





'''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,
# specifically showcasing the checkpointing capability,
# 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
#
########################################################################

A :			int							   =      16
A :			List[Any]							   =      32





def    lowercase_   (		lowercase__					,	lowercase__ = 16    ) ->Optional[int]:
						_snake_case: Tuple	       =		AutoTokenizer.from_pretrained('bert-base-cased'    )
						_snake_case: Optional[Any]	       =		load_dataset('glue'					,	'mrpc'    )

						def tokenize_function(lowercase__    ):
												# max_length=None => use the model max length (it's actually the default)
												_snake_case: str	       =		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():
												_snake_case: List[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
						_snake_case: List[Any]	       =		tokenized_datasets.rename_column('label'					,	'labels'    )

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

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

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

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		A :			Optional[Any]							   =      mocked_dataloaders  # noqa: F811





def    lowercase_   (		lowercase__					,	lowercase__    ) ->Union[str, Any]:
						# For testing only
						if os.environ.get('TESTING_MOCKED_DATALOADERS'					,	a_    ) == "1":
												_snake_case: Tuple	       =		2
						# Initialize accelerator
						_snake_case: Dict	       =		Accelerator(cpu=args.cpu					,	mixed_precision=args.mixed_precision    )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_snake_case: str	       =		config['lr']
						_snake_case: List[str]	       =		int(config['num_epochs']    )
						_snake_case: Any	       =		int(config['seed']    )
						_snake_case: Optional[int]	       =		int(config['batch_size']    )

						# New Code #
						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps					,	'isdigit'    ):
												if args.checkpointing_steps == "epoch":
																		_snake_case: Any	       =		args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		_snake_case: int	       =		int(args.checkpointing_steps    )
												else:
																		raise ValueError(
																		    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
						else:
												_snake_case: List[str]	       =		None

						set_seed(a_    )

						_snake_case       ,    _snake_case: str	       =		get_dataloaders(a_					,	a_    )
						_snake_case: List[Any]	       =		evaluate.load('glue'					,	'mrpc'    )

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

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_snake_case: Tuple	       =		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).
						_snake_case: int	       =		model.to(accelerator.device    )

						# Instantiate optimizer
						_snake_case: List[str]	       =		AdamW(params=model.parameters()					,	lr=a_    )

						# Instantiate scheduler
						_snake_case: Union[str, Any]	       =		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.
						_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case: Optional[Any]	       =		accelerator.prepare(
						    a_					,	a_					,	a_					,	a_					,	a_    )

						# New Code #
						# We need to keep track of how many total steps we have iterated over
						_snake_case: Union[str, Any]	       =		0
						# We also need to keep track of the stating epoch so files are named properly
						_snake_case: Any	       =		0

						# We need to load the checkpoint back in before training here with `load_state`
						# The total number of epochs is adjusted based on where the state is being loaded from,
						# as we assume continuation of the same training script
						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    )
																		_snake_case: str	       =		os.path.basename(args.resume_from_checkpoint    )
												else:
																		# Get the most recent checkpoint
																		_snake_case: Dict	       =		[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime    )
																		_snake_case: Optional[Any]	       =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												_snake_case: Union[str, Any]	       =		os.path.splitext(a_    )[0]

												if "epoch" in training_difference:
																		_snake_case: Optional[int]	       =		int(training_difference.replace('epoch_'					,	''    )    ) + 1
																		_snake_case: str	       =		None
												else:
																		_snake_case: int	       =		int(training_difference.replace('step_'					,	''    )    )
																		_snake_case: Any	       =		resume_step // len(a_    )
																		resume_step -= starting_epoch * len(a_    )

    # Now we train the model
						for epoch in range(a_					,	a_    ):
												model.train()
												# New Code #
												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
																		_snake_case: int	       =		accelerator.skip_first_batches(a_					,	a_    )
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		_snake_case: Dict	       =		train_dataloader
												for step, batch in enumerate(a_    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		_snake_case: List[Any]	       =		model(**a_    )
																		_snake_case: List[str]	       =		outputs.loss
																		_snake_case: Dict	       =		loss / gradient_accumulation_steps
																		accelerator.backward(a_    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()
																		# New Code #
																		overall_step += 1

																		# New Code #
																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																		# These are saved to folders named `step_{overall_step}`
																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																		# If mixed precision was used, will also save a "scalar.bin" file
																		if isinstance(a_					,	a_    ):
																								_snake_case: int	       =		F'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				_snake_case: Dict	       =		os.path.join(args.output_dir					,	a_    )
																														accelerator.save_state(a_    )

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

												_snake_case: Union[str, Any]	       =		metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''					,	a_    )

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `epoch_{epoch}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if checkpointing_steps == "epoch":
																		_snake_case: List[Any]	       =		F'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								_snake_case: Optional[Any]	       =		os.path.join(args.output_dir					,	a_    )
																		accelerator.save_state(a_    )





def    lowercase_   (		) ->List[str]:
						_snake_case: Union[str, Any]	       =		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.'    )
						parser.add_argument(
						    '--checkpointing_steps'					,	type=a_					,	default=a_					,	help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'					,	)
						parser.add_argument(
						    '--output_dir'					,	type=a_					,	default='.'					,	help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'					,	)
						parser.add_argument(
						    '--resume_from_checkpoint'					,	type=a_					,	default=a_					,	help='If the training should continue from a checkpoint folder.'					,	)
						_snake_case: List[Any]	       =		parser.parse_args()
						_snake_case: Optional[Any]	       =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(a_					,	a_    )


if __name__ == "__main__":
		main()







"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_  					= {
    'configuration_deformable_detr': ['DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeformableDetrConfig'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= ['DeformableDetrFeatureExtractor']
  SCREAMING_SNAKE_CASE_  					= ['DeformableDetrImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= [
      'DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DeformableDetrForObjectDetection',
      'DeformableDetrModel',
      'DeformableDetrPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
    from .image_processing_deformable_detr import DeformableDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deformable_detr import (
        DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        DeformableDetrForObjectDetection,
        DeformableDetrModel,
        DeformableDetrPreTrainedModel,
    )

else:
  import sys

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

