




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 					snake_case					(							A						,   A = 16			)							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							AutoTokenizer.from_pretrained('bert-base-cased'			)
	lowerCamelCase_							:		List[str]							   =							load_dataset('glue'						,   'mrpc'			)

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

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

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

	# Instantiate dataloaders.
	lowerCamelCase_							:		Dict							   =							DataLoader(
	    tokenized_datasets['train']						,   shuffle=A						,   collate_fn=A						,   batch_size=A			)
	lowerCamelCase_							:		List[str]							   =							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

							UpperCamelCase__              =						mocked_dataloaders  # noqa: F811






def 					snake_case					(							A						,   A			)							->							Union[str, Any]:




	'''simple docstring'''


	if os.environ.get('TESTING_MOCKED_DATALOADERS'						,   A			) == "1":
		lowerCamelCase_							:		Optional[Any]							   =							2
	# Initialize accelerator
	lowerCamelCase_							:		int							   =							Accelerator(cpu=args.cpu						,   mixed_precision=args.mixed_precision			)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowerCamelCase_							:		str							   =							config['lr']
	lowerCamelCase_							:		Any							   =							int(config['num_epochs']			)
	lowerCamelCase_							:		str							   =							int(config['seed']			)
	lowerCamelCase_							:		str							   =							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_							:		Dict							   =							args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			lowerCamelCase_							:		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_							:		List[str]							   =							None

	set_seed(A			)

	lowerCamelCase_   ,			lowerCamelCase_							:		Optional[int]							   =							get_dataloaders(A						,   A			)
	lowerCamelCase_							:		List[str]							   =							evaluate.load('glue'						,   'mrpc'			)

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

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCamelCase_							:		Optional[int]							   =							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).
	lowerCamelCase_							:		Optional[Any]							   =							model.to(accelerator.device			)

	# Instantiate optimizer
	lowerCamelCase_							:		Union[str, Any]							   =							AdamW(params=model.parameters()						,   lr=A			)

	# Instantiate scheduler
	lowerCamelCase_							:		Tuple							   =							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.
	lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		str							   =							accelerator.prepare(
	    A						,   A						,   A						,   A						,   A			)

	# New Code #
	# We need to keep track of how many total steps we have iterated over
	lowerCamelCase_							:		List[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_							:		Optional[int]							   =							os.path.basename(args.resume_from_checkpoint			)
		else:
			# Get the most recent checkpoint
			lowerCamelCase_							:		Any							   =							[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
			dirs.sort(key=os.path.getctime			)
			lowerCamelCase_							:		Optional[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(A			)[0]

		if "epoch" in training_difference:
			lowerCamelCase_							:		List[str]							   =							int(training_difference.replace('epoch_'						,   ''			)			) + 1
			lowerCamelCase_							:		Dict							   =							None
		else:
			lowerCamelCase_							:		Optional[Any]							   =							int(training_difference.replace('step_'						,   ''			)			)
			lowerCamelCase_							:		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
			lowerCamelCase_							:		str							   =							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
			lowerCamelCase_							:		Optional[int]							   =							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			)
			lowerCamelCase_							:		Tuple							   =							model(**A			)
			lowerCamelCase_							:		List[str]							   =							outputs.loss
			lowerCamelCase_							:		Optional[int]							   =							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			):
				lowerCamelCase_							:		Optional[Any]							   =							f"step_{overall_step}"
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						lowerCamelCase_							:		List[str]							   =							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():
				lowerCamelCase_							:		List[Any]							   =							model(**A			)
			lowerCamelCase_							:		Optional[Any]							   =							outputs.logits.argmax(dim=-1			)
			lowerCamelCase_   ,			lowerCamelCase_							:		str							   =							accelerator.gather_for_metrics((predictions, batch['labels'])			)
			metric.add_batch(
			    predictions=A						,   references=A						,   )

		lowerCamelCase_							:		Optional[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":
			lowerCamelCase_							:		List[str]							   =							f"epoch_{epoch}"
			if args.output_dir is not None:
				lowerCamelCase_							:		List[Any]							   =							os.path.join(args.output_dir						,   A			)
			accelerator.save_state(A			)






def 					snake_case					(							)							->							List[str]:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							   =							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.'						,   )
	lowerCamelCase_							:		Optional[int]							   =							parser.parse_args()
	lowerCamelCase_							:		List[Any]							   =							{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(A						,   A			)


if __name__ == "__main__":
							main()






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase__              =						[
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCamelCase__              =						get_logger(__name__)


UpperCamelCase__              =						{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def 					snake_case					(							A			)							->							Optional[TaskTemplate]:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							task_template_dict.get('task'			)
	if task_name is None:
		logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(A			)}"			)
		return None
	lowerCamelCase_							:		Any							   =							NAMEaTEMPLATE.get(A			)
	return template.from_dict(A			)
