

'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase__			    =			{
    """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""",
}

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




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_=False	):
 UpperCAmelCase					, UpperCAmelCase       :	List[str]											= create_model(
     'HTSAT-tiny'  ,       'roberta'  ,       lowerCAmelCase__  ,       precision='fp32'  ,       device='cuda:0' if torch.cuda.is_available() else 'cpu'  ,       enable_fusion=lowerCAmelCase__  ,       fusion_type='aff_2d' if enable_fusion else None  ,       )
 return model, model_cfg




def       UpperCamelCase(     UpperCAmelCase_	):
 UpperCAmelCase       :	int											= {}

 UpperCAmelCase       :	Union[str, Any]											= R'.*sequential.(\d+).*'
 UpperCAmelCase       :	Optional[int]											= 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(lowerCAmelCase__  ,       lowerCAmelCase__	)

  if re.match(lowerCAmelCase__  ,       lowerCAmelCase__	):
   # replace sequential layers with list
   UpperCAmelCase       :	Optional[Any]											= re.match(lowerCAmelCase__  ,       lowerCAmelCase__	).group(1	)

   UpperCAmelCase       :	Tuple											= key.replace(F"""sequential.{sequential_layer}."""  ,       F"""layers.{int(lowerCAmelCase__	)//3}.linear."""	)
  elif re.match(lowerCAmelCase__  ,       lowerCAmelCase__	):
   UpperCAmelCase       :	Tuple											= int(re.match(lowerCAmelCase__  ,       lowerCAmelCase__	).group(1	)	)

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

   UpperCAmelCase       :	int											= 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       :	str											= value
   UpperCAmelCase       :	Optional[int]											= mixed_qkv.size(0	) // 3

   UpperCAmelCase       :	List[str]											= mixed_qkv[:qkv_dim]
   UpperCAmelCase       :	Tuple											= mixed_qkv[qkv_dim : qkv_dim * 2]
   UpperCAmelCase       :	Union[str, Any]											= mixed_qkv[qkv_dim * 2 :]

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

 return model_state_dict




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_=False	):
 UpperCAmelCase					, UpperCAmelCase       :	str											= init_clap(lowerCAmelCase__  ,       enable_fusion=lowerCAmelCase__	)

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

 UpperCAmelCase       :	Optional[Any]											= ClapConfig()
 UpperCAmelCase       :	List[Any]											= enable_fusion
 UpperCAmelCase       :	Union[str, Any]											= ClapModel(lowerCAmelCase__	)

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

 model.save_pretrained(lowerCAmelCase__	)
 transformers_config.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
 lowercase__			    =			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")
 lowercase__			    =			parser.parse_args()

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





"""simple docstring"""






import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str = None					,		lowerCAmelCase__     :str = None					,		lowerCAmelCase__     :str = None					,		) ->    Optional[int]:
      '''simple docstring'''


      if config_name_or_path is None:
            lowercase														=	"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

      if generator_tokenizer_name_or_path is None:
            lowercase														=	generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            lowercase														=	question_encoder_name_or_path

      lowercase														=	RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

      # Save model.
      lowercase														=	RagConfig.from_pretrained(lowerCAmelCase__		)
      lowercase														=	AutoConfig.from_pretrained(lowerCAmelCase__		)
      lowercase														=	AutoConfig.from_pretrained(lowerCAmelCase__		)

      lowercase														=	gen_config
      lowercase														=	question_encoder_config

      lowercase														=	model_class.from_pretrained_question_encoder_generator(
          lowerCAmelCase__					,		lowerCAmelCase__					,		config=lowerCAmelCase__		)
      rag_model.save_pretrained(lowerCAmelCase__		)

      # Sanity check.
      model_class.from_pretrained(lowerCAmelCase__		)

      # Save tokenizers.
      lowercase														=	AutoTokenizer.from_pretrained(lowerCAmelCase__		)
      gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""		)
      lowercase														=	AutoTokenizer.from_pretrained(lowerCAmelCase__		)
      question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""		)


if __name__ == "__main__":
       __lowerCAmelCase    :       int					     =argparse.ArgumentParser()
       parser.add_argument(
           """--model_type""",
           choices=["""rag_sequence""", """rag_token"""],
           required=True,
           type=str,
           help="""RAG model type: rag_sequence, rag_token""",
       )
       parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
       parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
       parser.add_argument(
           """--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
       )

       parser.add_argument(
           """--generator_tokenizer_name_or_path""",
           type=str,
           help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
       )
       parser.add_argument(
           """--question_encoder_tokenizer_name_or_path""",
           type=str,
           help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
       )
       parser.add_argument(
           """--config_name_or_path""",
           type=str,
           help=(
               """Identifier of the model config to use, if not provided, resolves to a base config for a given"""
               """ ``model_type``"""
           ),
       )

       __lowerCAmelCase    :       List[str]					     =parser.parse_args()

       __lowerCAmelCase    :       Dict					     =Path(args.dest)
       dest_dir.mkdir(exist_ok=True)

       consolidate(
           args.model_type,
           args.generator_name_or_path,
           args.question_encoder_name_or_path,
           dest_dir,
           args.config_name_or_path,
           args.generator_tokenizer_name_or_path,
           args.question_encoder_tokenizer_name_or_path,
       )



