
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
# and perform 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)
#
# 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					: List[str]      =       16
_UpperCAmelCase					: Any      =       32


def       A       (					lowercase    ,       lowercase = 16 )   ->  Optional[int]:
		'''simple docstring'''

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

		def tokenize_function(lowercase ):
				# max_length=None => use the model max length (it's actually the default)
				UpperCamelCase		=     tokenizer(examples['sentence1']    ,       examples['sentence2']    ,       truncation=lowercase    ,       max_length=lowercase )
				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():
				UpperCamelCase		=     datasets.map(
				    lowercase    ,       batched=lowercase    ,       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
		UpperCamelCase		=     tokenized_datasets.rename_column('label'    ,       'labels' )

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

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

		# Instantiate dataloaders.
		UpperCamelCase		=     DataLoader(
		    tokenized_datasets['train']    ,       shuffle=lowercase    ,       collate_fn=lowercase    ,       batch_size=lowercase )
		UpperCamelCase		=     DataLoader(
		    tokenized_datasets['validation']    ,       shuffle=lowercase    ,       collate_fn=lowercase    ,       batch_size=lowercase )

		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					: Optional[int]      =       mocked_dataloaders  # noqa: F811


def       A       (					lowercase    ,       lowercase )   ->  Optional[int]:
		'''simple docstring'''

		if os.environ.get('TESTING_MOCKED_DATALOADERS'    ,       lowercase ) == "1":
				UpperCamelCase		=     2
		# New Code #
		UpperCamelCase		=     int(args.gradient_accumulation_steps )
		# Initialize accelerator
		UpperCamelCase		=     Accelerator(
		    cpu=args.cpu    ,       mixed_precision=args.mixed_precision    ,       gradient_accumulation_steps=lowercase )
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`' )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		UpperCamelCase		=     config['lr']
		UpperCamelCase		=     int(config['num_epochs'] )
		UpperCamelCase		=     int(config['seed'] )
		UpperCamelCase		=     int(config['batch_size'] )

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

		set_seed(lowercase )
		UpperCamelCase	,  UpperCamelCase		=     get_dataloaders(lowercase    ,       lowercase )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		UpperCamelCase		=     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'    ,       return_dict=lowercase )

		# 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).
		UpperCamelCase		=     model.to(accelerator.device )

		# Instantiate optimizer
		UpperCamelCase		=     AdamW(params=model.parameters()    ,       lr=lowercase )

		# Instantiate scheduler
		UpperCamelCase		=     get_linear_schedule_with_warmup(
		    optimizer=lowercase    ,       num_warmup_steps=100    ,       num_training_steps=(len(lowercase ) * 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.
		UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     accelerator.prepare(
		    lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase )

		# Now we train the model
		for epoch in range(lowercase ):
				model.train()
				for step, batch in enumerate(lowercase ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device )
						# New code #
						# We use the new `accumulate` context manager to perform gradient accumulation
						# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
						with accelerator.accumulate(lowercase ):
								UpperCamelCase		=     model(**lowercase )
								UpperCamelCase		=     output.loss
								accelerator.backward(lowercase )
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

				UpperCamelCase		=     metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''    ,       lowercase )


def       A       (					)   ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase		=     argparse.ArgumentParser(description='Simple example of training script.' )
		parser.add_argument(
		    '--mixed_precision'    ,       type=lowercase    ,       default=lowercase    ,       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.'    ,       )
		# New Code #
		parser.add_argument(
		    '--gradient_accumulation_steps'    ,       type=lowercase    ,       default=1    ,       help='The number of minibatches to be ran before gradients are accumulated.'    ,       )
		parser.add_argument('--cpu'    ,       action='store_true'    ,       help='If passed, will train on the CPU.' )
		UpperCamelCase		=     parser.parse_args()
		UpperCamelCase		=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(lowercase    ,       lowercase )


if __name__ == "__main__":
					main()


import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


_UpperCAmelCase					: Optional[int]      =       HUGGINGFACE_HUB_CACHE


_UpperCAmelCase					: List[str]      =       "config.json"
_UpperCAmelCase					: Union[str, Any]      =       "diffusion_pytorch_model.bin"
_UpperCAmelCase					: List[Any]      =       "diffusion_flax_model.msgpack"
_UpperCAmelCase					: Optional[Any]      =       "model.onnx"
_UpperCAmelCase					: int      =       "diffusion_pytorch_model.safetensors"
_UpperCAmelCase					: Optional[Any]      =       "weights.pb"
_UpperCAmelCase					: Tuple      =       "https://huggingface.co"
_UpperCAmelCase					: Union[str, Any]      =       default_cache_path
_UpperCAmelCase					: Optional[Any]      =       "diffusers_modules"
_UpperCAmelCase					: List[Any]      =       os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
_UpperCAmelCase					: Tuple      =       ["fp16", "non-ema"]
_UpperCAmelCase					: Any      =       ".self_attn"
