






"""simple docstring"""





import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowerCAmelCase_     =	{
    '''<''': operator.lt,
    '''<=''': operator.le,
    '''==''': operator.eq,
    '''!=''': operator.ne,
    '''>=''': operator.ge,
    '''>''': operator.gt,
}



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							if got_ver is None or want_ver is None:
														raise ValueError(
														    F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
														    F""" reinstalling {pkg}."""  )
							if not ops[op](version.parse(_lowerCamelCase  )     ,	version.parse(_lowerCamelCase  )  ):
														raise ImportError(
														    F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    F"""\n{hint}""" if hint is not None else """"""

							# non-versioned check
							if re.match(R"""^[\w_\-\d]+$"""     ,	_lowerCamelCase  ):
														_SCREAMING_SNAKE_CASE		:						str			  =    requirement, None, None
							else:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    re.findall(R"""^([^!=<>\s]+)([\s!=<>]{1,2}.+)"""     ,	_lowerCamelCase  )
														if not match:
																					raise ValueError(
																					    """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"""
																					    F""" got {requirement}"""  )
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    match[0]
														_SCREAMING_SNAKE_CASE		:						Dict			  =    want_full.split(""","""  )  # there could be multiple requirements
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    {}
														for w in want_range:
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    re.findall(R"""^([\s!=<>]{1,2})(.+)"""     ,	_lowerCamelCase  )
																					if not match:
																												raise ValueError(
																												    """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"""
																												    F""" but got {requirement}"""  )
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    match[0]
																					_SCREAMING_SNAKE_CASE		:						Any			  =    want_ver
																					if op not in ops:
																												raise ValueError(F"""{requirement}: need one of {list(ops.keys()  )}, but got {op}"""  )

    # special case
							if pkg == "python":
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    """.""".join([str(_lowerCamelCase  ) for x in sys.version_info[:3]]  )
														for op, want_ver in wanted.items():
																					_compare_versions(_lowerCamelCase     ,	_lowerCamelCase     ,	_lowerCamelCase     ,	_lowerCamelCase     ,	_lowerCamelCase     ,	_lowerCamelCase  )
														return

							# check if any version is installed
							try:
														_SCREAMING_SNAKE_CASE		:						Any			  =    importlib.metadata.version(_lowerCamelCase  )
							except importlib.metadata.PackageNotFoundError:
														raise importlib.metadata.PackageNotFoundError(
														    F"""The \'{requirement}\' distribution was not found and is required by this application. {hint}"""  )

							# check that the right version is installed if version number or a range was provided
							if want_ver is not None:
														for op, want_ver in wanted.items():
																					_compare_versions(_lowerCamelCase     ,	_lowerCamelCase     ,	_lowerCamelCase     ,	_lowerCamelCase     ,	_lowerCamelCase     ,	_lowerCamelCase  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    """Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"""
							return require_version(_lowerCamelCase     ,	_lowerCamelCase  )



"""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_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    str:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    create_model(
							    """HTSAT-tiny"""     ,	"""roberta"""     ,	__SCREAMING_SNAKE_CASE     ,	precision="""fp32"""     ,	device="""cuda:0""" if torch.cuda.is_available() else """cpu"""     ,	enable_fusion=__SCREAMING_SNAKE_CASE     ,	fusion_type="""aff_2d""" if enable_fusion else None     ,	)
							return model, model_cfg



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {}

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    R""".*sequential.(\d+).*"""
							_SCREAMING_SNAKE_CASE		:						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:
																												_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    key.replace(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

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

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    key.replace(F"""sequential.{sequential_layer}."""     ,	F"""layers.{int(__SCREAMING_SNAKE_CASE  )//3}.linear."""  )
														elif re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    int(re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).group(1  )  )

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

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

																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    mixed_qkv[:qkv_dim]
																					_SCREAMING_SNAKE_CASE		:						str			  =    mixed_qkv[qkv_dim : qkv_dim * 2]
																					_SCREAMING_SNAKE_CASE		:						Any			  =    mixed_qkv[qkv_dim * 2 :]

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    query_layer
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    key_layer
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    value_layer
														else:
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    value

							return model_state_dict



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    List[Any]:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    init_clap(__SCREAMING_SNAKE_CASE     ,	enable_fusion=__SCREAMING_SNAKE_CASE  )

							clap_model.eval()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    clap_model.state_dict()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    rename_state_dict(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						int			  =    ClapConfig()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    enable_fusion
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ClapModel(__SCREAMING_SNAKE_CASE  )

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

							model.save_pretrained(__SCREAMING_SNAKE_CASE  )
							transformers_config.save_pretrained(__SCREAMING_SNAKE_CASE  )


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)


