

'''simple docstring'''





import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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      :    int						       =    32






def  UpperCamelCase_(							_snake_case     :						Accelerator ,  _snake_case     :						int = 16			):




			"""simple docstring"""






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

			def tokenize_function(_snake_case     :						str			):
						# max_length=None => use the model max length (it's actually the default)
						__a						   =tokenizer(examples['sentence1'] ,  examples['sentence2'] ,  truncation=_snake_case ,  max_length=_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(
						    _snake_case ,  batched=_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(_snake_case     :						Any			):
						# 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(
						    _snake_case ,  padding='longest' ,  pad_to_multiple_of=_snake_case ,  return_tensors='pt' ,  )

			# Instantiate dataloaders.
			__a						   =DataLoader(
			    tokenized_datasets['train'] ,  shuffle=_snake_case ,  collate_fn=_snake_case ,  batch_size=_snake_case			)
			__a						   =DataLoader(
			    tokenized_datasets['validation'] ,  shuffle=_snake_case ,  collate_fn=_snake_case ,  batch_size=_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      :    str						       =    mocked_dataloaders  # noqa: F811






def  UpperCamelCase_(							_snake_case     :						Optional[int] ,  _snake_case     :						Optional[Any]			):




			"""simple docstring"""






			if os.environ.get('TESTING_MOCKED_DATALOADERS' ,  _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']			)

			__a						   =evaluate.load('glue' ,  'mrpc'			)

			# New Code #
			# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
			# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
			# half each time. From this, we can calculate the number of gradient accumulation steps needed
			# and modify the Accelerator object as a result
			@find_executable_batch_size(starting_batch_size=int(_snake_case			)			)
			def inner_training_loop(_snake_case     :						Any			):
						# Since we need to modify the outside accelerator object, we need to bring it
						# to the local scope
						nonlocal accelerator

						# We can calculate the number of gradient accumulation steps based on the current
						# batch size vs the starting batch size
						__a						   =observed_batch_size // batch_size

						# And then set it in the Accelerator directly:
						__a						   =num_gradient_accumulation_steps

						# Next we need to free all of the stored model references in the Accelerator each time
						accelerator.free_memory()

						# And set the seed so our results are reproducable each reset
						set_seed(_snake_case			)

						# 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=_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=_snake_case			)
						__a   ,					__a						   =get_dataloaders(_snake_case ,  _snake_case			)

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

						# 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(
						    _snake_case ,  _snake_case ,  _snake_case ,  _snake_case ,  _snake_case			)

						# Now we train the model
						for epoch in range(_snake_case			):
									model.train()
									for step, batch in enumerate(_snake_case			):
												# And perform gradient accumulation
												with accelerator.accumulate(_snake_case			):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device			)
															__a						   =model(**_snake_case			)
															__a						   =outputs.loss
															accelerator.backward(_snake_case			)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
			inner_training_loop()






def  UpperCamelCase_(							):




			"""simple docstring"""






			__a						   =argparse.ArgumentParser(description='Simple example of training script.'			)
			parser.add_argument(
			    '--mixed_precision' ,  type=_snake_case ,  default=_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.'			)
			__a						   =parser.parse_args()
			# New Code #
			# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
			__a						   ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
			training_function(_snake_case ,  _snake_case			)


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


_lowerCAmelCase      :    List[Any]						       =    [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

_lowerCAmelCase      :    List[str]						       =    get_logger(__name__)


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






def  UpperCamelCase_(							_snake_case     :						dict			):




			"""simple docstring"""






			__a						   =task_template_dict.get('task'			)
			if task_name is None:
						logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_snake_case			)}'			)
						return None
			__a						   =NAMEaTEMPLATE.get(_snake_case			)
			return template.from_dict(_snake_case			)






