





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


snake_case__					:							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',
}

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



def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Dict=False )	->						Dict:







      '''simple docstring'''






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



def _a				(	lowerCamelCase:   Dict )	->						Dict:







      '''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(lowerCamelCase					,				lowerCamelCase )

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

                  __A          =				key.replace(F"""sequential.{sequential_layer}."""					,				F"""layers.{int(lowerCamelCase )//3}.linear.""" )
            elif re.match(lowerCamelCase					,				lowerCamelCase ):
                  __A          =				int(re.match(lowerCamelCase					,				lowerCamelCase ).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 _a				(	lowerCamelCase:   Optional[Any]					,				lowerCamelCase:   List[Any]					,				lowerCamelCase:   Optional[Any]					,				lowerCamelCase:   Optional[int]=False )	->						int:







      '''simple docstring'''






      __A   ,						__A          =				init_clap(lowerCamelCase					,				enable_fusion=lowerCamelCase )

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

      __A          =				ClapConfig()
      __A          =				enable_fusion
      __A          =				ClapModel(lowerCamelCase )

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

      model.save_pretrained(lowerCamelCase )
      transformers_config.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
       snake_case__					:							str													=     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')
       snake_case__					:							List[str]													=     parser.parse_args()

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












import os

snake_case__					:							Any													=     {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}



def _a				(	lowerCamelCase:   str )	->						int:







      '''simple docstring'''






      __A          =				0

      __A          =				0
      while index < len(lowerCamelCase ) - 1:
            __A          =				SYMBOLS[numerals[index]]
            __A          =				SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value



def _a				(	lowerCamelCase:   int )	->						str:







      '''simple docstring'''






      __A          =				''''''

      __A          =				num // 10_00
      numerals += m_count * "M"
      num %= 10_00

      __A          =				num // 1_00
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 1_00

      __A          =				num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals



def _a				(	lowerCamelCase:   str = "/p089_roman.txt" )	->						int:







      '''simple docstring'''






      __A          =				0

      with open(os.path.dirname(lowerCamelCase ) + roman_numerals_filename ) as filea:
            __A          =				filea.readlines()

      for line in lines:
            __A          =				line.strip()
            __A          =				parse_roman_numerals(lowerCamelCase )
            __A          =				generate_roman_numerals(lowerCamelCase )
            savings += len(lowerCamelCase ) - len(lowerCamelCase )

      return savings


if __name__ == "__main__":
       print(f'{solution() = }')





