


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a_ :Tuple  							=			{
    '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',
}

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




def    a   (							A__      ,    A__=False							)	-> List[str]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		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    a   (							A__							)	-> Union[str, Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Dict						     =		{}

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		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__       :  Dict						     =		key.replace(A__      ,    A__							)

            if re.match(A__      ,    A__							):
                  # replace sequential layers with list
                  SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		re.match(A__      ,    A__							).group(1							)

                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		key.replace(f"""sequential.{sequential_layer}."""      ,    f"""layers.{int(A__							)//3}.linear."""							)
            elif re.match(A__      ,    A__							):
                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		int(re.match(A__      ,    A__							).group(1							)							)

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

                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		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__       :  Optional[int]						     =		value
                  SCREAMING_SNAKE_CASE__       :  Tuple						     =		mixed_qkv.size(0							) // 3

                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		mixed_qkv[:qkv_dim]
                  SCREAMING_SNAKE_CASE__       :  Tuple						     =		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__       :  Dict						     =		key_layer
                  SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		value_layer
            else:
                  SCREAMING_SNAKE_CASE__       :  List[Any]						     =		value

      return model_state_dict




def    a   (							A__      ,    A__      ,    A__      ,    A__=False							)	-> List[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		init_clap(A__      ,    enable_fusion=A__							)

      clap_model.eval()
      SCREAMING_SNAKE_CASE__       :  int						     =		clap_model.state_dict()
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		rename_state_dict(A__							)

      SCREAMING_SNAKE_CASE__       :  str						     =		ClapConfig()
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		enable_fusion
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		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__":
    a_ :List[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')
    a_ :Any  							=			parser.parse_args()

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










'''simple docstring'''

from __future__ import annotations

lowerCamelCase				:  List[str]    		=      []

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			bool:






       """simple docstring"""







       for i in range(len(A			)			):
              if board[row][i] == 1:
                     return False
       for i in range(len(A			)			):
              if board[i][column] == 1:
                     return False
       for i, j in zip(range(A    ,					-1    ,					-1			)    ,					range(A    ,					-1    ,					-1			)			):
              if board[i][j] == 1:
                     return False
       for i, j in zip(range(A    ,					-1    ,					-1			)    ,					range(A    ,					len(A			)			)			):
              if board[i][j] == 1:
                     return False
       return True

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			bool:






       """simple docstring"""







       if row >= len(A			):
              solution.append(A			)
              printboard(A			)
              print()
              return True
       for i in range(len(A			)			):
              if is_safe(A    ,					A    ,					A			):
                     lowercase__      =					1
                     solve(A    ,					row + 1			)
                     lowercase__      =					0
       return False

def _SCREAMING_SNAKE_CASE (A			)			->			None:






       """simple docstring"""







       for i in range(len(A			)			):
              for j in range(len(A			)			):
                     if board[i][j] == 1:
                            print('''Q'''    ,					end=''' '''			)
                     else:
                            print('''.'''    ,					end=''' '''			)
              print()


# n=int(input("The no. of queens"))
lowerCamelCase				:  Optional[Any]    		=      8
lowerCamelCase				:  Optional[int]    		=      [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print('The total no. of solutions are :', len(solution))




