

'''simple docstring'''


import argparse

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
#
# 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
#
########################################################################


lowerCAmelCase__										= 16
lowerCAmelCase__										= 32







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = 1_6  )    ->    Dict:
	UpperCAmelCase_		:      Optional[int]       	=					AutoTokenizer.from_pretrained('bert-base-cased'  )
	UpperCAmelCase_		:      Optional[int]       	=					load_dataset('glue'   ,'mrpc'  )

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

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

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

	# Instantiate dataloaders.
	UpperCAmelCase_		:      int       	=					DataLoader(
	    tokenized_datasets['train']   ,shuffle=UpperCamelCase   ,collate_fn=UpperCamelCase   ,batch_size=UpperCamelCase   ,drop_last=UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					DataLoader(
	    tokenized_datasets['validation']   ,shuffle=UpperCamelCase   ,collate_fn=UpperCamelCase   ,batch_size=UpperCamelCase   ,drop_last=(accelerator.mixed_precision == 'fp8')   ,)

	return train_dataloader, eval_dataloader







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    int:
	# Initialize accelerator
	UpperCAmelCase_		:      Union[str, Any]       	=					Accelerator(cpu=args.cpu   ,mixed_precision=args.mixed_precision  )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCAmelCase_		:      Any       	=					config['lr']
	UpperCAmelCase_		:      Optional[Any]       	=					int(config['num_epochs']  )
	UpperCAmelCase_		:      Dict       	=					int(config['seed']  )
	UpperCAmelCase_		:      Dict       	=					int(config['batch_size']  )

	UpperCAmelCase_		:      Dict       	=					evaluate.load('glue'   ,'mrpc'  )

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

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

	# 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_		:      int       	=					model.to(accelerator.device  )
	# Instantiate optimizer
	UpperCAmelCase_		:      Any       	=					AdamW(params=model.parameters()   ,lr=UpperCamelCase  )

	# Instantiate scheduler
	UpperCAmelCase_		:      Optional[int]       	=					get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase   ,num_warmup_steps=1_0_0   ,num_training_steps=(len(UpperCamelCase  ) * 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.

	UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      str       	=					accelerator.prepare(
	    UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )

	# Now we train the model
	for epoch in range(UpperCamelCase  ):
		model.train()
		for step, batch in enumerate(UpperCamelCase  ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device  )
			UpperCAmelCase_		:      List[str]       	=					model(**UpperCamelCase  )
			UpperCAmelCase_		:      Optional[int]       	=					outputs.loss
			UpperCAmelCase_		:      Optional[Any]       	=					loss / gradient_accumulation_steps
			accelerator.backward(UpperCamelCase  )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		UpperCAmelCase_		:      List[Any]       	=					metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""   ,UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  )    ->    int:
	UpperCAmelCase_		:      Tuple       	=					argparse.ArgumentParser(description='Simple example of training script.'  )
	parser.add_argument(
	    '--mixed_precision'   ,type=UpperCamelCase   ,default=UpperCamelCase   ,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.'  )
	UpperCAmelCase_		:      str       	=					parser.parse_args()
	UpperCAmelCase_		:      List[Any]       	=					{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	training_function(UpperCamelCase   ,UpperCamelCase  )


if __name__ == "__main__":
						main()









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__										= {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__										= [
						    "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ClapModel",
						    "ClapPreTrainedModel",
						    "ClapTextModel",
						    "ClapTextModelWithProjection",
						    "ClapAudioModel",
						    "ClapAudioModelWithProjection",
						]
						lowerCAmelCase__										= ["ClapFeatureExtractor"]

if TYPE_CHECKING:
						from .configuration_clap import (
						    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ClapAudioConfig,
						    ClapConfig,
						    ClapTextConfig,
						)
						from .processing_clap import ClapProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_clap import ClapFeatureExtractor
												from .modeling_clap import (
												    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ClapAudioModel,
												    ClapAudioModelWithProjection,
												    ClapModel,
												    ClapPreTrainedModel,
												    ClapTextModel,
												    ClapTextModelWithProjection,
												)


else:
						import sys

						lowerCAmelCase__										= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






