
'''simple docstring'''






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase_       :   List[Any]   =       {
    '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',
}

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







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Any=False							):



      """simple docstring"""




      _lowerCamelCase						,		_lowerCamelCase				:   str								=   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							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[int]								=   {}

      _lowerCamelCase				:   Union[str, Any]								=   r".*sequential.(\d+).*"
      _lowerCamelCase				:   Optional[int]								=   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:
                        _lowerCamelCase				:   List[Any]								=   key.replace(lowerCAmelCase__				,	lowerCAmelCase__							)

            if re.match(lowerCAmelCase__				,	lowerCAmelCase__							):
                  # replace sequential layers with list
                  _lowerCamelCase				:   List[str]								=   re.match(lowerCAmelCase__				,	lowerCAmelCase__							).group(1							)

                  _lowerCamelCase				:   Tuple								=   key.replace(F'sequential.{sequential_layer}.'				,	F'layers.{int(lowerCAmelCase__							)//3}.linear.'							)
            elif re.match(lowerCAmelCase__				,	lowerCAmelCase__							):
                  _lowerCamelCase				:   Optional[Any]								=   int(re.match(lowerCAmelCase__				,	lowerCAmelCase__							).group(1							)							)

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

                  _lowerCamelCase				:   Dict								=   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
                  _lowerCamelCase				:   Optional[Any]								=   value
                  _lowerCamelCase				:   Dict								=   mixed_qkv.size(0							) // 3

                  _lowerCamelCase				:   List[str]								=   mixed_qkv[:qkv_dim]
                  _lowerCamelCase				:   List[Any]								=   mixed_qkv[qkv_dim : qkv_dim * 2]
                  _lowerCamelCase				:   Optional[int]								=   mixed_qkv[qkv_dim * 2 :]

                  _lowerCamelCase				:   int								=   query_layer
                  _lowerCamelCase				:   List[Any]								=   key_layer
                  _lowerCamelCase				:   Dict								=   value_layer
            else:
                  _lowerCamelCase				:   Dict								=   value

      return model_state_dict







def 		A_       (	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    Optional[Any]=False							):



      """simple docstring"""




      _lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   init_clap(lowerCAmelCase__				,	enable_fusion=lowerCAmelCase__							)

      clap_model.eval()
      _lowerCamelCase				:   int								=   clap_model.state_dict()
      _lowerCamelCase				:   Dict								=   rename_state_dict(lowerCAmelCase__							)

      _lowerCamelCase				:   Tuple								=   ClapConfig()
      _lowerCamelCase				:   List[str]								=   enable_fusion
      _lowerCamelCase				:   Dict								=   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__":
 UpperCAmelCase_       :   Tuple   =       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')
 UpperCAmelCase_       :   List[str]   =       parser.parse_args()

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



'''simple docstring'''






def 		A_       (	_lowerCAmelCase   :    float							):



      """simple docstring"""




      return 10 - x * x







def 		A_       (	_lowerCAmelCase   :    float				,	_lowerCAmelCase   :    float							):



      """simple docstring"""




      if equation(_lowerCAmelCase							) * equation(_lowerCAmelCase							) >= 0:
            raise ValueError("Wrong space!"							)

      _lowerCamelCase				:   List[str]								=   a
      while (b - a) >= 0.0_1:
            # Find middle point
            _lowerCamelCase				:   Union[str, Any]								=   (a + b) / 2
            # Check if middle point is root
            if equation(_lowerCAmelCase							) == 0.0:
                  break
            # Decide the side to repeat the steps
            if equation(_lowerCAmelCase							) * equation(_lowerCAmelCase							) < 0:
                  _lowerCamelCase				:   Union[str, Any]								=   c
            else:
                  _lowerCamelCase				:   Any								=   c
      return c


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 print(bisection(-2, 5))
 print(bisection(0, 6))