




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a_   		=     {
    "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",
}

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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[int]:

    """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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_=False      )       ->	str:

    """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__":
 a_   		=     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")
 a_   		=     parser.parse_args()

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












from abc import ABC, abstractmethod
from argparse import ArgumentParser

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @staticmethod
      @abstractmethod
      def       snake_case	(     snake_case					: ArgumentParser				):
          raise NotImplementedError()







      @abstractmethod
      def       snake_case	(     self					: Optional[Any]				):
          raise NotImplementedError()






