




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    snake_case(							__magic_name__					,						__magic_name__=False       )					->				Optional[int]:





     '''simple docstring'''


     lowercase   :  Optional[int]        =					create_model(
         '''HTSAT-tiny'''					,						'''roberta'''					,						_a					,						precision='''fp32'''					,						device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''					,						enable_fusion=_a					,						fusion_type='''aff_2d''' if enable_fusion else None					,						)
     return model, model_cfg






def    snake_case(							__magic_name__       )					->				str:





     '''simple docstring'''


     lowercase   :  int        =					{}

     lowercase   :  Optional[Any]        =					r".*sequential.(\d+).*"
     lowercase   :  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:
                    lowercase   :  Any        =					key.replace(_a					,						_a       )

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

               lowercase   :  Optional[Any]        =					key.replace(F"""sequential.{sequential_layer}."""					,						F"""layers.{int(_a       )//3}.linear."""       )
          elif re.match(_a					,						_a       ):
               lowercase   :  str        =					int(re.match(_a					,						_a       ).group(1       )       )

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

               lowercase   :  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
               lowercase   :  Optional[Any]        =					value
               lowercase   :  Dict        =					mixed_qkv.size(0       ) // 3

               lowercase   :  Dict        =					mixed_qkv[:qkv_dim]
               lowercase   :  Union[str, Any]        =					mixed_qkv[qkv_dim : qkv_dim * 2]
               lowercase   :  str        =					mixed_qkv[qkv_dim * 2 :]

               lowercase   :  Dict        =					query_layer
               lowercase   :  str        =					key_layer
               lowercase   :  List[str]        =					value_layer
          else:
               lowercase   :  Any        =					value

     return model_state_dict






def    snake_case(							__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__=False       )					->				Optional[Any]:





     '''simple docstring'''


     lowercase   :  int        =					init_clap(_a					,						enable_fusion=_a       )

     clap_model.eval()
     lowercase   :  Optional[int]        =					clap_model.state_dict()
     lowercase   :  Tuple        =					rename_state_dict(_a       )

     lowercase   :  Any        =					ClapConfig()
     lowercase   :  Tuple        =					enable_fusion
     lowercase   :  str        =					ClapModel(_a       )

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

     model.save_pretrained(_a       )
     transformers_config.save_pretrained(_a       )


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)


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_      =     {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'VanForImageClassification',
			    'VanModel',
			    'VanPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_van import (
						    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    VanForImageClassification,
						    VanModel,
						    VanPreTrainedModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure)