





'''simple docstring'''
from math import factorial

class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							int							,	__A       :							List[str]							,	__A       :							int	):
        __UpperCamelCase       =  real
        if isinstance(__A							,	__A	):
               __UpperCamelCase       =  [1] * rank
        else:
               __UpperCamelCase       =  rank






 def __repr__(       self       :							Optional[Any]	):
        return (
            f'''{self.real}+'''
            f'''{'+'.join(str(__A	)+'E'+str(n+1	)for n,dual in enumerate(self.duals	)	)}'''
        )






 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  self.duals.copy()
        while cur[-1] == 0:
               cur.pop(-1	)
        return Dual(self.real							,	__A	)






 def __add__(       self       :							str							,	__A       :							Any	):
        if not isinstance(__A							,	__A	):
               return Dual(self.real + other							,	self.duals	)
        __UpperCamelCase       =  self.duals.copy()
        __UpperCamelCase       =  other.duals.copy()
        if len(__A	) > len(__A	):
               o_dual.extend([1] * (len(__A	) - len(__A	))	)
        elif len(__A	) < len(__A	):
               s_dual.extend([1] * (len(__A	) - len(__A	))	)
        __UpperCamelCase       =  []
        for i in range(len(__A	)	):
               new_duals.append(s_dual[i] + o_dual[i]	)
        return Dual(self.real + other.real							,	__A	)

 SCREAMING_SNAKE_CASE_	:					List[str]					=__add__






 def __sub__(       self       :							int							,	__A       :							List[Any]	):
        return self + other * -1






 def __mul__(       self       :							List[str]							,	__A       :							Optional[int]	):
        if not isinstance(__A							,	__A	):
               __UpperCamelCase       =  []
               for i in self.duals:
                      new_duals.append(i * other	)
               return Dual(self.real * other							,	__A	)
        __UpperCamelCase       =  [0] * (len(self.duals	) + len(other.duals	) + 1)
        for i, item in enumerate(self.duals	):
               for j, jtem in enumerate(other.duals	):
                      new_duals[i + j + 1] += item * jtem
        for k in range(len(self.duals	)	):
               new_duals[k] += self.duals[k] * other.real
        for index in range(len(other.duals	)	):
               new_duals[index] += other.duals[index] * self.real
        return Dual(self.real * other.real							,	__A	)

 SCREAMING_SNAKE_CASE_	:					Any					=__mul__






 def __truediv__(       self       :							Optional[Any]							,	__A       :							Optional[int]	):
        if not isinstance(__A							,	__A	):
               __UpperCamelCase       =  []
               for i in self.duals:
                      new_duals.append(i / other	)
               return Dual(self.real / other							,	__A	)
        raise ValueError






 def __floordiv__(       self       :							str							,	__A       :							List[str]	):
        if not isinstance(__A							,	__A	):
               __UpperCamelCase       =  []
               for i in self.duals:
                      new_duals.append(i // other	)
               return Dual(self.real // other							,	__A	)
        raise ValueError






 def __pow__(       self       :							Optional[int]							,	__A       :							Optional[Any]	):
        if n < 0 or isinstance(__A							,	__A	):
               raise ValueError('power must be a positive integer'	)
        if n == 0:
               return 1
        if n == 1:
               return self
        __UpperCamelCase       =  self
        for _ in range(n - 1	):
               x *= self
        return x






def 				lowercase__   (   __lowercase						:  List[Any]				,  __lowercase						:  Tuple				,  __lowercase						:  int     ) ->      Optional[int]:



       """simple docstring"""






       if not callable(SCREAMING_SNAKE_CASE__     ):
              raise ValueError('differentiate() requires a function as input for func'     )
       if not isinstance(SCREAMING_SNAKE_CASE__				,  (float, int)     ):
              raise ValueError('differentiate() requires a float as input for position'     )
       if not isinstance(SCREAMING_SNAKE_CASE__				,  SCREAMING_SNAKE_CASE__     ):
              raise ValueError('differentiate() requires an int as input for order'     )
       __UpperCamelCase       =  Dual(SCREAMING_SNAKE_CASE__				,  1     )
       __UpperCamelCase       =  func(SCREAMING_SNAKE_CASE__     )
       if order == 0:
              return result.real
       return result.duals[order - 1] * factorial(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    def 				lowercase__   (   __lowercase						:  Optional[int]     ) ->      Tuple:



           """simple docstring"""






           return y**2 * y**4


    print(differentiate(f, 9, 2))







import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__UpperCAmelCase       :   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",
}

__UpperCAmelCase       :   List[str]				       =							AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False)   ->    Union[str, Any]:
  __snake_case  ,   __snake_case:						int   =						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 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Any:
  __snake_case:						Optional[Any]   =						{}

  __snake_case:						int   =						r""".*sequential.(\d+).*"""
  __snake_case:						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:
        __snake_case:						Tuple   =						key.replace(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)

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

      __snake_case:						str   =						key.replace(F'''sequential.{sequential_layer}.'''					,					F'''layers.{int(SCREAMING_SNAKE_CASE__)//3}.linear.''')
    elif re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__):
      __snake_case:						Any   =						int(re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__).group(1))

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

      __snake_case:						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
      __snake_case:						List[str]   =						value
      __snake_case:						Optional[Any]   =						mixed_qkv.size(0) // 3

      __snake_case:						Union[str, Any]   =						mixed_qkv[:qkv_dim]
      __snake_case:						Dict   =						mixed_qkv[qkv_dim : qkv_dim * 2]
      __snake_case:						int   =						mixed_qkv[qkv_dim * 2 :]

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

  return model_state_dict







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False)   ->    Optional[Any]:
  __snake_case  ,   __snake_case:						List[str]   =						init_clap(SCREAMING_SNAKE_CASE__					,					enable_fusion=SCREAMING_SNAKE_CASE__)

  clap_model.eval()
  __snake_case:						List[str]   =						clap_model.state_dict()
  __snake_case:						Optional[int]   =						rename_state_dict(SCREAMING_SNAKE_CASE__)

  __snake_case:						Any   =						ClapConfig()
  __snake_case:						Dict   =						enable_fusion
  __snake_case:						List[str]   =						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__":
      __UpperCAmelCase       :   Union[str, Any]				       =							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       :   Tuple				       =							parser.parse_args()

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






