
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_lowerCamelCase					   ={
    """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""",
}

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





def     _a					(						lowerCamelCase,	lowerCamelCase=False							):
 lowerCamelCase			,    lowerCamelCase		:	int							=      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     _a					(						lowerCamelCase							):
 lowerCamelCase		:	List[Any]							=      {}

 lowerCamelCase		:	str							=      R""".*sequential.(\d+).*"""
 lowerCamelCase		:	str							=      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		:	int							=      key.replace(lowerCamelCase,	lowerCamelCase							)

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

   lowerCamelCase		:	Optional[int]							=      key.replace(F'''sequential.{sequential_layer}.''',	F'''layers.{int(lowerCamelCase							)//3}.linear.'''							)
  elif re.match(lowerCamelCase,	lowerCamelCase							):
   lowerCamelCase		:	Tuple							=      int(re.match(lowerCamelCase,	lowerCamelCase							).group(1							)							)

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

   lowerCamelCase		:	Optional[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
   lowerCamelCase		:	Dict							=      value
   lowerCamelCase		:	int							=      mixed_qkv.size(0							) // 3

   lowerCamelCase		:	Dict							=      mixed_qkv[:qkv_dim]
   lowerCamelCase		:	List[str]							=      mixed_qkv[qkv_dim : qkv_dim * 2]
   lowerCamelCase		:	Tuple							=      mixed_qkv[qkv_dim * 2 :]

   lowerCamelCase		:	List[str]							=      query_layer
   lowerCamelCase		:	List[str]							=      key_layer
   lowerCamelCase		:	Union[str, Any]							=      value_layer
  else:
   lowerCamelCase		:	Optional[Any]							=      value

 return model_state_dict





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=False							):
 lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      init_clap(lowerCamelCase,	enable_fusion=lowerCamelCase							)

 clap_model.eval()
 lowerCamelCase		:	List[Any]							=      clap_model.state_dict()
 lowerCamelCase		:	str							=      rename_state_dict(lowerCamelCase							)

 lowerCamelCase		:	Union[str, Any]							=      ClapConfig()
 lowerCamelCase		:	List[Any]							=      enable_fusion
 lowerCamelCase		:	Tuple							=      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__":
      _lowerCamelCase					   =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""")
      _lowerCamelCase					   =parser.parse_args()

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






import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""conditional_detr"""
      _UpperCAmelCase				:    Optional[int]           =			["""past_key_values"""]
      _UpperCAmelCase				:    Optional[Any]           =			{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__=None    ,  __magic_name__=3    ,  __magic_name__=3_0_0    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=True    ,  __magic_name__="relu"    ,  __magic_name__=2_5_6    ,  __magic_name__=0.1    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.02    ,  __magic_name__=1.0    ,  __magic_name__=False    ,  __magic_name__="sine"    ,  __magic_name__="resnet50"    ,  __magic_name__=True    ,  __magic_name__=False    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=1    ,  __magic_name__=1    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=0.25    ,  **__magic_name__    ,  ):
       if backbone_config is not None and use_timm_backbone:
        raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""      )

       if not use_timm_backbone:
        if backbone_config is None:
         logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""      )
         lowerCamelCase		:	Optional[int]							=      CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]      )
        elif isinstance(__magic_name__    ,  __magic_name__      ):
         lowerCamelCase		:	List[Any]							=      backbone_config.get("""model_type"""      )
         lowerCamelCase		:	Dict							=      CONFIG_MAPPING[backbone_model_type]
         lowerCamelCase		:	str							=      config_class.from_dict(__magic_name__      )

       lowerCamelCase		:	Dict							=      use_timm_backbone
       lowerCamelCase		:	str							=      backbone_config
       lowerCamelCase		:	Tuple							=      num_channels
       lowerCamelCase		:	Dict							=      num_queries
       lowerCamelCase		:	Any							=      d_model
       lowerCamelCase		:	Optional[Any]							=      encoder_ffn_dim
       lowerCamelCase		:	List[str]							=      encoder_layers
       lowerCamelCase		:	Union[str, Any]							=      encoder_attention_heads
       lowerCamelCase		:	Any							=      decoder_ffn_dim
       lowerCamelCase		:	Dict							=      decoder_layers
       lowerCamelCase		:	Union[str, Any]							=      decoder_attention_heads
       lowerCamelCase		:	Dict							=      dropout
       lowerCamelCase		:	List[str]							=      attention_dropout
       lowerCamelCase		:	Union[str, Any]							=      activation_dropout
       lowerCamelCase		:	Optional[int]							=      activation_function
       lowerCamelCase		:	int							=      init_std
       lowerCamelCase		:	str							=      init_xavier_std
       lowerCamelCase		:	Tuple							=      encoder_layerdrop
       lowerCamelCase		:	str							=      decoder_layerdrop
       lowerCamelCase		:	Tuple							=      encoder_layers
       lowerCamelCase		:	Optional[int]							=      auxiliary_loss
       lowerCamelCase		:	Optional[Any]							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      backbone
       lowerCamelCase		:	Union[str, Any]							=      use_pretrained_backbone
       lowerCamelCase		:	str							=      dilation
       # Hungarian matcher
       lowerCamelCase		:	Optional[Any]							=      class_cost
       lowerCamelCase		:	Dict							=      bbox_cost
       lowerCamelCase		:	Tuple							=      giou_cost
       # Loss coefficients
       lowerCamelCase		:	Union[str, Any]							=      mask_loss_coefficient
       lowerCamelCase		:	Dict							=      dice_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      cls_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      bbox_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      giou_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      focal_alpha
       super().__init__(is_encoder_decoder=__magic_name__    ,  **__magic_name__      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.encoder_attention_heads
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.d_model






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      copy.deepcopy(self.__dict__      )
       if self.backbone_config is not None:
        lowerCamelCase		:	Optional[int]							=      self.backbone_config.to_dict()
       lowerCamelCase		:	Optional[Any]							=      self.__class__.model_type
       return output





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Any           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-5






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




