
"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__magic_name__       =			{
    "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",
}

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



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_=False				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = 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 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = {}

		__SCREAMING_SNAKE_CASE            = r""".*sequential.(\d+).*"""
		__SCREAMING_SNAKE_CASE            = 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            = key.replace(UpperCamelCase_  ,				UpperCamelCase_				)

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

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

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

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

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

						__SCREAMING_SNAKE_CASE            = query_layer
						__SCREAMING_SNAKE_CASE            = key_layer
						__SCREAMING_SNAKE_CASE            = value_layer
				else:
						__SCREAMING_SNAKE_CASE            = value

		return model_state_dict



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_=False				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = init_clap(UpperCamelCase_  ,				enable_fusion=UpperCamelCase_				)

		clap_model.eval()
		__SCREAMING_SNAKE_CASE            = clap_model.state_dict()
		__SCREAMING_SNAKE_CASE            = rename_state_dict(UpperCamelCase_				)

		__SCREAMING_SNAKE_CASE            = ClapConfig()
		__SCREAMING_SNAKE_CASE            = enable_fusion
		__SCREAMING_SNAKE_CASE            = 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__":
							__magic_name__       =			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")
							__magic_name__       =			parser.parse_args()

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











"""simple docstring"""



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


a_ =			logging.get_logger(__name__)

a_ =			{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """nat"""

 _lowerCamelCase        =     {
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }

 def __init__(    self					,						__lowerCamelCase=4					,						__lowerCamelCase=3					,						__lowerCamelCase=64					,						__lowerCamelCase=[3, 4, 6, 5]					,						__lowerCamelCase=[2, 4, 8, 16]					,						__lowerCamelCase=7					,						__lowerCamelCase=3.0					,						__lowerCamelCase=True					,						__lowerCamelCase=0.0					,						__lowerCamelCase=0.0					,						__lowerCamelCase=0.1					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-5					,						__lowerCamelCase=0.0					,						__lowerCamelCase=None					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

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




