


'''simple docstring'''







import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase    :     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""",
}

lowerCAmelCase    :     Tuple					=						AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")






def 			lowercase				(_A   ,						_A=False					):







       """simple docstring"""







       _lowerCAmelCase			,     _lowerCAmelCase    :							List[Any]   			=       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 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Any   			=       {}

       _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(_A   ,						_A					)

              if re.match(_A   ,						_A					):
                     # replace sequential layers with list
                     _lowerCAmelCase    :							Optional[int]   			=       re.match(_A   ,						_A					).group(1					)

                     _lowerCAmelCase    :							Optional[int]   			=       key.replace(f'sequential.{sequential_layer}.'   ,						f'layers.{int(_A					)//3}.linear.'					)
              elif re.match(_A   ,						_A					):
                     _lowerCAmelCase    :							int   			=       int(re.match(_A   ,						_A					).group(1					)					)

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

                     _lowerCAmelCase    :							Any   			=       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    :							List[Any]   			=       value
                     _lowerCAmelCase    :							Optional[Any]   			=       mixed_qkv.size(0					) // 3

                     _lowerCAmelCase    :							Optional[Any]   			=       mixed_qkv[:qkv_dim]
                     _lowerCAmelCase    :							Union[str, Any]   			=       mixed_qkv[qkv_dim : qkv_dim * 2]
                     _lowerCAmelCase    :							int   			=       mixed_qkv[qkv_dim * 2 :]

                     _lowerCAmelCase    :							Any   			=       query_layer
                     _lowerCAmelCase    :							List[Any]   			=       key_layer
                     _lowerCAmelCase    :							List[str]   			=       value_layer
              else:
                     _lowerCAmelCase    :							Optional[int]   			=       value

       return model_state_dict






def 			lowercase				(_A   ,						_A   ,						_A   ,						_A=False					):







       """simple docstring"""







       _lowerCAmelCase			,     _lowerCAmelCase    :							Dict   			=       init_clap(_A   ,						enable_fusion=_A					)

       clap_model.eval()
       _lowerCAmelCase    :							Tuple   			=       clap_model.state_dict()
       _lowerCAmelCase    :							List[str]   			=       rename_state_dict(_A					)

       _lowerCAmelCase    :							List[str]   			=       ClapConfig()
       _lowerCAmelCase    :							int   			=       enable_fusion
       _lowerCAmelCase    :							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__":
       lowerCAmelCase    :     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""")
       lowerCAmelCase    :     Dict					=						parser.parse_args()

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





'''simple docstring'''







def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[int]   			=       (boundary[1] - boundary[0]) / steps
       _lowerCAmelCase    :							Any   			=       boundary[0]
       _lowerCAmelCase    :							List[str]   			=       boundary[1]
       _lowerCAmelCase    :							Tuple   			=       make_points(_A   ,						_A   ,						_A					)
       _lowerCAmelCase    :							Tuple   			=       0.0
       y += (h / 2.0) * f(_A					)
       for i in x_i:
              # print(i)
              y += h * f(_A					)
       y += (h / 2.0) * f(_A					)
       return y






def 			lowercase				(_A   ,						_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Tuple   			=       a + h
       while x < (b - h):
              yield x
              _lowerCAmelCase    :							Any   			=       x + h






def 			lowercase				(_A					):  # enter your function here







       """simple docstring"""







       _lowerCAmelCase    :							int   			=       (x - 0) * (x - 0)
       return y






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Optional[Any]   			=       0.0  # Lower bound of integration
       _lowerCAmelCase    :							Dict   			=       1.0  # Upper bound of integration
       _lowerCAmelCase    :							Optional[Any]   			=       10.0  # define number of steps or resolution
       _lowerCAmelCase    :							Optional[int]   			=       [a, b]  # define boundary of integration
       _lowerCAmelCase    :							List[Any]   			=       method_a(_A   ,						_A					)
       print(f'y = {y}'					)


if __name__ == "__main__":
       main()

