'''simple docstring'''





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


SCREAMING_SNAKE_CASE_:					Optional[int]  ={
    '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',
}

SCREAMING_SNAKE_CASE_:					Dict  =AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')





def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			Optional[Any]=False      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_   ,			UpperCAmelCase_	   =       create_model(
         "HTSAT-tiny" ,   "roberta" ,   __snake_case ,   precision="fp32" ,   device="cuda:0" if torch.cuda.is_available() else "cpu" ,   enable_fusion=__snake_case ,   fusion_type="aff_2d" if enable_fusion else None ,   )
     return model, model_cfg





def      lowerCAmelCase_    ( snake_case_	:			Tuple      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       {}

     UpperCAmelCase_	   =       R".*sequential.(\d+).*"
     UpperCAmelCase_	   =       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_	   =       key.replace(__snake_case ,   __snake_case      )

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

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

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

               UpperCAmelCase_	   =       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_	   =       value
               UpperCAmelCase_	   =       mixed_qkv.size(0      ) // 3

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

               UpperCAmelCase_	   =       query_layer
               UpperCAmelCase_	   =       key_layer
               UpperCAmelCase_	   =       value_layer
          else:
               UpperCAmelCase_	   =       value

     return model_state_dict





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			int ,   snake_case_	:			Union[str, Any]=False      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_   ,			UpperCAmelCase_	   =       init_clap(__snake_case ,   enable_fusion=__snake_case      )

     clap_model.eval()
     UpperCAmelCase_	   =       clap_model.state_dict()
     UpperCAmelCase_	   =       rename_state_dict(__snake_case      )

     UpperCAmelCase_	   =       ClapConfig()
     UpperCAmelCase_	   =       enable_fusion
     UpperCAmelCase_	   =       ClapModel(__snake_case      )

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

     model.save_pretrained(__snake_case      )
     transformers_config.save_pretrained(__snake_case      )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Optional[int]  =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')
    SCREAMING_SNAKE_CASE_:					Optional[Any]  =parser.parse_args()

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





'''simple docstring'''





import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
    import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin




@require_torch
class 	__A				(			unittest.TestCase		,     UpperCamelCase__     ):



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       load_tool("text-to-speech"			)
         self.tool.setup()



    def  _lowercase						(self   :   Union[str, Any]			):
         # SpeechT5 isn't deterministic
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       self.tool("hey"			)
         UpperCAmelCase_	   =       result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]       ,      torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]			)       ,      )			)






    def  _lowercase						(self   :   List[str]			):
         # SpeechT5 isn't deterministic
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       self.tool("hey"			)
         UpperCAmelCase_	   =       result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]       ,      torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]			)       ,      )			)




