.
          __lowercase		,  __lowercase		,  __lowercase		,  __lowercase		,  __lowercase      		=accelerator.prepare(
              lowercase__,       lowercase__,       lowercase__,       lowercase__,       lowercase__			)

          # Now we train the model
          for epoch in range(lowercase__			):
               model.train()
               for step, batch in enumerate(lowercase__			):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device			)
                    __lowercase      		=model(**lowercase__			)
                    __lowercase      		=outputs.loss
                    __lowercase      		=loss / gradient_accumulation_steps
                    accelerator.backward(lowercase__			)
                    if step % gradient_accumulation_steps == 0:
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()

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

               __lowercase      		=metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(F'''epoch {epoch}:''',       lowercase__			)

          # New Code #
          # We also run predictions on the test set at the very end
          __lowercase      		=[]
          for step, batch in enumerate(lowercase__			):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device			)
               with torch.no_grad():
                    __lowercase      		=model(**lowercase__			)
               __lowercase      		=outputs.logits
               __lowercase		,  __lowercase      		=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(lowercase__,       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:
     __lowercase      		=torch.cat(lowercase__,       dim=0			)
     __lowercase      		=torch.stack(lowercase__,       dim=0			).sum(dim=0			).div(int(args.num_folds			)			).argmax(dim=-1			)
     __lowercase      		=metric.compute(predictions=lowercase__,       references=lowercase__			)
     accelerator.print('Average test metrics from all folds:',       lowercase__			)

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		=argparse.ArgumentParser(description='Simple example of training script.'			)
     parser.add_argument(
         '--mixed_precision',       type=lowercase__,       default=lowercase__,       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=lowercase__,       default=3,       help='The number of splits to perform across the dataset'			)
     __lowercase      		=parser.parse_args()
     __lowercase      		={'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(lowercase__,       lowercase__			)


if __name__ == "__main__":
       main()






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase       							=				{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase       							=				[
		    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPSegModel''',
		    '''CLIPSegPreTrainedModel''',
		    '''CLIPSegTextModel''',
		    '''CLIPSegVisionModel''',
		    '''CLIPSegForImageSegmentation''',
		]

if TYPE_CHECKING:
		from .configuration_clipseg import (
		    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPSegConfig,
		    CLIPSegTextConfig,
		    CLIPSegVisionConfig,
		)
		from .processing_clipseg import CLIPSegProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clipseg import (
				    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPSegForImageSegmentation,
				    CLIPSegModel,
				    CLIPSegPreTrainedModel,
				    CLIPSegTextModel,
				    CLIPSegVisionModel,
				)

else:
		import sys

		__UpperCAmelCase       							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)