





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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





def  __magic_name__  (				lowercase				,       lowercase=False				)				-> List[Any]:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   str  				=     create_model(
							    """HTSAT-tiny"""				,       """roberta"""				,       lowercase				,       precision="""fp32"""				,       device="""cuda:0""" if torch.cuda.is_available() else """cpu"""				,       enable_fusion=lowercase				,       fusion_type="""aff_2d""" if enable_fusion else None				,       )
							return model, model_cfg





def  __magic_name__  (				lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   int  				=     {}

							lowercase_	:   List[Any]  				=     r""".*sequential.(\d+).*"""
							lowercase_	:   List[str]  				=     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:
																												lowercase_	:   Optional[int]  				=     key.replace(lowercase				,       lowercase				)

														if re.match(lowercase				,       lowercase				):
																					# replace sequential layers with list
																					lowercase_	:   Dict  				=     re.match(lowercase				,       lowercase				).group(1				)

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

																					# Because in CLAP they use `nn.Sequential`...
																					lowercase_	:   List[str]  				=     1 if projecton_layer == 0 else 2

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

																					lowercase_	:   Dict  				=     mixed_qkv[:qkv_dim]
																					lowercase_	:   Dict  				=     mixed_qkv[qkv_dim : qkv_dim * 2]
																					lowercase_	:   int  				=     mixed_qkv[qkv_dim * 2 :]

																					lowercase_	:   Dict  				=     query_layer
																					lowercase_	:   int  				=     key_layer
																					lowercase_	:   Tuple  				=     value_layer
														else:
																					lowercase_	:   str  				=     value

							return model_state_dict





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase=False				)				-> Tuple:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   Tuple  				=     init_clap(lowercase				,       enable_fusion=lowercase				)

							clap_model.eval()
							lowercase_	:   str  				=     clap_model.state_dict()
							lowercase_	:   Optional[Any]  				=     rename_state_dict(lowercase				)

							lowercase_	:   str  				=     ClapConfig()
							lowercase_	:   str  				=     enable_fusion
							lowercase_	:   List[Any]  				=     ClapModel(lowercase				)

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

							model.save_pretrained(lowercase				)
							transformers_config.save_pretrained(lowercase				)


if __name__ == "__main__":
							UpperCAmelCase_							=				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_							=				parser.parse_args()

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






UpperCAmelCase_							=				{"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
UpperCAmelCase_							=				["""a""", """b""", """c""", """d""", """e"""]





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Union[str, Any]:

							"""simple docstring"""



							lowercase_	:   Tuple  				=     start
							# add current to visited
							visited.append(lowercase				)
							lowercase_	:   Any  				=     edges[current]
							for neighbor in neighbors:
														# if neighbor not in visited, visit
														if neighbor not in visited:
																					lowercase_	:   List[str]  				=     topological_sort(lowercase				,       lowercase				,       lowercase				)
    # if all neighbors visited add current to sort
							sort.append(lowercase				)
							# if all vertices haven't been visited select a new one to visit
							if len(lowercase				) != len(lowercase				):
														for vertice in vertices:
																					if vertice not in visited:
																												lowercase_	:   Optional[Any]  				=     topological_sort(lowercase				,       lowercase				,       lowercase				)
    # return sort
							return sort


if __name__ == "__main__":
							UpperCAmelCase_							=				topological_sort("""a""", [], [])
							print(sort)