




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase__			:       Optional[int]         =							{
    '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',
}

lowerCamelCase__			:       str         =							AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Any      ,      __UpperCAmelCase						:     Optional[Any]=False							) ->			Dict:
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      create_model(
          'HTSAT-tiny'      ,      'roberta'      ,      __UpperCAmelCase      ,      precision='fp32'      ,      device='cuda:0' if torch.cuda.is_available() else 'cpu'      ,      enable_fusion=__UpperCAmelCase      ,      fusion_type='aff_2d' if enable_fusion else None      ,      )
      return model, model_cfg

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Tuple							) ->			List[Any]:
      SCREAMING_SNAKE_CASE_     						=      {}

      SCREAMING_SNAKE_CASE_     						=      r'.*sequential.(\d+).*'
      SCREAMING_SNAKE_CASE_     						=      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_     						=      key.replace(__UpperCAmelCase      ,      __UpperCAmelCase							)

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

                  SCREAMING_SNAKE_CASE_     						=      key.replace(f"sequential.{sequential_layer}."      ,      f"layers.{int(__UpperCAmelCase							)//3}.linear."							)
            elif re.match(__UpperCAmelCase      ,      __UpperCAmelCase							):
                  SCREAMING_SNAKE_CASE_     						=      int(re.match(__UpperCAmelCase      ,      __UpperCAmelCase							).group(1							)							)

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

                  SCREAMING_SNAKE_CASE_     						=      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_     						=      value
                  SCREAMING_SNAKE_CASE_     						=      mixed_qkv.size(0							) // 3

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

                  SCREAMING_SNAKE_CASE_     						=      query_layer
                  SCREAMING_SNAKE_CASE_     						=      key_layer
                  SCREAMING_SNAKE_CASE_     						=      value_layer
            else:
                  SCREAMING_SNAKE_CASE_     						=      value

      return model_state_dict

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     Optional[Any]=False							) ->			List[Any]:
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      init_clap(__UpperCAmelCase      ,      enable_fusion=__UpperCAmelCase							)

      clap_model.eval()
      SCREAMING_SNAKE_CASE_     						=      clap_model.state_dict()
      SCREAMING_SNAKE_CASE_     						=      rename_state_dict(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_     						=      ClapConfig()
      SCREAMING_SNAKE_CASE_     						=      enable_fusion
      SCREAMING_SNAKE_CASE_     						=      ClapModel(__UpperCAmelCase							)

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

      model.save_pretrained(__UpperCAmelCase							)
      transformers_config.save_pretrained(__UpperCAmelCase							)


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

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





from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
   import accelerate

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			int:
      if not is_accelerate_available():
            return method
      SCREAMING_SNAKE_CASE_     						=      version.parse(accelerate.__version__							).base_version
      if version.parse(__UpperCAmelCase							) < version.parse('0.17.0'							):
            return method

      def wrapper(self						:     Optional[int]      ,      *__UpperCAmelCase						:     Optional[Any]      ,      **__UpperCAmelCase						:     Optional[Any]							):
            if hasattr(self      ,      '_hf_hook'							) and hasattr(self._hf_hook      ,      'pre_forward'							):
                  self._hf_hook.pre_forward(self							)
            return method(self      ,      *__UpperCAmelCase      ,      **__UpperCAmelCase							)

      return wrapper