


"""simple docstring"""


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCamelCase						:				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						:				List[str]             =      AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__=False	)      ->		Tuple:







  """simple docstring"""





  A       , A					       = 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       (   UpperCamelCase__	)      ->		str:







  """simple docstring"""





  A					       = {}

  A					       = r'.*sequential.(\d+).*'
  A					       = 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:
        A					       = key.replace(UpperCamelCase__ ,			UpperCamelCase__	)

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

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

      # Because in CLAP they use `nn.Sequential`...
      A					       = 1 if projecton_layer == 0 else 2

      A					       = 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
      A					       = value
      A					       = mixed_qkv.size(0	) // 3

      A					       = mixed_qkv[:qkv_dim]
      A					       = mixed_qkv[qkv_dim : qkv_dim * 2]
      A					       = mixed_qkv[qkv_dim * 2 :]

      A					       = query_layer
      A					       = key_layer
      A					       = value_layer
    else:
      A					       = value

  return model_state_dict







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__=False	)      ->		List[str]:







  """simple docstring"""





  A       , A					       = init_clap(UpperCamelCase__ ,			enable_fusion=UpperCamelCase__	)

  clap_model.eval()
  A					       = clap_model.state_dict()
  A					       = rename_state_dict(UpperCamelCase__	)

  A					       = ClapConfig()
  A					       = enable_fusion
  A					       = 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						:				Optional[Any]             =      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						:				List[Any]             =      parser.parse_args()

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









"""simple docstring"""


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """ClapFeatureExtractor"""
						lowerCAmelCase       =     ("""RobertaTokenizer""", """RobertaTokenizerFast""")







						def __init__(							self   :    Dict			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[int]		):
								super().__init__(_lowercase			,			_lowercase		)







						def __call__(							self   :    Optional[int]			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Dict=None			,			_lowercase   :    str=None			,			**_lowercase   :    Union[str, Any]		):
								A					       = kwargs.pop('sampling_rate'			,			_lowercase		)

								if text is None and audios is None:
										raise ValueError('You have to specify either text or audios. Both cannot be none.'		)

								if text is not None:
										A					       = self.tokenizer(_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if audios is not None:
										A					       = self.feature_extractor(
										    _lowercase			,			sampling_rate=_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if text is not None and audios is not None:
										A					       = audio_features.input_features
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**_lowercase		)			,			tensor_type=_lowercase		)







						def __a						(							self   :    List[Any]			,			*_lowercase   :    Dict			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.batch_decode(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    List[str]			,			*_lowercase   :    str			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.decode(*_lowercase			,			**_lowercase		)







						@property
						def __a						(							self   :    Tuple		):
								A					       = self.tokenizer.model_input_names
								A					       = self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)





