






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


__lowerCAmelCase							=16
__lowerCAmelCase							=32

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				Any:






					"""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 							a	(   )				->				str:






					"""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()









'''simple docstring'''
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 ConvNextImageProcessor


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__=7   , UpperCAmelCase__=3   , UpperCAmelCase__=18   , UpperCAmelCase__=30   , UpperCAmelCase__=400   , UpperCAmelCase__=True   , UpperCAmelCase__=None   , UpperCAmelCase__=0.8_7_5   , UpperCAmelCase__=True   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , )      ->	Optional[int]:
										a_									=   size if size is not None else {'shortest_edge': 20}
										a_									=   parent
										a_									=   batch_size
										a_									=   num_channels
										a_									=   image_size
										a_									=   min_resolution
										a_									=   max_resolution
										a_									=   do_resize
										a_									=   size
										a_									=   crop_pct
										a_									=   do_normalize
										a_									=   image_mean
										a_									=   image_std




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_pct": self.crop_pct,
										}



@require_torch
@require_vision
class    _snake_case   (				snake_case     ,					unittest.TestCase					):

					"""simple docstring"""





					_UpperCamelCase		 =      ConvNextImageProcessor if is_vision_available() else None




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   ConvNextImageProcessingTester(self     )




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




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_resize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'size'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'crop_pct'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_normalize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_mean'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_std'     )     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   self.image_processing_class.from_dict(self.image_processor_dict     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 20}     )

										a_									=   self.image_processing_class.from_dict(self.image_processor_dict   , size=42     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 42}     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , Image.Image     )

										# Test not batched input
										a_									=   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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , 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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__   , numpify=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , np.ndarray     )

										# Test not batched input
										a_									=   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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , 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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__   , torchify=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , torch.Tensor     )

										# Test not batched input
										a_									=   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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , 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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

