




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

UpperCAmelCase_       				=      1_6
UpperCAmelCase_       				=      3_2

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Accelerator       ,						SCREAMING_SNAKE_CASE__			:      int = 16       ):
	'''simple docstring'''





	UpperCAmelCase__										=    AutoTokenizer.from_pretrained("""bert-base-cased"""       )
	UpperCAmelCase__										=    load_dataset("""glue"""       ,						"""mrpc"""       )

	def tokenize_function(SCREAMING_SNAKE_CASE__			:      Tuple       ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase__										=    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():
		UpperCAmelCase__										=    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
	UpperCAmelCase__										=    tokenized_datasets.rename_column("""label"""       ,						"""labels"""       )

	def collate_fn(SCREAMING_SNAKE_CASE__			:      List[str]       ):
		# 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(
		    UpperCamelCase__       ,						padding="""longest"""       ,						max_length=UpperCamelCase__       ,						pad_to_multiple_of=UpperCamelCase__       ,						return_tensors="""pt"""       ,						)

	# Instantiate dataloaders.
	UpperCAmelCase__										=    DataLoader(
	    tokenized_datasets["""train"""]       ,						shuffle=UpperCamelCase__       ,						collate_fn=UpperCamelCase__       ,						batch_size=UpperCamelCase__       )
	UpperCAmelCase__										=    DataLoader(
	    tokenized_datasets["""validation"""]       ,						shuffle=UpperCamelCase__       ,						collate_fn=UpperCamelCase__       ,						batch_size=UpperCamelCase__       )

	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

				UpperCAmelCase_       				=      mocked_dataloaders  # noqa: F811

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Dict       ,						SCREAMING_SNAKE_CASE__			:      Dict       ):
	'''simple docstring'''





	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,						UpperCamelCase__       ) == "1":
		UpperCAmelCase__										=    2
	# 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"""]       )

	# 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":
			UpperCAmelCase__										=    args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			UpperCAmelCase__										=    int(args.checkpointing_steps       )
		else:
			raise ValueError(
			    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''       )
	else:
		UpperCAmelCase__										=    None

	set_seed(UpperCamelCase__       )

	UpperCAmelCase__										=    get_dataloaders(UpperCamelCase__       ,						UpperCamelCase__       )
	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

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

	# Instantiate optimizer
	UpperCAmelCase__										=    AdamW(params=model.parameters()       ,						lr=UpperCamelCase__       )

	# Instantiate scheduler
	UpperCAmelCase__										=    get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase__       ,						num_warmup_steps=100       ,						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.
	UpperCAmelCase__										=    accelerator.prepare(
	    UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       )

	# New Code #
	# We need to keep track of how many total steps we have iterated over
	UpperCAmelCase__										=    0
	# We also need to keep track of the stating epoch so files are named properly
	UpperCAmelCase__										=    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       )
			UpperCAmelCase__										=    os.path.basename(args.resume_from_checkpoint       )
		else:
			# Get the most recent checkpoint
			UpperCAmelCase__										=    [f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
			dirs.sort(key=os.path.getctime       )
			UpperCAmelCase__										=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		UpperCAmelCase__										=    os.path.splitext(UpperCamelCase__       )[0]

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

    # Now we train the model
	for epoch in range(UpperCamelCase__       ,						UpperCamelCase__       ):
		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
			UpperCAmelCase__										=    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
			UpperCAmelCase__										=    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       )
			UpperCAmelCase__										=    model(**UpperCamelCase__       )
			UpperCAmelCase__										=    outputs.loss
			UpperCAmelCase__										=    loss / gradient_accumulation_steps
			accelerator.backward(UpperCamelCase__       )
			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(UpperCamelCase__       ,						UpperCamelCase__       ):
				UpperCAmelCase__										=    F'''step_{overall_step}'''
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						UpperCAmelCase__										=    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` (the default).
			batch.to(accelerator.device       )
			with torch.no_grad():
				UpperCAmelCase__										=    model(**UpperCamelCase__       )
			UpperCAmelCase__										=    outputs.logits.argmax(dim=-1       )
			UpperCAmelCase__										=    accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
			metric.add_batch(
			    predictions=UpperCamelCase__       ,						references=UpperCamelCase__       ,						)

		UpperCAmelCase__										=    metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''       ,						UpperCamelCase__       )

		# 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":
			UpperCAmelCase__										=    F'''epoch_{epoch}'''
			if args.output_dir is not None:
				UpperCAmelCase__										=    os.path.join(args.output_dir       ,						UpperCamelCase__       )
			accelerator.save_state(UpperCamelCase__       )

def    _UpperCamelCase      (				):
	'''simple docstring'''





	UpperCAmelCase__										=    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(
	    """--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(
	    """--resume_from_checkpoint"""       ,						type=UpperCamelCase__       ,						default=UpperCamelCase__       ,						help="""If the training should continue from a checkpoint folder."""       ,						)
	UpperCAmelCase__										=    parser.parse_args()
	UpperCAmelCase__										=    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(UpperCamelCase__       ,						UpperCamelCase__       )


if __name__ == "__main__":
				main()






'''simple docstring'''




from collections import namedtuple

UpperCAmelCase_       				=      namedtuple('from_to', 'from_ to')

UpperCAmelCase_       				=      {
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

UpperCAmelCase_       				=      {
    'mm': from_to(0.001, 1_0_0_0),
    'cm': from_to(0.01, 1_0_0),
    'm': from_to(1, 1),
    'km': from_to(1_0_0_0, 0.001),
    'in': from_to(0.0254, 39.3701),
    'ft': from_to(0.3048, 3.2_8084),
    'yd': from_to(0.9144, 1.0_9361),
    'mi': from_to(1609.34, 0.0_0062_1371),
}

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      float       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      str       ):
 '''simple docstring'''





 UpperCAmelCase__										=    from_type.lower().rstrip("""s"""       )
 UpperCAmelCase__										=    TYPE_CONVERSION.get(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    to_type.lower().rstrip("""s"""       )
 UpperCAmelCase__										=    TYPE_CONVERSION.get(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )
 if new_from not in METRIC_CONVERSION:
  UpperCAmelCase__										=    (
      F'''Invalid \'from_type\' value: {from_type!r}.\n'''
      F'''Conversion abbreviations are: {', '.join(SCREAMING_SNAKE_CASE__       )}'''
  )
  raise ValueError(SCREAMING_SNAKE_CASE__       )
 if new_to not in METRIC_CONVERSION:
  UpperCAmelCase__										=    (
      F'''Invalid \'to_type\' value: {to_type!r}.\n'''
      F'''Conversion abbreviations are: {', '.join(SCREAMING_SNAKE_CASE__       )}'''
  )
  raise ValueError(SCREAMING_SNAKE_CASE__       )
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
    import doctest

    doctest.testmod()
