




from __future__ import annotations

from cmath import sqrt




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):

				if a == 0:
								raise ValueError("""Coefficient 'a' must not be zero."""   )
				__magic_name__	:	Optional[int]								=b * b - 4 * a * c

				__magic_name__	:	str								=(-b + sqrt(lowerCamelCase   )) / (2 * a)
				__magic_name__	:	List[str]								=(-b - sqrt(lowerCamelCase   )) / (2 * a)

				return (
				    root_a.real if not root_a.imag else root_a,
				    root_a.real if not root_a.imag else root_a,
				)




def 		lowerCAmelCase_   (	):
				__magic_name__				, __magic_name__	:	Any								=quadratic_roots(a=5	,     b=6	,     c=1   )
				print(F"The solutions are: {solutiona} and {solutiona}"   )


if __name__ == "__main__":
							main()










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase_		:				Optional[int]									=     {
    "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_		:				Optional[int]									=     AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	Any								=create_model(
				    """HTSAT-tiny"""	,     """roberta"""	,     lowerCamelCase	,     precision="""fp32"""	,     device="""cuda:0""" if torch.cuda.is_available() else """cpu"""	,     enable_fusion=lowerCamelCase	,     fusion_type="""aff_2d""" if enable_fusion else None	,     )
				return model, model_cfg




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Optional[int]								={}

				__magic_name__	:	Union[str, Any]								=R""".*sequential.(\d+).*"""
				__magic_name__	:	Union[str, 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:
																__magic_name__	:	Union[str, Any]								=key.replace(lowerCamelCase	,     lowerCamelCase   )

								if re.match(lowerCamelCase	,     lowerCamelCase   ):
												# replace sequential layers with list
												__magic_name__	:	str								=re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )

												__magic_name__	:	Optional[Any]								=key.replace(F"sequential.{sequential_layer}."	,     F"layers.{int(lowerCamelCase   )//3}.linear."   )
								elif re.match(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Any								=int(re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )   )

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

												__magic_name__	:	List[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
												__magic_name__	:	Dict								=value
												__magic_name__	:	List[str]								=mixed_qkv.size(0   ) // 3

												__magic_name__	:	Optional[Any]								=mixed_qkv[:qkv_dim]
												__magic_name__	:	Any								=mixed_qkv[qkv_dim : qkv_dim * 2]
												__magic_name__	:	Optional[Any]								=mixed_qkv[qkv_dim * 2 :]

												__magic_name__	:	str								=query_layer
												__magic_name__	:	int								=key_layer
												__magic_name__	:	str								=value_layer
								else:
												__magic_name__	:	int								=value

				return model_state_dict




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	Tuple								=init_clap(lowerCamelCase	,     enable_fusion=lowerCamelCase   )

				clap_model.eval()
				__magic_name__	:	int								=clap_model.state_dict()
				__magic_name__	:	Optional[Any]								=rename_state_dict(lowerCamelCase   )

				__magic_name__	:	Any								=ClapConfig()
				__magic_name__	:	Optional[Any]								=enable_fusion
				__magic_name__	:	Tuple								=ClapModel(lowerCamelCase   )

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

				model.save_pretrained(lowerCamelCase   )
				transformers_config.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
							UpperCAmelCase_		:				Optional[int]									=     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_		:				Dict									=     parser.parse_args()

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




