






"""simple docstring"""







import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_UpperCamelCase					      =  logging.get_logger(__name__)







class  lowerCamelCase__ :
    SCREAMING_SNAKE_CASE		=      None







@experimental
def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      if ParallelBackendConfig.backend_name is None:
            return _map_with_multiprocessing_pool(
                _snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							)

      return _map_with_joblib(_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							)

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	num_proc if num_proc <= len(_snake_case							) else len(_snake_case							)
      UpperCAmelCase          =	[]  # We organize the splits ourselve (contiguous splits)
      for index in range(_snake_case							):
            UpperCAmelCase          =	len(_snake_case							) // num_proc
            UpperCAmelCase          =	len(_snake_case							) % num_proc
            UpperCAmelCase          =	div * index + min(_snake_case				,	_snake_case							)
            UpperCAmelCase          =	start + div + (1 if index < mod else 0)
            split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)							)

      if len(_snake_case							) != sum(len(i[1]							) for i in split_kwds							):
            raise ValueError(
                F'''Error dividing inputs iterable among processes. '''
                F'''Total number of objects {len(_snake_case							)}, '''
                F'''length: {sum(len(i[1]							) for i in split_kwds							)}'''							)

      logger.info(
          F'''Spawning {num_proc} processes for {len(_snake_case							)} objects in slices of {[len(i[1]							) for i in split_kwds]}'''							)
      UpperCAmelCase      , UpperCAmelCase          =	None, None
      if not disable_tqdm:
            UpperCAmelCase      , UpperCAmelCase          =	(RLock(),), tqdm.set_lock
      with Pool(_snake_case				,	initargs=_snake_case				,	initializer=_snake_case							) as pool:
            UpperCAmelCase          =	pool.map(_snake_case				,	_snake_case							)
      logger.info(F'''Finished {num_proc} processes'''							)
      UpperCAmelCase          =	[obj for proc_res in mapped for obj in proc_res]
      logger.info(F'''Unpacked {len(_snake_case							)} objects'''							)

      return mapped

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      import joblib

      with joblib.parallel_backend(ParallelBackendConfig.backend_name				,	n_jobs=_snake_case							):
            return joblib.Parallel()(
                joblib.delayed(_snake_case							)((function, obj, types, None, True, None)							) for obj in iterable							)






@experimental
@contextlib.contextmanager
def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	backend_name

      if backend_name == "spark":
            from joblibspark import register_spark

            register_spark()

            # TODO: call create_cache_and_write_probe if "download" in steps
            # TODO: raise NotImplementedError when Dataset.map etc is called

      try:
            yield
      finally:
            UpperCAmelCase          =	None











"""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 _a			(		_snake_case				,	_snake_case=False							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	create_model(
          """HTSAT-tiny"""				,	"""roberta"""				,	_snake_case				,	precision="""fp32"""				,	device="""cuda:0""" if torch.cuda.is_available() else """cpu"""				,	enable_fusion=_snake_case				,	fusion_type="""aff_2d""" if enable_fusion else None				,	)
      return model, model_cfg

def _a			(		_snake_case							):







      """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(_snake_case				,	_snake_case							)

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

                  UpperCAmelCase          =	key.replace(F'''sequential.{sequential_layer}.'''				,	F'''layers.{int(_snake_case							)//3}.linear.'''							)
            elif re.match(_snake_case				,	_snake_case							):
                  UpperCAmelCase          =	int(re.match(_snake_case				,	_snake_case							).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 _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case=False							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	init_clap(_snake_case				,	enable_fusion=_snake_case							)

      clap_model.eval()
      UpperCAmelCase          =	clap_model.state_dict()
      UpperCAmelCase          =	rename_state_dict(_snake_case							)

      UpperCAmelCase          =	ClapConfig()
      UpperCAmelCase          =	enable_fusion
      UpperCAmelCase          =	ClapModel(_snake_case							)

      # ignore the spectrogram embedding layer
      model.load_state_dict(_snake_case				,	strict=_snake_case							)

      model.save_pretrained(_snake_case							)
      transformers_config.save_pretrained(_snake_case							)


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)



