




import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize

def      A      (      _SCREAMING_SNAKE_CASE    )	-> float:
       return np.dot(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )




class 		UpperCamelCase__	:

    '''simple docstring'''



    def __init__(    self     ,  *,
        UpperCamelCase__ = np.inf     ,  UpperCamelCase__ = "linear"     ,  UpperCamelCase__ = 0.0     ,  )     ->      None:
           lowerCamelCase   :      Tuple			 =					regularization
           lowerCamelCase   :      List[str]			 =					gamma
           if kernel == "linear":
                  lowerCamelCase   :      Optional[Any]			 =					self.__linear
           elif kernel == "rbf":
                  if self.gamma == 0:
                         raise ValueError("rbf kernel requires gamma"     )
                  if not isinstance(self.gamma     ,  (float, int)     ):
                         raise ValueError("gamma must be float or int"     )
                  if not self.gamma > 0:
                         raise ValueError("gamma must be > 0"     )
                  lowerCamelCase   :      Any			 =					self.__rbf
                  # in the future, there could be a default value like in sklearn
                  # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
                  # previously it was 1/(n_features)
           else:
                  lowerCamelCase   :      Optional[int]			 =					F'''Unknown kernel: {kernel}'''
                  raise ValueError(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      float:
           return np.dot(UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      float:
           return np.exp(-(self.gamma * norm_squared(vectora - vectora     ))     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      None:
           lowerCamelCase   :      Dict			 =					observations
           lowerCamelCase   :      List[Any]			 =					classes

           # using Wolfe's Dual to calculate w.
           # Primal problem: minimize 1/2*norm_squared(w)
           #   constraint: yn(w . xn + b) >= 1
           #
           # With l a vector
           # Dual problem: maximize sum_n(ln) -
           #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
           #   constraint: self.C >= ln >= 0
           #           and sum_n(ln*yn) = 0
           # Then we get w using w = sum_n(ln*yn*xn)
           # At the end we can get b ~= mean(yn - w . xn)
           #
           # Since we use kernels, we only need l_star to calculate b
           # and to classify observations

           ((lowerCamelCase) ,      )   :      List[Any]			 =					np.shape(UpperCamelCase__     )

           def to_minimize(UpperCamelCase__     ) -> float:
                  lowerCamelCase   :      int			 =					0
                  ((lowerCamelCase) ,      )   :      Tuple			 =					np.shape(UpperCamelCase__     )
                  for i in range(UpperCamelCase__     ):
                         for j in range(UpperCamelCase__     ):
                                s += (
                                    candidate[i]
                                    * candidate[j]
                                    * classes[i]
                                    * classes[j]
                                    * self.kernel(observations[i]     ,  observations[j]     )
                                )
                  return 1 / 2 * s - sum(UpperCamelCase__     )

           lowerCamelCase   :      Dict			 =					LinearConstraint(UpperCamelCase__     ,  0     ,  0     )
           lowerCamelCase   :      Union[str, Any]			 =					Bounds(0     ,  self.regularization     )

           lowerCamelCase   :      Any			 =					minimize(
               UpperCamelCase__     ,  np.ones(UpperCamelCase__     )     ,  bounds=UpperCamelCase__     ,  constraints=[ly_contraint]     ).x
           lowerCamelCase   :      Any			 =					l_star

           # calculating mean offset of separation plane to points
           lowerCamelCase   :      Optional[int]			 =					0
           for i in range(UpperCamelCase__     ):
                  for j in range(UpperCamelCase__     ):
                         s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                             observations[i]     ,  observations[j]     )
           lowerCamelCase   :      Optional[int]			 =					s / n







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      List[str]			 =					sum(
               self.optimum[n]
               * self.classes[n]
               * self.kernel(self.observations[n]     ,  UpperCamelCase__     )
               for n in range(len(self.classes     )     )     )
           return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()









import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase							:  Union[str, Any]   	=						{
    """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							:  int   	=						AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       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 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {}

						SCREAMING_SNAKE_CASE_: Tuple  		=       R".*sequential.(\d+).*"
						SCREAMING_SNAKE_CASE_: Dict  		=       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_: Any  		=       key.replace(_UpperCAmelCase				,	_UpperCAmelCase    )

												if re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		# replace sequential layers with list
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )

																		SCREAMING_SNAKE_CASE_: Dict  		=       key.replace(f"sequential.{sequential_layer}."				,	f"layers.{int(_UpperCAmelCase    )//3}.linear."    )
												elif re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		SCREAMING_SNAKE_CASE_: Any  		=       int(re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )    )

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

																		SCREAMING_SNAKE_CASE_: Dict  		=       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_: Tuple  		=       value
																		SCREAMING_SNAKE_CASE_: List[str]  		=       mixed_qkv.size(0    ) // 3

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

																		SCREAMING_SNAKE_CASE_: str  		=       query_layer
																		SCREAMING_SNAKE_CASE_: int  		=       key_layer
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       value_layer
												else:
																		SCREAMING_SNAKE_CASE_: int  		=       value

						return model_state_dict







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       init_clap(_UpperCAmelCase				,	enable_fusion=_UpperCAmelCase    )

						clap_model.eval()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       clap_model.state_dict()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_state_dict(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       ClapConfig()
						SCREAMING_SNAKE_CASE_: Tuple  		=       enable_fusion
						SCREAMING_SNAKE_CASE_: Tuple  		=       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							:  Tuple   	=						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							:  int   	=						parser.parse_args()

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






