



"""simple docstring"""






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_lowercase   		=		{
    "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",
}

_lowercase   		=		AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")






def   snake_case__     (	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					Optional[Any]=False			):
			"""simple docstring"""
			lowerCamelCase__			,     lowerCamelCase__	:	Any				=create_model(
			    '''HTSAT-tiny'''							,	'''roberta'''							,	A__							,	precision='''fp32'''							,	device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''							,	enable_fusion=A__							,	fusion_type='''aff_2d''' if enable_fusion else None							,	)
			return model, model_cfg






def   snake_case__     (	__lowerCamelCase       :					List[Any]			):
			"""simple docstring"""
			lowerCamelCase__	:	Dict				={}

			lowerCamelCase__	:	str				=R'''.*sequential.(\d+).*'''
			lowerCamelCase__	:	Tuple				=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:
												lowerCamelCase__	:	List[Any]				=key.replace(A__							,	A__			)

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

									lowerCamelCase__	:	List[Any]				=key.replace(f'''sequential.{sequential_layer}.'''							,	f'''layers.{int(A__			)//3}.linear.'''			)
						elif re.match(A__							,	A__			):
									lowerCamelCase__	:	str				=int(re.match(A__							,	A__			).group(1			)			)

									# Because in CLAP they use `nn.Sequential`...
									lowerCamelCase__	:	Union[str, Any]				=1 if projecton_layer == 0 else 2

									lowerCamelCase__	:	Tuple				=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
									lowerCamelCase__	:	Optional[Any]				=value
									lowerCamelCase__	:	str				=mixed_qkv.size(0			) // 3

									lowerCamelCase__	:	int				=mixed_qkv[:qkv_dim]
									lowerCamelCase__	:	List[str]				=mixed_qkv[qkv_dim : qkv_dim * 2]
									lowerCamelCase__	:	List[Any]				=mixed_qkv[qkv_dim * 2 :]

									lowerCamelCase__	:	Tuple				=query_layer
									lowerCamelCase__	:	Optional[Any]				=key_layer
									lowerCamelCase__	:	Dict				=value_layer
						else:
									lowerCamelCase__	:	Any				=value

			return model_state_dict






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Union[str, Any]=False			):
			"""simple docstring"""
			lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=init_clap(A__							,	enable_fusion=A__			)

			clap_model.eval()
			lowerCamelCase__	:	Any				=clap_model.state_dict()
			lowerCamelCase__	:	Optional[Any]				=rename_state_dict(A__			)

			lowerCamelCase__	:	Optional[int]				=ClapConfig()
			lowerCamelCase__	:	int				=enable_fusion
			lowerCamelCase__	:	List[Any]				=ClapModel(A__			)

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

			model.save_pretrained(A__			)
			transformers_config.save_pretrained(A__			)


if __name__ == "__main__":
		_lowercase   		=		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")
		_lowercase   		=		parser.parse_args()

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









"""simple docstring"""






_lowercase				:				str   		=		0  # The first color of the flag.
_lowercase				:				Dict   		=		1  # The second color of the flag.
_lowercase				:				Tuple   		=		2  # The third color of the flag.
_lowercase				:				Optional[int]   		=		(red, white, blue)






def   snake_case__     (	__lowerCamelCase       :					list			):
			"""simple docstring"""
			if not sequence:
						return []
			if len(__lowerCamelCase			) == 1:
						return list(__lowerCamelCase			)
			lowerCamelCase__	:	List[Any]				=0
			lowerCamelCase__	:	Dict				=len(__lowerCamelCase			) - 1
			lowerCamelCase__	:	Tuple				=0
			while mid <= high:
						if sequence[mid] == colors[0]:
									lowerCamelCase__			,     lowerCamelCase__	:	Optional[Any]				=sequence[mid], sequence[low]
									low += 1
									mid += 1
						elif sequence[mid] == colors[1]:
									mid += 1
						elif sequence[mid] == colors[2]:
									lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=sequence[high], sequence[mid]
									high -= 1
						else:
									lowerCamelCase__	:	Dict				=f'''The elements inside the sequence must contains only {colors} values'''
									raise ValueError(__lowerCamelCase			)
			return sequence


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_lowercase				:				Optional[Any]   		=		input("Enter numbers separated by commas:\n").strip()
		_lowercase				:				int   		=		[int(item.strip()) for item in user_input.split(",")]
		print(f'{dutch_national_flag_sort(unsorted)}')




