






"""simple docstring"""


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


lowercase_								=	16
lowercase_								=	32



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  if args.with_tracking:
    __A		     =					Accelerator(
        cpu=args.cpu						,			mixed_precision=args.mixed_precision						,			log_with='''all'''						,			project_dir=args.project_dir					)
  else:
    __A		     =					Accelerator(cpu=args.cpu						,			mixed_precision=args.mixed_precision					)

  if hasattr(args.checkpointing_steps						,			'''isdigit'''					):
    if args.checkpointing_steps == "epoch":
      __A		     =					args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      __A		     =					int(args.checkpointing_steps					)
    else:
      raise ValueError(
          f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'					)
  else:
    __A		     =					None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __A		     =					config['''lr''']
  __A		     =					int(config['''num_epochs''']					)
  __A		     =					int(config['''seed''']					)
  __A		     =					int(config['''batch_size''']					)

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    __A		     =					os.path.split(__UpperCamelCase					)[-1].split('''.'''					)[0]
    accelerator.init_trackers(__UpperCamelCase						,			__UpperCamelCase					)

  __A		     =					AutoTokenizer.from_pretrained('''bert-base-cased'''					)
  __A		     =					load_dataset('''glue'''						,			'''mrpc'''					)
  __A		     =					evaluate.load('''glue'''						,			'''mrpc'''					)

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

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

  def collate_fn(__UpperCamelCase					):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    __A		     =					1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      __A		     =					1_6
    elif accelerator.mixed_precision != "no":
      __A		     =					8
    else:
      __A		     =					None

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

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

  set_seed(__UpperCamelCase					)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  __A		     =					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''						,			return_dict=__UpperCamelCase					)

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

  # Instantiate optimizer
  __A		     =					AdamW(params=model.parameters()						,			lr=__UpperCamelCase					)

  # Instantiate scheduler
  __A		     =					get_linear_schedule_with_warmup(
      optimizer=__UpperCamelCase						,			num_warmup_steps=1_0_0						,			num_training_steps=(len(__UpperCamelCase					) * 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.
  __A     ,       __A     ,       __A     ,       __A     ,       __A		     =					accelerator.prepare(
      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)

  # We need to keep track of how many total steps we have iterated over
  __A		     =					0
  # We also need to keep track of the stating epoch so files are named properly
  __A		     =					0

  # Potentially load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'					)
      accelerator.load_state(args.resume_from_checkpoint					)
      __A		     =					os.path.basename(args.resume_from_checkpoint					)
    else:
      # Get the most recent checkpoint
      __A		     =					[f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
      dirs.sort(key=os.path.getctime					)
      __A		     =					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    __A		     =					os.path.splitext(__UpperCamelCase					)[0]

    if "epoch" in training_difference:
      __A		     =					int(training_difference.replace('''epoch_'''						,			''''''					)					) + 1
      __A		     =					None
    else:
      __A		     =					int(training_difference.replace('''step_'''						,			''''''					)					)
      __A		     =					resume_step // len(__UpperCamelCase					)
      resume_step -= starting_epoch * len(__UpperCamelCase					)

    # Now we train the model
  for epoch in range(__UpperCamelCase						,			__UpperCamelCase					):
    model.train()
    if args.with_tracking:
      __A		     =					0
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      __A		     =					accelerator.skip_first_batches(__UpperCamelCase						,			__UpperCamelCase					)
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      __A		     =					train_dataloader
    for step, batch in enumerate(__UpperCamelCase					):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device					)
      __A		     =					model(**__UpperCamelCase					)
      __A		     =					outputs.loss
      __A		     =					loss / gradient_accumulation_steps
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(__UpperCamelCase					)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

      if isinstance(__UpperCamelCase						,			__UpperCamelCase					):
        __A		     =					f'step_{overall_step}'
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            __A		     =					os.path.join(args.output_dir						,			__UpperCamelCase					)
          accelerator.save_state(__UpperCamelCase					)

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

    __A		     =					metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}:'						,			__UpperCamelCase					)
    if args.with_tracking:
      accelerator.log(
          {
              '''accuracy''': eval_metric['''accuracy'''],
              '''f1''': eval_metric['''f1'''],
              '''train_loss''': total_loss.item() / len(__UpperCamelCase					),
              '''epoch''': epoch,
          }						,			step=__UpperCamelCase						,			)

    if checkpointing_steps == "epoch":
      __A		     =					f'epoch_{epoch}'
      if args.output_dir is not None:
        __A		     =					os.path.join(args.output_dir						,			__UpperCamelCase					)
      accelerator.save_state(__UpperCamelCase					)

  if args.with_tracking:
    accelerator.end_training()



def    lowerCAmelCase							(       ):




  """simple docstring"""







  __A		     =					argparse.ArgumentParser(description='''Simple example of training script.'''					)
  parser.add_argument(
      '''--mixed_precision'''						,			type=__UpperCamelCase						,			default=__UpperCamelCase						,			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.'''					)
  parser.add_argument(
      '''--checkpointing_steps'''						,			type=__UpperCamelCase						,			default=__UpperCamelCase						,			help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''						,			)
  parser.add_argument(
      '''--resume_from_checkpoint'''						,			type=__UpperCamelCase						,			default=__UpperCamelCase						,			help='''If the training should continue from a checkpoint folder.'''						,			)
  parser.add_argument(
      '''--with_tracking'''						,			action='''store_true'''						,			help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''						,			)
  parser.add_argument(
      '''--output_dir'''						,			type=__UpperCamelCase						,			default='''.'''						,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''						,			)
  parser.add_argument(
      '''--project_dir'''						,			type=__UpperCamelCase						,			default='''logs'''						,			help='''Location on where to store experiment tracking logs` and relevent project information'''						,			)
  __A		     =					parser.parse_args()
  __A		     =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
  training_function(__UpperCamelCase						,			__UpperCamelCase					)


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


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				def __init__( self							:	Union[str, Any]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	List[str]=7   , __lowerCamelCase							:	int=3   , __lowerCamelCase							:	Any=18   , __lowerCamelCase							:	Optional[int]=30   , __lowerCamelCase							:	Union[str, Any]=4_00   , __lowerCamelCase							:	Optional[int]=True   , __lowerCamelCase							:	List[str]=1 / 2_55   , __lowerCamelCase							:	Any=True   , __lowerCamelCase							:	Optional[int]=8   , )   ->	Optional[Any]:
							a       =     parent
							a       =     batch_size
							a       =     num_channels
							a       =     image_size
							a       =     min_resolution
							a       =     max_resolution
							a       =     do_rescale
							a       =     rescale_factor
							a       =     do_pad
							a       =     pad_size





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Optional[int]:
							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	Any=False   , __lowerCamelCase							:	List[Any]=False				)   ->	List[Any]:
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
										a       =     []
										for i in range(self.batch_size				):
													image_inputs.append(
													    np.random.randint(
													        2_55   , size=(self.num_channels, self.max_resolution, self.max_resolution)   , dtype=np.uinta				)				)
							else:
										a       =     []
										for i in range(self.batch_size				):
													a       ,						a       =     np.random.choice(np.arange(self.min_resolution   , self.max_resolution				)   , 2				)
													image_inputs.append(np.random.randint(2_55   , size=(self.num_channels, width, height)   , dtype=np.uinta				)				)

							if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										a       =     [Image.fromarray(np.moveaxis(__lowerCamelCase   , 0   , -1				)				) for x in image_inputs]

							if torchify:
										a       =     [torch.from_numpy(__lowerCamelCase				) for x in image_inputs]

							return image_inputs




@require_torch
@require_vision
class        snake_case__				(_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   SwinaSRImageProcessor if is_vision_available() else None





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     SwinaSRImageProcessingTester(self				)





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





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_rescale"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "rescale_factor"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_pad"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "pad_size"				)				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Union[str, Any]:
							pass





				def 				__UpperCAmelCase				( self							:	int   , __lowerCamelCase							:	int				)   ->	Dict:
							a       ,						a       =     get_image_size(__lowerCamelCase				)
							a       =     self.image_processor_tester.pad_size

							a       =     (old_height // size + 1) * size - old_height
							a       =     (old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , Image.Image				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(np.array(image_inputs[0]				)				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Union[str, Any]:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase   , numpify=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , np.ndarray				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	int:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase   , torchify=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , torch.Tensor				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )






