



"""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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


lowercase__			   =  16
lowercase__			   =  32





def       __magic_name__    (   _lowerCamelCase				: Accelerator  ,       _lowerCamelCase				: int = 1_6					):
							__a		:		Union[str, Any]									=       AutoTokenizer.from_pretrained("""bert-base-cased"""					)
							__a		:		List[str]									=       load_dataset("""glue"""  ,       """mrpc"""					)

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

							def collate_fn(_lowerCamelCase				: List[str]					):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														__a		:		List[str]									=       1_2_8 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		:		List[Any]									=       1_6
														elif accelerator.mixed_precision != "no":
																					__a		:		int									=       8
														else:
																					__a		:		Dict									=       None

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

							# Instantiate dataloaders.
							__a		:		List[Any]									=       DataLoader(
							    tokenized_datasets["""train"""]  ,       shuffle=_lowerCamelCase  ,       collate_fn=_lowerCamelCase  ,       batch_size=_lowerCamelCase					)
							__a		:		int									=       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

	lowercase__			   =  mocked_dataloaders  # noqa: F811





def       __magic_name__    (   _lowerCamelCase				: Tuple  ,       _lowerCamelCase				: Dict					):
							# For testing only
							if os.environ.get("""TESTING_MOCKED_DATALOADERS"""  ,       _lowerCamelCase					) == "1":
														__a		:		List[Any]									=       2
							# Initialize accelerator
							__a		:		int									=       Accelerator(cpu=args.cpu  ,       mixed_precision=args.mixed_precision					)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__a		:		Dict									=       config["""lr"""]
							__a		:		Any									=       int(config["""num_epochs"""]					)
							__a		:		Optional[int]									=       int(config["""seed"""]					)
							__a		:		List[Any]									=       int(config["""batch_size"""]					)

							__a		:		Optional[Any]									=       evaluate.load("""glue"""  ,       """mrpc"""					)

							# New Code #
							# We now can define an inner training loop function. It should take a batch size as the only parameter,
							# and build the dataloaders in there.
							# It also gets our decorator
							@find_executable_batch_size(starting_batch_size=_lowerCamelCase					)
							def inner_training_loop(_lowerCamelCase				: Optional[Any]					):
														# And now just move everything below under this function
														# We need to bring in the Accelerator object from earlier
														nonlocal accelerator
														# And reset all of its attributes that could hold onto any memory:
														accelerator.free_memory()

														# Then we can declare the model, optimizer, and everything else:
														set_seed(_lowerCamelCase					)

														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														__a		:		str									=       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).
														__a		:		str									=       model.to(accelerator.device					)

														# Instantiate optimizer
														__a		:		Optional[Any]									=       AdamW(params=model.parameters()  ,       lr=_lowerCamelCase					)
														__a							,     __a		:		List[Any]									=       get_dataloaders(_lowerCamelCase  ,       _lowerCamelCase					)

														# Instantiate scheduler
														__a		:		Dict									=       get_linear_schedule_with_warmup(
														    optimizer=_lowerCamelCase  ,       num_warmup_steps=1_0_0  ,       num_training_steps=(len(_lowerCamelCase					) * 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		:		Optional[Any]									=       accelerator.prepare(
														    _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)

														# Now we train the model
														for epoch in range(_lowerCamelCase					):
																					model.train()
																					for step, batch in enumerate(_lowerCamelCase					):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device					)
																												__a		:		Any									=       model(**_lowerCamelCase					)
																												__a		:		Union[str, Any]									=       outputs.loss
																												accelerator.backward(_lowerCamelCase					)
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

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

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

    # 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       __magic_name__    (   ):
							__a		:		Optional[int]									=       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."""					)
							__a		:		Optional[Any]									=       parser.parse_args()
							__a		:		Any									=       {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
							training_function(_lowerCamelCase  ,       _lowerCamelCase					)


if __name__ == "__main__":
	main()






"""simple docstring"""

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Union[str, Any]=None  ,       _lowerCamelCase				: int=None					):
							if conf_path is None:
														__a		:		str									=       """./model_checkpoints/vqgan_only.yaml"""
							__a		:		List[Any]									=       load_config(_lowerCamelCase  ,       display=_lowerCamelCase					)
							__a		:		Dict									=       VQModel(**config.model.params					)
							if ckpt_path is None:
														__a		:		List[Any]									=       """./model_checkpoints/vqgan_only.pt"""
							__a		:		Tuple									=       torch.load(_lowerCamelCase  ,       map_location=_lowerCamelCase					)
							if ".ckpt" in ckpt_path:
														__a		:		List[str]									=       sd["""state_dict"""]
							model.load_state_dict(_lowerCamelCase  ,       strict=_lowerCamelCase					)
							model.to(_lowerCamelCase					)
							del sd
							return model





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





def       __magic_name__    (   _lowerCamelCase				: Any					):
							if "target" not in config:
														raise KeyError("""Expected key `target` to instantiate."""					)
							return get_obj_from_str(config["""target"""]					)(**config.get("""params"""  ,       {}					)					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       instantiate_from_config(_lowerCamelCase					)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase					)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}







def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

