'''simple docstring'''





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor






class 				UpperCAmelCase (       unittest.TestCase ):




				'''simple docstring'''







				def __init__(       self    ,							lowercase__    ,							lowercase__=7    ,							lowercase__=3    ,							lowercase__=30    ,							lowercase__=400    ,							lowercase__=True    ,							lowercase__=None    ,							lowercase__=True    ,							lowercase__=[0.5, 0.5, 0.5]    ,							lowercase__=[0.5, 0.5, 0.5]    ,							lowercase__=True    ,							lowercase__=1 / 255    ,							lowercase__=True    ,							)							-> Any:
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       parent
									SCREAMING_SNAKE_CASE					:			str    	=       batch_size
									SCREAMING_SNAKE_CASE					:			Tuple    	=       num_channels
									SCREAMING_SNAKE_CASE					:			Any    	=       min_resolution
									SCREAMING_SNAKE_CASE					:			Any    	=       max_resolution
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       do_resize
									SCREAMING_SNAKE_CASE					:			Dict    	=       size
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       do_normalize
									SCREAMING_SNAKE_CASE					:			Tuple    	=       image_mean
									SCREAMING_SNAKE_CASE					:			Dict    	=       image_std
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       do_rescale
									SCREAMING_SNAKE_CASE					:			List[str]    	=       rescale_factor
									SCREAMING_SNAKE_CASE					:			List[str]    	=       do_pad






				def       _UpperCamelCase    (       self )							-> Optional[Any]:
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}






				def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=False )							-> Any:
									if not batched:
														SCREAMING_SNAKE_CASE					:			int    	=       image_inputs[0]
														if isinstance(lowercase__    ,							Image.Image ):
																			SCREAMING_SNAKE_CASE					:			str    	=       image.size
														else:
																			SCREAMING_SNAKE_CASE					:			Any    	=       image.shape[1], image.shape[2]
														if w < h:
																			SCREAMING_SNAKE_CASE					:			Optional[int]    	=       int(self.size['shortest_edge'] * h / w )
																			SCREAMING_SNAKE_CASE					:			int    	=       self.size['shortest_edge']
														elif w > h:
																			SCREAMING_SNAKE_CASE					:			List[str]    	=       self.size['shortest_edge']
																			SCREAMING_SNAKE_CASE					:			Optional[int]    	=       int(self.size['shortest_edge'] * w / h )
														else:
																			SCREAMING_SNAKE_CASE					:			int    	=       self.size['shortest_edge']
																			SCREAMING_SNAKE_CASE					:			str    	=       self.size['shortest_edge']

									else:
														SCREAMING_SNAKE_CASE					:			List[str]    	=       []
														for image in image_inputs:
																			SCREAMING_SNAKE_CASE					:			str    	=       self.get_expected_values([image] )
																			expected_values.append((expected_height, expected_width) )
														SCREAMING_SNAKE_CASE					:			Tuple    	=       max(lowercase__    ,							key=lambda lowercase__ : item[0] )[0]
														SCREAMING_SNAKE_CASE					:			str    	=       max(lowercase__    ,							key=lambda lowercase__ : item[1] )[1]

									return expected_height, expected_width






@require_torch
@require_vision
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




				'''simple docstring'''







				snake_case__							:   Union[str, Any]							=						ConditionalDetrImageProcessor if is_vision_available() else None






				def       _UpperCamelCase    (       self )							-> Optional[Any]:
									SCREAMING_SNAKE_CASE					:			Dict    	=       ConditionalDetrImageProcessingTester(self )






				@property
				def       _UpperCamelCase    (       self )							-> Optional[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()






				def       _UpperCamelCase    (       self )							-> Optional[int]:
									SCREAMING_SNAKE_CASE					:			Any    	=       self.image_processing_class(**self.image_processor_dict )
									self.assertTrue(hasattr(lowercase__    ,							'image_mean' ) )
									self.assertTrue(hasattr(lowercase__    ,							'image_std' ) )
									self.assertTrue(hasattr(lowercase__    ,							'do_normalize' ) )
									self.assertTrue(hasattr(lowercase__    ,							'do_resize' ) )
									self.assertTrue(hasattr(lowercase__    ,							'size' ) )






				def       _UpperCamelCase    (       self )							-> List[Any]:
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.image_processing_class.from_dict(self.image_processor_dict )
									self.assertEqual(image_processor.size    ,							{'shortest_edge': 18, 'longest_edge': 1_333} )
									self.assertEqual(image_processor.do_pad    ,							lowercase__ )

									SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.image_processing_class.from_dict(
									    self.image_processor_dict    ,							size=42    ,							max_size=84    ,							pad_and_return_pixel_mask=lowercase__ )
									self.assertEqual(image_processor.size    ,							{'shortest_edge': 42, 'longest_edge': 84} )
									self.assertEqual(image_processor.do_pad    ,							lowercase__ )






				def       _UpperCamelCase    (       self )							-> Tuple:
									pass






				def       _UpperCamelCase    (       self )							-> str:
									# Initialize image_processing
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.image_processing_class(**self.image_processor_dict )
									# create random PIL images
									SCREAMING_SNAKE_CASE					:			Dict    	=       prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=lowercase__ )
									for image in image_inputs:
														self.assertIsInstance(lowercase__    ,							Image.Image )

									# Test not batched input
									SCREAMING_SNAKE_CASE					:			List[str]    	=       image_processing(image_inputs[0]    ,							return_tensors='pt' ).pixel_values

									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.image_processor_tester.get_expected_values(lowercase__ )

									self.assertEqual(
									    encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

									# Test batched
									SCREAMING_SNAKE_CASE					:			Any    	=       self.image_processor_tester.get_expected_values(lowercase__    ,							batched=lowercase__ )

									SCREAMING_SNAKE_CASE					:			List[str]    	=       image_processing(lowercase__    ,							return_tensors='pt' ).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )    ,							)






				def       _UpperCamelCase    (       self )							-> Any:
									# Initialize image_processing
									SCREAMING_SNAKE_CASE					:			str    	=       self.image_processing_class(**self.image_processor_dict )
									# create random numpy tensors
									SCREAMING_SNAKE_CASE					:			Any    	=       prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=lowercase__    ,							numpify=lowercase__ )
									for image in image_inputs:
														self.assertIsInstance(lowercase__    ,							np.ndarray )

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

									SCREAMING_SNAKE_CASE					:			Tuple    	=       self.image_processor_tester.get_expected_values(lowercase__ )

									self.assertEqual(
									    encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

									# Test batched
									SCREAMING_SNAKE_CASE					:			int    	=       image_processing(lowercase__    ,							return_tensors='pt' ).pixel_values

									SCREAMING_SNAKE_CASE					:			Tuple    	=       self.image_processor_tester.get_expected_values(lowercase__    ,							batched=lowercase__ )

									self.assertEqual(
									    encoded_images.shape    ,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )    ,							)






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

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

									SCREAMING_SNAKE_CASE					:			str    	=       self.image_processor_tester.get_expected_values(lowercase__ )

									self.assertEqual(
									    encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

									# Test batched
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image_processing(lowercase__    ,							return_tensors='pt' ).pixel_values

									SCREAMING_SNAKE_CASE					:			int    	=       self.image_processor_tester.get_expected_values(lowercase__    ,							batched=lowercase__ )

									self.assertEqual(
									    encoded_images.shape    ,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )    ,							)






				@slow
				def       _UpperCamelCase    (       self )							-> int:
									# prepare image and target
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
									with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,							'r' ) as f:
														SCREAMING_SNAKE_CASE					:			Tuple    	=       json.loads(f.read() )

									SCREAMING_SNAKE_CASE					:			List[str]    	=       {'image_id': 39_769, 'annotations': target}

									# encode them
									SCREAMING_SNAKE_CASE					:			Tuple    	=       ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50' )
									SCREAMING_SNAKE_CASE					:			Tuple    	=       image_processing(images=lowercase__    ,							annotations=lowercase__    ,							return_tensors='pt' )

									# verify pixel values
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.Size([1, 3, 800, 1_066] )
									self.assertEqual(encoding['pixel_values'].shape    ,							lowercase__ )

									SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,							lowercase__    ,							atol=1E-4 ) )

									# verify area
									SCREAMING_SNAKE_CASE					:			int    	=       torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,							lowercase__ ) )
									# verify boxes
									SCREAMING_SNAKE_CASE					:			int    	=       torch.Size([6, 4] )
									self.assertEqual(encoding['labels'][0]['boxes'].shape    ,							lowercase__ )
									SCREAMING_SNAKE_CASE					:			Dict    	=       torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,							lowercase__    ,							atol=1E-3 ) )
									# verify image_id
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor([39_769] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,							lowercase__ ) )
									# verify is_crowd
									SCREAMING_SNAKE_CASE					:			Dict    	=       torch.tensor([0, 0, 0, 0, 0, 0] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,							lowercase__ ) )
									# verify class_labels
									SCREAMING_SNAKE_CASE					:			Any    	=       torch.tensor([75, 75, 63, 65, 17, 17] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,							lowercase__ ) )
									# verify orig_size
									SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([480, 640] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,							lowercase__ ) )
									# verify size
									SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.tensor([800, 1_066] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,							lowercase__ ) )






				@slow
				def       _UpperCamelCase    (       self )							-> Any:
									# prepare image, target and masks_path
									SCREAMING_SNAKE_CASE					:			Tuple    	=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
									with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,							'r' ) as f:
														SCREAMING_SNAKE_CASE					:			Optional[int]    	=       json.loads(f.read() )

									SCREAMING_SNAKE_CASE					:			Any    	=       {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )

									# encode them
									SCREAMING_SNAKE_CASE					:			str    	=       ConditionalDetrImageProcessor(format='coco_panoptic' )
									SCREAMING_SNAKE_CASE					:			str    	=       image_processing(images=lowercase__    ,							annotations=lowercase__    ,							masks_path=lowercase__    ,							return_tensors='pt' )

									# verify pixel values
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.Size([1, 3, 800, 1_066] )
									self.assertEqual(encoding['pixel_values'].shape    ,							lowercase__ )

									SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,							lowercase__    ,							atol=1E-4 ) )

									# verify area
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,							lowercase__ ) )
									# verify boxes
									SCREAMING_SNAKE_CASE					:			Dict    	=       torch.Size([6, 4] )
									self.assertEqual(encoding['labels'][0]['boxes'].shape    ,							lowercase__ )
									SCREAMING_SNAKE_CASE					:			int    	=       torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,							lowercase__    ,							atol=1E-3 ) )
									# verify image_id
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor([39_769] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,							lowercase__ ) )
									# verify is_crowd
									SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.tensor([0, 0, 0, 0, 0, 0] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,							lowercase__ ) )
									# verify class_labels
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor([17, 17, 63, 75, 75, 93] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,							lowercase__ ) )
									# verify masks
									SCREAMING_SNAKE_CASE					:			Dict    	=       822_873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,							lowercase__ )
									# verify orig_size
									SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([480, 640] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,							lowercase__ ) )
									# verify size
									SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor([800, 1_066] )
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,							lowercase__ ) )




'''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
#
########################################################################


_lowerCAmelCase  :Tuple  				=    16
_lowerCAmelCase  :List[Any]  				=    32


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_       ,      a_ = 16		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       AutoTokenizer.from_pretrained('bert-base-cased'		)
     SCREAMING_SNAKE_CASE					:			Union[str, 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)
          SCREAMING_SNAKE_CASE					:			Any    	=       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():
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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
     SCREAMING_SNAKE_CASE					:			Dict    	=       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.
          SCREAMING_SNAKE_CASE					:			Optional[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":
               SCREAMING_SNAKE_CASE					:			Any    	=       16
          elif accelerator.mixed_precision != "no":
               SCREAMING_SNAKE_CASE					:			Any    	=       8
          else:
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       None

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

     # Instantiate dataloaders.
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       DataLoader(
         tokenized_datasets['train']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)
     SCREAMING_SNAKE_CASE					:			int    	=       DataLoader(
         tokenized_datasets['validation']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)

     SCREAMING_SNAKE_CASE					:			Any    	=       DataLoader(
         tokenized_datasets['test']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)

     return train_dataloader, eval_dataloader, test_dataloader


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





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

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       evaluate.load('glue'       ,      'mrpc'		)

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

     set_seed(a_		)

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

          # Instantiate optimizer
          SCREAMING_SNAKE_CASE					:			int    	=       AdamW(params=model.parameters()       ,      lr=a_		)

          # Instantiate scheduler
          SCREAMING_SNAKE_CASE					:			int    	=       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.
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[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		)
                    SCREAMING_SNAKE_CASE					:			Dict    	=       model(**a_		)
                    SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       outputs.loss
                    SCREAMING_SNAKE_CASE					:			List[Any]    	=       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():
                         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(**a_		)
                    SCREAMING_SNAKE_CASE					:			Any    	=       outputs.logits.argmax(dim=-1		)
                    SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       accelerator.gather_for_metrics((predictions, batch['labels'])		)
                    metric.add_batch(
                        predictions=a_       ,      references=a_       ,      )

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


def     __lowerCAmelCase						(       )      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			int    	=       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'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       parser.parse_args()
     SCREAMING_SNAKE_CASE					:			Dict    	=       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(a_       ,      a_		)


if __name__ == "__main__":
 main()



