"""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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 16  )->    List[str]:
							_SCREAMING_SNAKE_CASE		:						str			  =    AutoTokenizer.from_pretrained("""bert-base-cased"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    load_dataset("""glue"""     ,	"""mrpc"""  )

							def tokenize_function(__SCREAMING_SNAKE_CASE  ):
														# max_length=None => use the model max length (it's actually the default)
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    tokenizer(examples["""sentence1"""]     ,	examples["""sentence2"""]     ,	truncation=__SCREAMING_SNAKE_CASE     ,	max_length=__SCREAMING_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():
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    datasets.map(
														    __SCREAMING_SNAKE_CASE     ,	batched=__SCREAMING_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
							_SCREAMING_SNAKE_CASE		:						str			  =    tokenized_datasets.rename_column("""label"""     ,	"""labels"""  )

							def collate_fn(__SCREAMING_SNAKE_CASE  ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    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":
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    16
														elif accelerator.mixed_precision != "no":
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    8
														else:
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    None

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

							# Instantiate dataloaders.
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    DataLoader(
							    tokenized_datasets["""train"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE     ,	drop_last=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						int			  =    DataLoader(
							    tokenized_datasets["""validation"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE     ,	drop_last=(accelerator.mixed_precision == """fp8""")     ,	)

							return train_dataloader, eval_dataloader



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

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    evaluate.load("""glue"""     ,	"""mrpc"""  )

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

							set_seed(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    get_dataloaders(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""     ,	return_dict=__SCREAMING_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).
							_SCREAMING_SNAKE_CASE		:						Any			  =    model.to(accelerator.device  )
							# Instantiate optimizer
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    AdamW(params=model.parameters()     ,	lr=__SCREAMING_SNAKE_CASE  )

							# Instantiate scheduler
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    get_linear_schedule_with_warmup(
							    optimizer=__SCREAMING_SNAKE_CASE     ,	num_warmup_steps=100     ,	num_training_steps=(len(__SCREAMING_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.

							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    accelerator.prepare(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

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

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

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"""epoch {epoch}:"""     ,	__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_()->    Any:
							_SCREAMING_SNAKE_CASE		:						str			  =    argparse.ArgumentParser(description="""Simple example of training script."""  )
							parser.add_argument(
							    """--mixed_precision"""     ,	type=__SCREAMING_SNAKE_CASE     ,	default=__SCREAMING_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."""  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    parser.parse_args()
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
			main()



"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase_     =	{
    '''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''',
}

lowerCAmelCase_     =	AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    str:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    create_model(
							    """HTSAT-tiny"""     ,	"""roberta"""     ,	__SCREAMING_SNAKE_CASE     ,	precision="""fp32"""     ,	device="""cuda:0""" if torch.cuda.is_available() else """cpu"""     ,	enable_fusion=__SCREAMING_SNAKE_CASE     ,	fusion_type="""aff_2d""" if enable_fusion else None     ,	)
							return model, model_cfg



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {}

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    R""".*sequential.(\d+).*"""
							_SCREAMING_SNAKE_CASE		:						Any			  =    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:
																												_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    key.replace(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

														if re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					# replace sequential layers with list
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).group(1  )

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    key.replace(F"""sequential.{sequential_layer}."""     ,	F"""layers.{int(__SCREAMING_SNAKE_CASE  )//3}.linear."""  )
														elif re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    int(re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).group(1  )  )

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

																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    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
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    value
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mixed_qkv.size(0  ) // 3

																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    mixed_qkv[:qkv_dim]
																					_SCREAMING_SNAKE_CASE		:						str			  =    mixed_qkv[qkv_dim : qkv_dim * 2]
																					_SCREAMING_SNAKE_CASE		:						Any			  =    mixed_qkv[qkv_dim * 2 :]

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    query_layer
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    key_layer
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    value_layer
														else:
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    value

							return model_state_dict



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    List[Any]:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    init_clap(__SCREAMING_SNAKE_CASE     ,	enable_fusion=__SCREAMING_SNAKE_CASE  )

							clap_model.eval()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    clap_model.state_dict()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    rename_state_dict(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						int			  =    ClapConfig()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    enable_fusion
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ClapModel(__SCREAMING_SNAKE_CASE  )

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

							model.save_pretrained(__SCREAMING_SNAKE_CASE  )
							transformers_config.save_pretrained(__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
			lowerCAmelCase_     =	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''')
			lowerCAmelCase_     =	parser.parse_args()

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


