
"""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				(_UpperCamelCase       ,  _UpperCamelCase=False		):
	__lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      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     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Optional[int]									=      {}

	__lowerCAmelCase			:						str									=      r'.*sequential.(\d+).*'
	__lowerCAmelCase			:						int									=      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			:						Union[str, Any]									=      key.replace(_UpperCamelCase       ,  _UpperCamelCase		)

		if re.match(_UpperCamelCase       ,  _UpperCamelCase		):
			# replace sequential layers with list
			__lowerCAmelCase			:						List[str]									=      re.match(_UpperCamelCase       ,  _UpperCamelCase		).group(1		)

			__lowerCAmelCase			:						Tuple									=      key.replace(F"sequential.{sequential_layer}."       ,  F"layers.{int(_UpperCamelCase		)//3}.linear."		)
		elif re.match(_UpperCamelCase       ,  _UpperCamelCase		):
			__lowerCAmelCase			:						Dict									=      int(re.match(_UpperCamelCase       ,  _UpperCamelCase		).group(1		)		)

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

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

			__lowerCAmelCase			:						Optional[int]									=      mixed_qkv[:qkv_dim]
			__lowerCAmelCase			:						Optional[Any]									=      mixed_qkv[qkv_dim : qkv_dim * 2]
			__lowerCAmelCase			:						Optional[int]									=      mixed_qkv[qkv_dim * 2 :]

			__lowerCAmelCase			:						Optional[Any]									=      query_layer
			__lowerCAmelCase			:						Dict									=      key_layer
			__lowerCAmelCase			:						str									=      value_layer
		else:
			__lowerCAmelCase			:						Dict									=      value

	return model_state_dict
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=False		):
	__lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      init_clap(_UpperCamelCase       ,  enable_fusion=_UpperCamelCase		)

	clap_model.eval()
	__lowerCAmelCase			:						Optional[int]									=      clap_model.state_dict()
	__lowerCAmelCase			:						Dict									=      rename_state_dict(_UpperCamelCase		)

	__lowerCAmelCase			:						List[str]									=      ClapConfig()
	__lowerCAmelCase			:						List[str]									=      enable_fusion
	__lowerCAmelCase			:						Dict									=      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__":
							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)







import os

# Precomputes a list of the 100 first triangular numbers
__UpperCAmelCase								=						[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Any            =						os.path.dirname(os.path.realpath(__snake_case  )  )
					UpperCAmelCase_    :      Optional[Any]            =						os.path.join(__snake_case     ,       'words.txt'  )

					UpperCAmelCase_    :      Union[str, Any]            =						''
					with open(__snake_case  ) as f:
										UpperCAmelCase_    :      List[Any]            =						f.readline()

					UpperCAmelCase_    :      Optional[int]            =						[word.strip('"'  ) for word in words.strip('\r\n'  ).split(','  )]
					UpperCAmelCase_    :      Optional[int]            =						[
					    word
					    for word in [sum(ord(__snake_case  ) - 64 for x in word  ) for word in words]
					    if word in TRIANGULAR_NUMBERS
					]
					return len(__snake_case  )


if __name__ == "__main__":
		print(solution())






