



import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case	=						{
    "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",
}

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase  :							Optional[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        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					{}

							_lowerCAmelCase  :							List[Any]				     =					r".*sequential.(\d+).*"
							_lowerCAmelCase  :							Optional[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(_lowerCamelCase       ,				_lowerCamelCase						)

														if re.match(_lowerCamelCase       ,				_lowerCamelCase						):
																					# replace sequential layers with list
																					_lowerCAmelCase  :							Optional[Any]				     =					re.match(_lowerCamelCase       ,				_lowerCamelCase						).group(1						)

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

																					# Because in CLAP they use `nn.Sequential`...
																					_lowerCAmelCase  :							Optional[Any]				     =					1 if projecton_layer == 0 else 2

																					_lowerCAmelCase  :							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
																					_lowerCAmelCase  :							int				     =					value
																					_lowerCAmelCase  :							str				     =					mixed_qkv.size(0						) // 3

																					_lowerCAmelCase  :							Optional[int]				     =					mixed_qkv[:qkv_dim]
																					_lowerCAmelCase  :							Tuple				     =					mixed_qkv[qkv_dim : qkv_dim * 2]
																					_lowerCAmelCase  :							Dict				     =					mixed_qkv[qkv_dim * 2 :]

																					_lowerCAmelCase  :							Tuple				     =					query_layer
																					_lowerCAmelCase  :							Optional[int]				     =					key_layer
																					_lowerCAmelCase  :							Optional[Any]				     =					value_layer
														else:
																					_lowerCAmelCase  :							str				     =					value

							return model_state_dict






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase  :							Any				     =					init_clap(_lowerCamelCase       ,				enable_fusion=_lowerCamelCase						)

							clap_model.eval()
							_lowerCAmelCase  :							Tuple				     =					clap_model.state_dict()
							_lowerCAmelCase  :							List[Any]				     =					rename_state_dict(_lowerCamelCase						)

							_lowerCAmelCase  :							List[str]				     =					ClapConfig()
							_lowerCAmelCase  :							Tuple				     =					enable_fusion
							_lowerCAmelCase  :							str				     =					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__":
						_snake_case	=						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")
						_snake_case	=						parser.parse_args()

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





from __future__ import annotations






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							if not nums:
														raise ValueError("List is empty"						)
							return sum(_lowerCamelCase						) / len(_lowerCamelCase						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
