





import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch


@require_torch
class        __SCREAMING_SNAKE_CASE      :







							@property
							def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Any:
										return self.get_dummy_input()







							@property
							def 						__lowerCamelCase   (				self					:     Tuple      )       ->str:
										if self.block_type == "down":
													return (4, 3_2, 1_6, 1_6)
										elif self.block_type == "mid":
													return (4, 3_2, 3_2, 3_2)
										elif self.block_type == "up":
													return (4, 3_2, 6_4, 6_4)

										raise ValueError(F"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."      )







							def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Tuple=True		, A					:     Optional[int]=False		, A					:     int=False		, A					:     Optional[Any]=False		, )       ->Any:
										lowerCamelCase__       :						Dict        =							4
										lowerCamelCase__       :						str        =							3_2
										lowerCamelCase__       :						Optional[int]        =							(3_2, 3_2)

										lowerCamelCase__       :						Optional[int]        =							torch.manual_seed(0      )
										lowerCamelCase__       :						List[str]        =							torch.device(A      )
										lowerCamelCase__       :						int        =							(batch_size, num_channels) + sizes
										lowerCamelCase__       :						Dict        =							randn_tensor(A		, generator=A		, device=A      )
										lowerCamelCase__       :						str        =							{'''hidden_states''': hidden_states}

										if include_temb:
													lowerCamelCase__       :						Dict        =							1_2_8
													lowerCamelCase__       :						Union[str, Any]        =							randn_tensor((batch_size, temb_channels)		, generator=A		, device=A      )

										if include_res_hidden_states_tuple:
													lowerCamelCase__       :						str        =							torch.manual_seed(1      )
													lowerCamelCase__       :						List[Any]        =							(randn_tensor(A		, generator=A		, device=A      ),)

										if include_encoder_hidden_states:
													lowerCamelCase__       :						str        =							floats_tensor((batch_size, 3_2, 3_2)      ).to(A      )

										if include_skip_sample:
													lowerCamelCase__       :						Optional[Any]        =							randn_tensor(((batch_size, 3) + sizes)		, generator=A		, device=A      )

										return dummy_input







							def 						__lowerCamelCase   (				self					:     List[str]      )       ->str:
										lowerCamelCase__       :						Union[str, Any]        =							{
										    '''in_channels''': 3_2,
										    '''out_channels''': 3_2,
										    '''temb_channels''': 1_2_8,
										}
										if self.block_type == "up":
													lowerCamelCase__       :						Any        =							3_2

										if self.block_type == "mid":
													init_dict.pop('''out_channels'''      )

										lowerCamelCase__       :						Dict        =							self.dummy_input
										return init_dict, inputs_dict







							def 						__lowerCamelCase   (				self					:     Any		, A					:     List[str]      )       ->List[Any]:
										lowerCamelCase__       :						Optional[int]        =							self.prepare_init_args_and_inputs_for_common()
										lowerCamelCase__       :						Tuple        =							self.block_class(**A      )
										unet_block.to(A      )
										unet_block.eval()

										with torch.no_grad():
													lowerCamelCase__       :						Optional[int]        =							unet_block(**A      )

										if isinstance(A		, A      ):
													lowerCamelCase__       :						Dict        =							output[0]

										self.assertEqual(output.shape		, self.output_shape      )

										lowerCamelCase__       :						List[Any]        =							output[0, -1, -3:, -3:]
										lowerCamelCase__       :						List[str]        =							torch.tensor(A      ).to(A      )
										assert torch_all_close(output_slice.flatten()		, A		, atol=5e-3      )







							@unittest.skipIf(torch_device == '''mps'''		, '''Training is not supported in mps'''      )
							def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[int]:
										lowerCamelCase__       :						Union[str, Any]        =							self.prepare_init_args_and_inputs_for_common()
										lowerCamelCase__       :						List[Any]        =							self.block_class(**A      )
										model.to(A      )
										model.train()
										lowerCamelCase__       :						str        =							model(**A      )

										if isinstance(A		, A      ):
													lowerCamelCase__       :						Any        =							output[0]

										lowerCamelCase__       :						Any        =							torch.device(A      )
										lowerCamelCase__       :						Tuple        =							randn_tensor(output.shape		, device=A      )
										lowerCamelCase__       :						Any        =							torch.nn.functional.mse_loss(A		, A      )
										loss.backward()










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_A :   Optional[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',
}

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



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							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					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							{}

   lowerCamelCase__       :						int        =							R'''.*sequential.(\d+).*'''
   lowerCamelCase__       :						Any        =							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__       :						List[str]        =							key.replace(UpperCAmelCase      ,  UpperCAmelCase					)

      if re.match(UpperCAmelCase      ,  UpperCAmelCase					):
         # replace sequential layers with list
         lowerCamelCase__       :						List[Any]        =							re.match(UpperCAmelCase      ,  UpperCAmelCase					).group(1					)

         lowerCamelCase__       :						Optional[int]        =							key.replace(f"sequential.{sequential_layer}."      ,  f"layers.{int(UpperCAmelCase					)//3}.linear."					)
      elif re.match(UpperCAmelCase      ,  UpperCAmelCase					):
         lowerCamelCase__       :						Optional[int]        =							int(re.match(UpperCAmelCase      ,  UpperCAmelCase					).group(1					)					)

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

         lowerCamelCase__       :						List[str]        =							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__       :						Optional[Any]        =							value
         lowerCamelCase__       :						Optional[Any]        =							mixed_qkv.size(0					) // 3

         lowerCamelCase__       :						Tuple        =							mixed_qkv[:qkv_dim]
         lowerCamelCase__       :						Dict        =							mixed_qkv[qkv_dim : qkv_dim * 2]
         lowerCamelCase__       :						int        =							mixed_qkv[qkv_dim * 2 :]

         lowerCamelCase__       :						Optional[int]        =							query_layer
         lowerCamelCase__       :						str        =							key_layer
         lowerCamelCase__       :						List[str]        =							value_layer
      else:
         lowerCamelCase__       :						Tuple        =							value

   return model_state_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    List[Any]:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							init_clap(UpperCAmelCase      ,  enable_fusion=UpperCAmelCase					)

   clap_model.eval()
   lowerCamelCase__       :						List[Any]        =							clap_model.state_dict()
   lowerCamelCase__       :						Dict        =							rename_state_dict(UpperCAmelCase					)

   lowerCamelCase__       :						Optional[int]        =							ClapConfig()
   lowerCamelCase__       :						Optional[int]        =							enable_fusion
   lowerCamelCase__       :						Optional[Any]        =							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__":
       _A :   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')
       _A :   Tuple  			=  parser.parse_args()

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



