"""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      lowercase		(						a__	:		Accelerator ,  a__	:		DatasetDict ,  a__	:		List[int] ,  a__	:		List[int] ,  a__	:		int = 16     )						->   Dict:
					_UpperCamelCase      						=	AutoTokenizer.from_pretrained('''bert-base-cased'''     )
					_UpperCamelCase      						=	DatasetDict(
					    {
					        '''train''': dataset['''train'''].select(a__     ),
					        '''validation''': dataset['''train'''].select(a__     ),
					        '''test''': dataset['''validation'''],
					    }     )

					def tokenize_function(a__	:		int     ):
										# max_length=None => use the model max length (it's actually the default)
										_UpperCamelCase      						=	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():
										_UpperCamelCase      						=	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
					_UpperCamelCase      						=	tokenized_datasets.rename_column('''label''' ,  '''labels'''     )

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

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

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

					_UpperCamelCase      						=	DataLoader(
					    tokenized_datasets['''test'''] ,  shuffle=a__ ,  collate_fn=a__ ,  batch_size=a__     )

					return train_dataloader, eval_dataloader, test_dataloader






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

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

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

					set_seed(a__     )

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

										# Instantiate optimizer
										_UpperCamelCase      						=	AdamW(params=model.parameters() ,  lr=a__     )

										# Instantiate scheduler
										_UpperCamelCase      						=	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.
										_UpperCamelCase      						=	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     )
																				_UpperCamelCase      						=	model(**a__     )
																				_UpperCamelCase      						=	outputs.loss
																				_UpperCamelCase      						=	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():
																									_UpperCamelCase      						=	model(**a__     )
																				_UpperCamelCase      						=	outputs.logits.argmax(dim=-1     )
																				_UpperCamelCase      						=	accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
																				metric.add_batch(
																				    predictions=a__ ,  references=a__ ,  )

															_UpperCamelCase      						=	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
										_UpperCamelCase      						=	[]
										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():
																				_UpperCamelCase      						=	model(**a__     )
															_UpperCamelCase      						=	outputs.logits
															_UpperCamelCase      						=	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:
					_UpperCamelCase      						=	torch.cat(a__ ,  dim=0     )
					_UpperCamelCase      						=	torch.stack(a__ ,  dim=0     ).sum(dim=0     ).div(int(args.num_folds     )     ).argmax(dim=-1     )
					_UpperCamelCase      						=	metric.compute(predictions=a__ ,  references=a__     )
					accelerator.print('''Average test metrics from all folds:''' ,  a__     )






def      lowercase		(						)						->   str:
					_UpperCamelCase      						=	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'''     )
					_UpperCamelCase      						=	parser.parse_args()
					_UpperCamelCase      						=	{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(a__ ,  a__     )


if __name__ == "__main__":
		main()





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor




class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def __init__(       self  :  Optional[int], _snake_case  :  List[Any], _snake_case  :  str=7, _snake_case  :  Tuple=3, _snake_case  :  List[str]=3_0, _snake_case  :  Tuple=4_0_0, _snake_case  :  Any=True, _snake_case  :  List[Any]=None, _snake_case  :  int=0.9, _snake_case  :  Optional[Any]=None, _snake_case  :  str=True, _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], )   ->List[Any]:
            snake_case__							:	int   	=	size if size is not None else {'shortest_edge': 3_0}
            snake_case__							:	Tuple   	=	crop_size if crop_size is not None else {'height': 3_0, 'width': 3_0}
            snake_case__							:	Union[str, Any]   	=	parent
            snake_case__							:	Dict   	=	batch_size
            snake_case__							:	int   	=	num_channels
            snake_case__							:	Tuple   	=	min_resolution
            snake_case__							:	Any   	=	max_resolution
            snake_case__							:	List[Any]   	=	do_resize_and_center_crop
            snake_case__							:	str   	=	size
            snake_case__							:	str   	=	crop_pct
            snake_case__							:	List[str]   	=	crop_size
            snake_case__							:	Optional[int]   	=	do_normalize
            snake_case__							:	Tuple   	=	image_mean
            snake_case__							:	Tuple   	=	image_std






     def    lowercase_   (       self  :  Optional[int]					)   ->int:
            return {
                "size": self.size,
                "do_resize_and_center_crop": self.do_resize_and_center_crop,
                "crop_pct": self.crop_pct,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }




@require_torch
@require_vision
class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	PoolFormerImageProcessor if is_vision_available() else None






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Dict:
            snake_case__							:	Union[str, Any]   	=	PoolFormerImageProcessingTester(self					)






     @property
     def    lowercase_   (       self  :  int					)   ->Dict:
            return self.image_processor_tester.prepare_image_processor_dict()






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Optional[int]:
            snake_case__							:	List[str]   	=	self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(_snake_case, 'do_resize_and_center_crop'					)					)
            self.assertTrue(hasattr(_snake_case, 'size'					)					)
            self.assertTrue(hasattr(_snake_case, 'crop_pct'					)					)
            self.assertTrue(hasattr(_snake_case, 'do_normalize'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_mean'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_std'					)					)






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size, {'shortest_edge': 3_0}					)
            self.assertEqual(image_processor.crop_size, {'height': 3_0, 'width': 3_0}					)

            snake_case__							:	int   	=	self.image_processing_class.from_dict(self.image_processor_dict, size=4_2, crop_size=8_4					)
            self.assertEqual(image_processor.size, {'shortest_edge': 4_2}					)
            self.assertEqual(image_processor.crop_size, {'height': 8_4, 'width': 8_4}					)






     def    lowercase_   (       self  :  List[Any]					)   ->List[Any]:
            pass






     def    lowercase_   (       self  :  List[str]					)   ->str:
            # Initialize image_processing
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, Image.Image					)

            # Test not batched input
            snake_case__							:	Optional[int]   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values

            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	str   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






     def    lowercase_   (       self  :  int					)   ->List[Any]:
            # Initialize image_processing
            snake_case__							:	Optional[int]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            snake_case__							:	Dict   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, numpify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, np.ndarray					)

            # Test not batched input
            snake_case__							:	Dict   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	List[Any]   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            # Initialize image_processing
            snake_case__							:	Tuple   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, torchify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, torch.Tensor					)

            # Test not batched input
            snake_case__							:	Tuple   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	Optional[Any]   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






