





from math import factorial







class     __snake_case       :





       '''simple docstring'''







       def __init__(  self :    int							,				A :    List[str]							,				A :    int		):
         __snake_case:						str   =						real
         if isinstance(A							,				A		):
           __snake_case:						str   =						[1] * rank
         else:
           __snake_case:						List[str]   =						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 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Union[str, Any]   =						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		)
         __snake_case:						Optional[int]   =						self.duals.copy()
         __snake_case:						List[Any]   =						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		))		)
         __snake_case:						int   =						[]
         for i in range(len(A		)		):
           new_duals.append(s_dual[i] + o_dual[i]		)
         return Dual(self.real + other.real							,				A		)

       lowerCAmelCase__      		=     __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		):
           __snake_case:						Dict   =						[]
           for i in self.duals:
             new_duals.append(i * other		)
           return Dual(self.real * other							,				A		)
         __snake_case:						List[Any]   =						[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		)

       lowerCAmelCase__      		=     __mul__




       def __truediv__(  self :    Optional[Any]							,				A :    Optional[int]		):
         if not isinstance(A							,				A		):
           __snake_case:						Union[str, Any]   =						[]
           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		):
           __snake_case:						Optional[Any]   =						[]
           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
         __snake_case:						int   =						self
         for _ in range(n - 1		):
           x *= self
         return x







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Optional[int]:
  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""")
  __snake_case:						List[str]   =						Dual(SCREAMING_SNAKE_CASE__					,					1)
  __snake_case:						Optional[int]   =						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 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Tuple:
        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)






