


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

__magic_name__       					=      1_6
__magic_name__       					=      3_2







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_ = 16):
	'''simple docstring'''


	lowerCamelCase_			:							Any      =       AutoTokenizer.from_pretrained("bert-base-cased")
	lowerCamelCase_			:							str      =       load_dataset("glue"      ,					"mrpc")

	def tokenize_function(lowerCAmelCase_):
		# max_length=None => use the model max length (it's actually the default)
		lowerCamelCase_			:							Optional[Any]      =       tokenizer(examples["sentence1"]      ,					examples["sentence2"]      ,					truncation=lowerCAmelCase_      ,					max_length=lowerCAmelCase_)
		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():
		lowerCamelCase_			:							List[str]      =       datasets.map(
		    lowerCAmelCase_      ,					batched=lowerCAmelCase_      ,					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
	lowerCamelCase_			:							Tuple      =       tokenized_datasets.rename_column("label"      ,					"labels")

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

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

	# Instantiate dataloaders.
	lowerCamelCase_			:							Dict      =       DataLoader(
	    tokenized_datasets["train"]      ,					shuffle=lowerCAmelCase_      ,					collate_fn=lowerCAmelCase_      ,					batch_size=lowerCAmelCase_)
	lowerCamelCase_			:							Tuple      =       DataLoader(
	    tokenized_datasets["validation"]      ,					shuffle=lowerCAmelCase_      ,					collate_fn=lowerCAmelCase_      ,					batch_size=lowerCAmelCase_)

	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

							__magic_name__       					=      mocked_dataloaders  # noqa: F811







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	if os.environ.get("TESTING_MOCKED_DATALOADERS"      ,					lowerCAmelCase_) == "1":
		lowerCamelCase_			:							List[str]      =       2
	# Initialize accelerator
	lowerCamelCase_			:							List[Any]      =       Accelerator(cpu=args.cpu      ,					mixed_precision=args.mixed_precision)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowerCamelCase_			:							Tuple      =       config["lr"]
	lowerCamelCase_			:							Optional[Any]      =       int(config["num_epochs"])
	lowerCamelCase_			:							Dict      =       int(config["seed"])
	lowerCamelCase_			:							List[Any]      =       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":
			lowerCamelCase_			:							Optional[int]      =       args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			lowerCamelCase_			:							List[str]      =       int(args.checkpointing_steps)
		else:
			raise ValueError(
			    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.""")
	else:
		lowerCamelCase_			:							Optional[int]      =       None

	set_seed(lowerCAmelCase_)

	lowerCamelCase_   ,lowerCamelCase_			:							int      =       get_dataloaders(lowerCAmelCase_      ,					lowerCAmelCase_)
	lowerCamelCase_			:							Any      =       evaluate.load("glue"      ,					"mrpc")

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

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCamelCase_			:							Dict      =       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"      ,					return_dict=lowerCAmelCase_)

	# 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).
	lowerCamelCase_			:							List[Any]      =       model.to(accelerator.device)

	# Instantiate optimizer
	lowerCamelCase_			:							Tuple      =       AdamW(params=model.parameters()      ,					lr=lowerCAmelCase_)

	# Instantiate scheduler
	lowerCamelCase_			:							Optional[Any]      =       get_linear_schedule_with_warmup(
	    optimizer=lowerCAmelCase_      ,					num_warmup_steps=100      ,					num_training_steps=(len(lowerCAmelCase_) * 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.
	lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_			:							int      =       accelerator.prepare(
	    lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_)

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

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

    # Now we train the model
	for epoch in range(lowerCAmelCase_      ,					lowerCAmelCase_):
		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
			lowerCamelCase_			:							Any      =       accelerator.skip_first_batches(lowerCAmelCase_      ,					lowerCAmelCase_)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			lowerCamelCase_			:							Dict      =       train_dataloader
		for step, batch in enumerate(lowerCAmelCase_):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device)
			lowerCamelCase_			:							Optional[int]      =       model(**lowerCAmelCase_)
			lowerCamelCase_			:							Optional[int]      =       outputs.loss
			lowerCamelCase_			:							int      =       loss / gradient_accumulation_steps
			accelerator.backward(lowerCAmelCase_)
			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(lowerCAmelCase_      ,					lowerCAmelCase_):
				lowerCamelCase_			:							str      =       F"""step_{overall_step}"""
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						lowerCamelCase_			:							Union[str, Any]      =       os.path.join(args.output_dir      ,					lowerCAmelCase_)
					accelerator.save_state(lowerCAmelCase_)

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

		lowerCamelCase_			:							List[Any]      =       metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"""epoch {epoch}:"""      ,					lowerCAmelCase_)

		# 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":
			lowerCamelCase_			:							Tuple      =       F"""epoch_{epoch}"""
			if args.output_dir is not None:
				lowerCamelCase_			:							Optional[int]      =       os.path.join(args.output_dir      ,					lowerCAmelCase_)
			accelerator.save_state(lowerCAmelCase_)







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							Union[str, Any]      =       argparse.ArgumentParser(description="Simple example of training script.")
	parser.add_argument(
	    "--mixed_precision"      ,					type=lowerCAmelCase_      ,					default=lowerCAmelCase_      ,					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=lowerCAmelCase_      ,					default=lowerCAmelCase_      ,					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=lowerCAmelCase_      ,					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=lowerCAmelCase_      ,					default=lowerCAmelCase_      ,					help="If the training should continue from a checkpoint folder."      ,					)
	lowerCamelCase_			:							Dict      =       parser.parse_args()
	lowerCamelCase_			:							Optional[Any]      =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(lowerCAmelCase_      ,					lowerCAmelCase_)


if __name__ == "__main__":
							main()





from typing import TYPE_CHECKING

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


__magic_name__       					=      {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''DetrFeatureExtractor''']
							__magic_name__       					=      ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

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

