

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase							:  Union[str, Any]   	=						logging.get_logger(__name__)

lowerCAmelCase							:  List[Any]   	=						{
    """shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json""",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}



class   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Tuple										=     '''dinat'''

			_UpperCAmelCase   :							Union[str, Any]										=     {
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}



			def __init__( self  :     Dict   ,						lowerCAmelCase__  :     Tuple=4   ,						lowerCAmelCase__  :     Optional[int]=3   ,						lowerCAmelCase__  :     int=64   ,						lowerCAmelCase__  :     Union[str, Any]=[3, 4, 6, 5]   ,						lowerCAmelCase__  :     int=[2, 4, 8, 16]   ,						lowerCAmelCase__  :     Optional[Any]=7   ,						lowerCAmelCase__  :     Tuple=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]   ,						lowerCAmelCase__  :     Tuple=3.0   ,						lowerCAmelCase__  :     List[Any]=True   ,						lowerCAmelCase__  :     Dict=0.0   ,						lowerCAmelCase__  :     str=0.0   ,						lowerCAmelCase__  :     List[str]=0.1   ,						lowerCAmelCase__  :     List[Any]="gelu"   ,						lowerCAmelCase__  :     Tuple=0.02   ,						lowerCAmelCase__  :     Any=1E-5   ,						lowerCAmelCase__  :     int=0.0   ,						lowerCAmelCase__  :     Optional[Any]=None   ,						lowerCAmelCase__  :     str=None   ,						**lowerCAmelCase__  :     Tuple   ,						):
									super().__init__(**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       patch_size
									SCREAMING_SNAKE_CASE_: List[str]  		=       num_channels
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       embed_dim
									SCREAMING_SNAKE_CASE_: List[Any]  		=       depths
									SCREAMING_SNAKE_CASE_: str  		=       len(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       num_heads
									SCREAMING_SNAKE_CASE_: List[Any]  		=       kernel_size
									SCREAMING_SNAKE_CASE_: Any  		=       dilations
									SCREAMING_SNAKE_CASE_: Tuple  		=       mlp_ratio
									SCREAMING_SNAKE_CASE_: Any  		=       qkv_bias
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       hidden_dropout_prob
									SCREAMING_SNAKE_CASE_: List[Any]  		=       attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_: List[str]  		=       drop_path_rate
									SCREAMING_SNAKE_CASE_: List[str]  		=       hidden_act
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       layer_norm_eps
									SCREAMING_SNAKE_CASE_: Any  		=       initializer_range
									# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       int(embed_dim * 2 ** (len(lowerCAmelCase__) - 1))
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       layer_scale_init_value
									SCREAMING_SNAKE_CASE_: str  		=       ["stem"] + [F"stage{idx}" for idx in range(1   ,						len(lowerCAmelCase__) + 1)]
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       get_aligned_output_features_output_indices(
									    out_features=lowerCAmelCase__   ,						out_indices=lowerCAmelCase__   ,						stage_names=self.stage_names)









import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase							:  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""",
}

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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: 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 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {}

						SCREAMING_SNAKE_CASE_: Tuple  		=       R".*sequential.(\d+).*"
						SCREAMING_SNAKE_CASE_: Dict  		=       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:
																								SCREAMING_SNAKE_CASE_: Any  		=       key.replace(_UpperCAmelCase				,	_UpperCAmelCase    )

												if re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		# replace sequential layers with list
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )

																		SCREAMING_SNAKE_CASE_: Dict  		=       key.replace(f"sequential.{sequential_layer}."				,	f"layers.{int(_UpperCAmelCase    )//3}.linear."    )
												elif re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		SCREAMING_SNAKE_CASE_: Any  		=       int(re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )    )

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

																		SCREAMING_SNAKE_CASE_: Dict  		=       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
																		SCREAMING_SNAKE_CASE_: Tuple  		=       value
																		SCREAMING_SNAKE_CASE_: List[str]  		=       mixed_qkv.size(0    ) // 3

																		SCREAMING_SNAKE_CASE_: Any  		=       mixed_qkv[:qkv_dim]
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       mixed_qkv[qkv_dim : qkv_dim * 2]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       mixed_qkv[qkv_dim * 2 :]

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

						return model_state_dict







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       init_clap(_UpperCAmelCase				,	enable_fusion=_UpperCAmelCase    )

						clap_model.eval()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       clap_model.state_dict()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_state_dict(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       ClapConfig()
						SCREAMING_SNAKE_CASE_: Tuple  		=       enable_fusion
						SCREAMING_SNAKE_CASE_: Tuple  		=       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__":
						lowerCAmelCase							:  Tuple   	=						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							:  int   	=						parser.parse_args()

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






