



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
#
########################################################################

lowerCamelCase__     =	16
lowerCamelCase__     =	32

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     str			,       _SCREAMING_SNAKE_CASE    :     Optional[Any] = 16      ):
				"""simple docstring"""







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

				def tokenize_function(_SCREAMING_SNAKE_CASE    :     str      ):
								# max_length=None => use the model max length (it's actually the default)
								__a					       =						tokenizer(examples["""sentence1"""]			,       examples["""sentence2"""]			,       truncation=_SCREAMING_SNAKE_CASE			,       max_length=_SCREAMING_SNAKE_CASE      )
								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(
								    _SCREAMING_SNAKE_CASE			,       batched=_SCREAMING_SNAKE_CASE			,       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"""      )

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

								return tokenizer.pad(
								    _SCREAMING_SNAKE_CASE			,       padding="""longest"""			,       max_length=_SCREAMING_SNAKE_CASE			,       pad_to_multiple_of=_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""			,       )

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

				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

						lowerCamelCase__     =	mocked_dataloaders  # noqa: F811

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,       _SCREAMING_SNAKE_CASE      ) == "1":
								__a					       =						2
				# Initialize accelerator
				__a					       =						Accelerator(cpu=args.cpu			,       mixed_precision=args.mixed_precision      )
				# 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"""]      )

				# 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":
												__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

				set_seed(_SCREAMING_SNAKE_CASE      )

				__a       ,   __a					       =						get_dataloaders(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )
				__a					       =						evaluate.load("""glue"""			,       """mrpc"""      )

				# 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

				# 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=_SCREAMING_SNAKE_CASE      )

				# 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=_SCREAMING_SNAKE_CASE      )

				# Instantiate scheduler
				__a					       =						get_linear_schedule_with_warmup(
				    optimizer=_SCREAMING_SNAKE_CASE			,       num_warmup_steps=100			,       num_training_steps=(len(_SCREAMING_SNAKE_CASE      ) * 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(
				    _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )

				# New Code #
				# 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

				# 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      )
												__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(_SCREAMING_SNAKE_CASE      )[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(_SCREAMING_SNAKE_CASE      )
												resume_step -= starting_epoch * len(_SCREAMING_SNAKE_CASE      )

    # Now we train the model
				for epoch in range(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      ):
								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
												__a					       =						accelerator.skip_first_batches(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )
												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(_SCREAMING_SNAKE_CASE      ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device      )
												__a					       =						model(**_SCREAMING_SNAKE_CASE      )
												__a					       =						outputs.loss
												__a					       =						loss / gradient_accumulation_steps
												accelerator.backward(_SCREAMING_SNAKE_CASE      )
												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(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      ):
																__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			,       _SCREAMING_SNAKE_CASE      )
																				accelerator.save_state(_SCREAMING_SNAKE_CASE      )

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

								__a					       =						metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"epoch {epoch}:"			,       _SCREAMING_SNAKE_CASE      )

								# 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":
												__a					       =						f"epoch_{epoch}"
												if args.output_dir is not None:
																__a					       =						os.path.join(args.output_dir			,       _SCREAMING_SNAKE_CASE      )
												accelerator.save_state(_SCREAMING_SNAKE_CASE      )

def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						argparse.ArgumentParser(description="""Simple example of training script."""      )
				parser.add_argument(
				    """--mixed_precision"""			,       type=_SCREAMING_SNAKE_CASE			,       default=_SCREAMING_SNAKE_CASE			,       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=_SCREAMING_SNAKE_CASE			,       default=_SCREAMING_SNAKE_CASE			,       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=_SCREAMING_SNAKE_CASE			,       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=_SCREAMING_SNAKE_CASE			,       default=_SCREAMING_SNAKE_CASE			,       help="""If the training should continue from a checkpoint folder."""			,       )
				__a					       =						parser.parse_args()
				__a					       =						{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
						main()





from typing import TYPE_CHECKING

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


lowerCamelCase__     =	{"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	[
						    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """HubertForCTC""",
						    """HubertForSequenceClassification""",
						    """HubertModel""",
						    """HubertPreTrainedModel""",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	[
						    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFHubertForCTC""",
						    """TFHubertModel""",
						    """TFHubertPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_hubert import (
												    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    HubertForCTC,
												    HubertForSequenceClassification,
												    HubertModel,
												    HubertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_hubert import (
												    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFHubertForCTC,
												    TFHubertModel,
												    TFHubertPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
