






'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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


def 							snake_case					(     a_							: List[Any]    ,   a_							: Tuple=False	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_    ,			UpperCamelCase_				:   List[str]					    =							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 							snake_case					(     a_							: int	)       ->				str:





      """simple docstring"""

      UpperCamelCase_				:   List[str]					    =							{}

      UpperCamelCase_				:   str					    =							r""".*sequential.(\d+).*"""
      UpperCamelCase_				:   Dict					    =							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_				:   Optional[Any]					    =							key.replace(UpperCamelCase__    ,   UpperCamelCase__	)

            if re.match(UpperCamelCase__    ,   UpperCamelCase__	):
                  # replace sequential layers with list
                  UpperCamelCase_				:   Dict					    =							re.match(UpperCamelCase__    ,   UpperCamelCase__	).group(1	)

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

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

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

                  UpperCamelCase_				:   Optional[Any]					    =							mixed_qkv[:qkv_dim]
                  UpperCamelCase_				:   int					    =							mixed_qkv[qkv_dim : qkv_dim * 2]
                  UpperCamelCase_				:   Optional[Any]					    =							mixed_qkv[qkv_dim * 2 :]

                  UpperCamelCase_				:   Optional[int]					    =							query_layer
                  UpperCamelCase_				:   Optional[Any]					    =							key_layer
                  UpperCamelCase_				:   Union[str, Any]					    =							value_layer
            else:
                  UpperCamelCase_				:   Any					    =							value

      return model_state_dict


def 							snake_case					(     a_							: Any    ,   a_							: Optional[Any]    ,   a_							: Dict    ,   a_							: Optional[int]=False	)       ->				List[str]:





      """simple docstring"""

      UpperCamelCase_    ,			UpperCamelCase_				:   int					    =							init_clap(UpperCamelCase__    ,   enable_fusion=UpperCamelCase__	)

      clap_model.eval()
      UpperCamelCase_				:   str					    =							clap_model.state_dict()
      UpperCamelCase_				:   int					    =							rename_state_dict(UpperCamelCase__	)

      UpperCamelCase_				:   Optional[int]					    =							ClapConfig()
      UpperCamelCase_				:   Optional[int]					    =							enable_fusion
      UpperCamelCase_				:   List[Any]					    =							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__":
      UpperCamelCase							=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							=parser.parse_args()

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














'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


UpperCamelCase							=re.compile(R"([A-Z]+)([A-Z][a-z])")
UpperCamelCase							=re.compile(R"([a-z\d])([A-Z])")

UpperCamelCase							=re.compile(R"(?<!_)_(?!_)")
UpperCamelCase							=re.compile(R"(_{2,})")

UpperCamelCase							=R"^\w+(\.\w+)*$"

UpperCamelCase							=R"<>:/\|?*"



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   str					    =							_uppercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      UpperCamelCase_				:   List[Any]					    =							_lowercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      return name.lower()



def 							snake_case					(     a_							: Any	)       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							_single_underscore_re.split(a_	)
      UpperCamelCase_				:   Tuple					    =							[_multiple_underscores_re.split(a_	) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(a_	) if n != """"""	)



def 							snake_case					(     a_							: Optional[int]	)       ->				str:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      return camelcase_to_snakecase(a_	)



def 							snake_case					(     a_							: List[Any]    ,   a_							: Optional[int]	)       ->				Optional[Any]:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      if not re.match(_split_re    ,   a_	):
            raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."	)
      return f"{filename_prefix_for_name(a_	)}-{split}"



def 							snake_case					(     a_							: Optional[Any]    ,   a_							: int    ,   a_							: List[Any]    ,   a_							: Union[str, Any]=None	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							filename_prefix_for_split(a_    ,   a_	)
      if filetype_suffix:
            prefix += f".{filetype_suffix}"
      UpperCamelCase_				:   str					    =							os.path.join(a_    ,   a_	)
      return f"{filepath}*"



def 							snake_case					(     a_							: Tuple    ,   a_							: Any    ,   a_							: Optional[int]    ,   a_							: Union[str, Any]=None    ,   a_							: Optional[Any]=None	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							filename_prefix_for_split(a_    ,   a_	)
      UpperCamelCase_				:   Dict					    =							os.path.join(a_    ,   a_	)

      if shard_lengths:
            UpperCamelCase_				:   Optional[int]					    =							len(a_	)
            UpperCamelCase_				:   Tuple					    =							[f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(a_	)]
            if filetype_suffix:
                  UpperCamelCase_				:   Tuple					    =							[filename + f".{filetype_suffix}" for filename in filenames]
            return filenames
      else:
            UpperCamelCase_				:   Dict					    =							prefix
            if filetype_suffix:
                  filename += f".{filetype_suffix}"
            return [filename]






