





'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase__						:      Dict	=    {
    '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__						:      Optional[Any]	=    AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')







def  a__    (    lowercase :					List[str], lowercase :					Any=False				)		->		Tuple:





     """simple docstring"""

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







def  a__    (    lowercase :					Tuple				)		->		Tuple:





     """simple docstring"""

     _UpperCamelCase       =	{}

     _UpperCamelCase       =	r'''.*sequential.(\d+).*'''
     _UpperCamelCase       =	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:
                    _UpperCamelCase       =	key.replace(lowercase, lowercase				)

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

               _UpperCamelCase       =	key.replace(F"""sequential.{sequential_layer}.""", F"""layers.{int(lowercase				)//3}.linear."""				)
          elif re.match(lowercase, lowercase				):
               _UpperCamelCase       =	int(re.match(lowercase, lowercase				).group(1				)				)

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

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

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

               _UpperCamelCase       =	query_layer
               _UpperCamelCase       =	key_layer
               _UpperCamelCase       =	value_layer
          else:
               _UpperCamelCase       =	value

     return model_state_dict







def  a__    (    lowercase :					int, lowercase :					Dict, lowercase :					Any, lowercase :					Union[str, Any]=False				)		->		Optional[Any]:





     """simple docstring"""

     _UpperCamelCase   ,    _UpperCamelCase       =	init_clap(lowercase, enable_fusion=lowercase				)

     clap_model.eval()
     _UpperCamelCase       =	clap_model.state_dict()
     _UpperCamelCase       =	rename_state_dict(lowercase				)

     _UpperCamelCase       =	ClapConfig()
     _UpperCamelCase       =	enable_fusion
     _UpperCamelCase       =	ClapModel(lowercase				)

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

     model.save_pretrained(lowercase				)
     transformers_config.save_pretrained(lowercase				)


if __name__ == "__main__":
   lowercase__						:      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')
   lowercase__						:      int	=    parser.parse_args()

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










'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey







def  a__    (    lowercase :					str				)		->		Optional[int]:





     """simple docstring"""

     _UpperCamelCase   ,    _UpperCamelCase       =	img.shape[0], img.shape[1]

     # converting each pixel's color to its negative
     for i in range(lowercase				):
          for j in range(lowercase				):
               _UpperCamelCase       =	[255, 255, 255] - img[i][j]

     return img


if __name__ == "__main__":
   # read original image
   lowercase__						:      Optional[int]	=    imread('image_data/lena.jpg', 1)

   # convert to its negative
   lowercase__						:      Union[str, Any]	=    convert_to_negative(img)

   # show result image
   imshow('negative of original image', img)
   waitKey(0)
   destroyAllWindows()



