


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__UpperCamelCase    :						Union[str, 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    :						Dict     =							AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Optional[Any]=False      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      List[Any]       						=						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      :			Optional[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Dict       						=						{}

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

            if re.match(UpperCAmelCase						,	UpperCAmelCase      ):
                  # replace sequential layers with list
                  __lowerCamelCase     :      int       						=						re.match(UpperCAmelCase						,	UpperCAmelCase      ).group(1      )

                  __lowerCamelCase     :      List[str]       						=						key.replace(f"""sequential.{sequential_layer}."""						,	f"""layers.{int(UpperCAmelCase      )//3}.linear."""      )
            elif re.match(UpperCAmelCase						,	UpperCAmelCase      ):
                  __lowerCamelCase     :      int       						=						int(re.match(UpperCAmelCase						,	UpperCAmelCase      ).group(1      )      )

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

                  __lowerCamelCase     :      List[str]       						=						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     :      int       						=						value
                  __lowerCamelCase     :      Union[str, Any]       						=						mixed_qkv.size(0      ) // 3

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

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

      return model_state_dict





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			Union[str, Any]=False      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						init_clap(UpperCAmelCase						,	enable_fusion=UpperCAmelCase      )

      clap_model.eval()
      __lowerCamelCase     :      Union[str, Any]       						=						clap_model.state_dict()
      __lowerCamelCase     :      int       						=						rename_state_dict(UpperCAmelCase      )

      __lowerCamelCase     :      Optional[Any]       						=						ClapConfig()
      __lowerCamelCase     :      int       						=						enable_fusion
      __lowerCamelCase     :      Optional[int]       						=						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    :						List[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')
    __UpperCamelCase    :						Any     =							parser.parse_args()

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







import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Any=1_024      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      str       						=						[], []

      __lowerCamelCase     :      Any       						=						list(zip(UpperCAmelCase						,	UpperCAmelCase      )      )
      __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						sorted_examples[0]

      def is_too_big(UpperCAmelCase      :			Optional[Any]      ):
            return tok(UpperCAmelCase						,	return_tensors="""pt"""      ).input_ids.shape[1] > max_tokens

      for src, tgt in tqdm(sorted_examples[1:]      ):
            __lowerCamelCase     :      Union[str, Any]       						=						new_src + """ """ + src
            __lowerCamelCase     :      str       						=						new_tgt + """ """ + tgt
            if is_too_big(UpperCAmelCase      ) or is_too_big(UpperCAmelCase      ):  # cant fit, finalize example
                  finished_src.append(UpperCAmelCase      )
                  finished_tgt.append(UpperCAmelCase      )
                  __lowerCamelCase						, __lowerCamelCase     :      str       						=						src, tgt
            else:  # can fit, keep adding
                  __lowerCamelCase						, __lowerCamelCase     :      int       						=						cand_src, cand_tgt

    # cleanup
      if new_src:
            assert new_tgt
            finished_src.append(UpperCAmelCase      )
            finished_tgt.append(UpperCAmelCase      )
      return finished_src, finished_tgt





def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Path						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						Path(UpperCAmelCase      )
      save_path.mkdir(exist_ok=UpperCAmelCase      )
      for split in ["train"]:
            __lowerCamelCase						, __lowerCamelCase     :      List[Any]       						=						data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
            __lowerCamelCase     :      Tuple       						=						[x.rstrip() for x in Path(UpperCAmelCase      ).open().readlines()]
            __lowerCamelCase     :      Tuple       						=						[x.rstrip() for x in Path(UpperCAmelCase      ).open().readlines()]
            __lowerCamelCase						, __lowerCamelCase     :      int       						=						pack_examples(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
            print(f"""packed {split} split from {len(UpperCAmelCase      )} examples -> {len(UpperCAmelCase      )}."""      )
            Path(save_path / f"""{split}.source"""      ).open("""w"""      ).write("""\n""".join(UpperCAmelCase      )      )
            Path(save_path / f"""{split}.target"""      ).open("""w"""      ).write("""\n""".join(UpperCAmelCase      )      )
      for split in ["val", "test"]:
            __lowerCamelCase						, __lowerCamelCase     :      Optional[Any]       						=						data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
            shutil.copyfile(UpperCAmelCase						,	save_path / f"""{split}.source"""      )
            shutil.copyfile(UpperCAmelCase						,	save_path / f"""{split}.target"""      )





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						argparse.ArgumentParser()
      parser.add_argument("""--tok_name"""						,	type=UpperCAmelCase						,	help="""like facebook/bart-large-cnn,t5-base, etc."""      )
      parser.add_argument("""--max_seq_len"""						,	type=UpperCAmelCase						,	default=128      )
      parser.add_argument("""--data_dir"""						,	type=UpperCAmelCase      )
      parser.add_argument("""--save_path"""						,	type=UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						parser.parse_args()
      __lowerCamelCase     :      Tuple       						=						AutoTokenizer.from_pretrained(args.tok_name      )
      return pack_data_dir(UpperCAmelCase						,	Path(args.data_dir      )						,	args.max_seq_len						,	args.save_path      )


if __name__ == "__main__":
    packer_cli()



