


"""simple docstring"""


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCamelCase						:				Any             =      {
    "text_branch": "text_model",
    "audio_branch": "audio_model.audio_encoder",
    "attn": "attention.self",
    "self.proj": "output.dense",
    "attention.self_mask": "attn_mask",
    "mlp.fc1": "intermediate.dense",
    "mlp.fc2": "output.dense",
    "norm1": "layernorm_before",
    "norm2": "layernorm_after",
    "bn0": "batch_norm",
}

UpperCamelCase						:				List[str]             =      AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__=False	)      ->		Tuple:







		"""simple docstring"""





		A       , A					       = create_model(
		    'HTSAT-tiny' ,			'roberta' ,			UpperCamelCase__ ,			precision='fp32' ,			device='cuda:0' if torch.cuda.is_available() else 'cpu' ,			enable_fusion=UpperCamelCase__ ,			fusion_type='aff_2d' if enable_fusion else None ,			)
		return model, model_cfg







def  __snake_case       (   UpperCamelCase__	)      ->		str:







		"""simple docstring"""





		A					       = {}

		A					       = r'.*sequential.(\d+).*'
		A					       = r'.*_projection.(\d+).*'

		for key, value in state_dict.items():
				# check if any key needs to be modified
				for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
						if key_to_modify in key:
								A					       = key.replace(UpperCamelCase__ ,			UpperCamelCase__	)

				if re.match(UpperCamelCase__ ,			UpperCamelCase__	):
						# replace sequential layers with list
						A					       = re.match(UpperCamelCase__ ,			UpperCamelCase__	).group(1	)

						A					       = key.replace(f'sequential.{sequential_layer}.' ,			f'layers.{int(UpperCamelCase__	)//3}.linear.'	)
				elif re.match(UpperCamelCase__ ,			UpperCamelCase__	):
						A					       = int(re.match(UpperCamelCase__ ,			UpperCamelCase__	).group(1	)	)

						# Because in CLAP they use `nn.Sequential`...
						A					       = 1 if projecton_layer == 0 else 2

						A					       = key.replace(f'_projection.{projecton_layer}.' ,			f'_projection.linear{transformers_projection_layer}.'	)

				if "audio" and "qkv" in key:
						# split qkv into query key and value
						A					       = value
						A					       = mixed_qkv.size(0	) // 3

						A					       = mixed_qkv[:qkv_dim]
						A					       = mixed_qkv[qkv_dim : qkv_dim * 2]
						A					       = mixed_qkv[qkv_dim * 2 :]

						A					       = query_layer
						A					       = key_layer
						A					       = value_layer
				else:
						A					       = value

		return model_state_dict







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__=False	)      ->		List[str]:







		"""simple docstring"""





		A       , A					       = init_clap(UpperCamelCase__ ,			enable_fusion=UpperCamelCase__	)

		clap_model.eval()
		A					       = clap_model.state_dict()
		A					       = rename_state_dict(UpperCamelCase__	)

		A					       = ClapConfig()
		A					       = enable_fusion
		A					       = ClapModel(UpperCamelCase__	)

		# ignore the spectrogram embedding layer
		model.load_state_dict(UpperCamelCase__ ,			strict=UpperCamelCase__	)

		model.save_pretrained(UpperCamelCase__	)
		transformers_config.save_pretrained(UpperCamelCase__	)


if __name__ == "__main__":
	UpperCamelCase						:				Optional[Any]             =      argparse.ArgumentParser()
	parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
	parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
	parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
	parser.add_argument("--enable_fusion", action="store_true", help="Whether to enable fusion or not")
	UpperCamelCase						:				List[Any]             =      parser.parse_args()

	convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)









"""simple docstring"""


import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCamelCase						:				int             =      16
UpperCamelCase						:				Optional[int]             =      32







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = 16 ,			UpperCamelCase__ = "bert-base-cased"	)      ->		Optional[int]:







		"""simple docstring"""





		A					       = AutoTokenizer.from_pretrained(UpperCamelCase__	)
		A					       = load_dataset('glue' ,			'mrpc'	)

		def tokenize_function(UpperCamelCase__	):
				# max_length=None => use the model max length (it's actually the default)
				A					       = 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
		A					       = datasets.map(
		    UpperCamelCase__ ,			batched=UpperCamelCase__ ,			remove_columns=['idx', 'sentence1', 'sentence2'] ,			load_from_cache_file=UpperCamelCase__	)

		# 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(UpperCamelCase__	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(UpperCamelCase__ ,			padding='max_length' ,			max_length=128 ,			return_tensors='pt'	)
				return tokenizer.pad(UpperCamelCase__ ,			padding='longest' ,			return_tensors='pt'	)

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

		return train_dataloader, eval_dataloader







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Optional[Any]:







		"""simple docstring"""





		A					       = Accelerator()

		# 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					       = args.model_name_or_path

		set_seed(UpperCamelCase__	)
		A       , A					       = get_dataloaders(UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		A					       = AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__ ,			return_dict=UpperCamelCase__	)

		# Instantiate optimizer
		A					       = (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		A					       = optimizer_cls(params=model.parameters() ,			lr=UpperCamelCase__	)

		if accelerator.state.deepspeed_plugin is not None:
				A					       = accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				A					       = 1
		A					       = (len(UpperCamelCase__	) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				A					       = get_linear_schedule_with_warmup(
				    optimizer=UpperCamelCase__ ,			num_warmup_steps=0 ,			num_training_steps=UpperCamelCase__ ,			)
		else:
				A					       = DummyScheduler(UpperCamelCase__ ,			total_num_steps=UpperCamelCase__ ,			warmup_num_steps=0	)

		# 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(
		    UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)

		# 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

		# Now we train the model
		A					       = evaluate.load('glue' ,			'mrpc'	)
		A					       = 0
		A					       = {}
		for epoch in range(UpperCamelCase__ ,			UpperCamelCase__	):
				model.train()
				for step, batch in enumerate(UpperCamelCase__	):
						A					       = model(**UpperCamelCase__	)
						A					       = outputs.loss
						A					       = loss / gradient_accumulation_steps
						accelerator.backward(UpperCamelCase__	)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

				model.eval()
				A					       = 0
				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():
								A					       = model(**UpperCamelCase__	)
						A					       = outputs.logits.argmax(dim=-1	)
						# It is slightly faster to call this once, than multiple times
						A       , A					       = accelerator.gather(
						    (predictions, batch['labels'])	)  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
								if step == len(UpperCamelCase__	) - 1:
										A					       = predictions[: len(eval_dataloader.dataset	) - samples_seen]
										A					       = references[: len(eval_dataloader.dataset	) - samples_seen]
								else:
										samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=UpperCamelCase__ ,			references=UpperCamelCase__ ,			)

				A					       = metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'epoch {epoch}:' ,			UpperCamelCase__	)
				A					       = eval_metric['accuracy']

				if best_performance < eval_metric["accuracy"]:
						A					       = eval_metric['accuracy']

		if args.performance_lower_bound is not None:
				assert (
				    args.performance_lower_bound <= best_performance
				), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
				with open(os.path.join(args.output_dir ,			'all_results.json'	) ,			'w'	) as f:
						json.dump(UpperCamelCase__ ,			UpperCamelCase__	)







def  __snake_case       (   )      ->		Optional[int]:







		"""simple docstring"""





		A					       = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'	)
		parser.add_argument(
		    '--model_name_or_path' ,			type=UpperCamelCase__ ,			default='bert-base-cased' ,			help='Path to pretrained model or model identifier from huggingface.co/models.' ,			required=UpperCamelCase__ ,			)
		parser.add_argument(
		    '--output_dir' ,			type=UpperCamelCase__ ,			default='.' ,			help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,			)
		parser.add_argument(
		    '--performance_lower_bound' ,			type=UpperCamelCase__ ,			default=UpperCamelCase__ ,			help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' ,			)
		parser.add_argument(
		    '--num_epochs' ,			type=UpperCamelCase__ ,			default=3 ,			help='Number of train epochs.' ,			)
		A					       = parser.parse_args()
		A					       = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
		training_function(UpperCamelCase__ ,			UpperCamelCase__	)


if __name__ == "__main__":
	main()





