

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      :    List[Any]						       =    16
_lowerCAmelCase      :    List[str]						       =    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     :						List[Any]			):
						# 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     :						List[str]			):
						# 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(
						    _snake_case ,  padding='longest' ,  max_length=_snake_case ,  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      :    int						       =    mocked_dataloaders  # noqa: F811






def  UpperCamelCase_(							_snake_case     :						List[str] ,  _snake_case     :						int			):




			"""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']			)

			# 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(_snake_case			)

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

			# Instantiate scheduler
			__a						   =get_linear_schedule_with_warmup(
			    optimizer=_snake_case ,  num_warmup_steps=100 ,  num_training_steps=(len(_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(
			    _snake_case ,  _snake_case ,  _snake_case ,  _snake_case ,  _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(_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(_snake_case			)
									resume_step -= starting_epoch * len(_snake_case			)

    # Now we train the model
			for epoch in range(_snake_case ,  _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(_snake_case ,  _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(_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
									__a						   =loss / gradient_accumulation_steps
									accelerator.backward(_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(_snake_case ,  _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 ,  _snake_case			)
															accelerator.save_state(_snake_case			)

						model.eval()
						for step, batch in enumerate(_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(**_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 #
						# 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 ,  _snake_case			)
									accelerator.save_state(_snake_case			)






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.'			)
			parser.add_argument(
			    '--checkpointing_steps' ,  type=_snake_case ,  default=_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=_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=_snake_case ,  default=_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(_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			)






