



'''simple docstring'''





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase_		       =  {
    "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",
}

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




def lowerCAmelCase  (__A  ,	__A=False):



     """simple docstring"""



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




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  {}

     _a      =  r'''.*sequential.(\d+).*'''
     _a      =  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:
                    _a      =  key.replace(__A  ,	__A)

          if re.match(__A  ,	__A):
               # replace sequential layers with list
               _a      =  re.match(__A  ,	__A).group(1)

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

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

               _a      =  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
               _a      =  value
               _a      =  mixed_qkv.size(0) // 3

               _a      =  mixed_qkv[:qkv_dim]
               _a      =  mixed_qkv[qkv_dim : qkv_dim * 2]
               _a      =  mixed_qkv[qkv_dim * 2 :]

               _a      =  query_layer
               _a      =  key_layer
               _a      =  value_layer
          else:
               _a      =  value

     return model_state_dict




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A=False):



     """simple docstring"""



     _a  ,    _a      =  init_clap(__A  ,	enable_fusion=__A)

     clap_model.eval()
     _a      =  clap_model.state_dict()
     _a      =  rename_state_dict(__A)

     _a      =  ClapConfig()
     _a      =  enable_fusion
     _a      =  ClapModel(__A)

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

     model.save_pretrained(__A)
     transformers_config.save_pretrained(__A)


if __name__ == "__main__":
  lowercase_		       =  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")
  lowercase_		       =  parser.parse_args()

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








'''simple docstring'''





import math






class __A			:



 '''simple docstring'''







 def __init__(self ,   A=0       )    ->     Dict:  # a graph with Node 0,1,...,N-1


      """simple docstring"""



      _a      =  n
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # adjacency matrix for weight
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # dp[i][j] stores minimum distance from i to j



 def 					a__  (self ,   A ,   A ,   A       )    ->     Tuple:


      """simple docstring"""



      _a      =  w



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      for k in range(0 ,   self.n       ):
           for i in range(0 ,   self.n       ):
                for j in range(0 ,   self.n       ):
                     _a      =  min(self.dp[i][j] ,   self.dp[i][k] + self.dp[k][j]       )



 def 					a__  (self ,   A ,   A       )    ->     str:


      """simple docstring"""



      return self.dp[u][v]


if __name__ == "__main__":
  lowercase_		       =  Graph(5)
  graph.add_edge(0, 2, 9)
  graph.add_edge(0, 4, 10)
  graph.add_edge(1, 3, 5)
  graph.add_edge(2, 3, 7)
  graph.add_edge(3, 0, 10)
  graph.add_edge(3, 1, 2)
  graph.add_edge(3, 2, 1)
  graph.add_edge(3, 4, 6)
  graph.add_edge(4, 1, 3)
  graph.add_edge(4, 2, 4)
  graph.add_edge(4, 3, 9)
  graph.floyd_warshall()
  graph.show_min(1, 4)
  graph.show_min(0, 3)



