





import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


_A :   Optional[Any]  			=  logging.get_logger(__name__)

enable_full_determinism()


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  int			   =   UNetaDModel
       _UpperCAmelCase				:  List[Any]			   =   "sample"







       @property
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Any:
          lowerCamelCase__       :						List[Any]        =							4
          lowerCamelCase__       :						List[str]        =							3
          lowerCamelCase__       :						Dict        =							(3_2, 3_2)

          lowerCamelCase__       :						Optional[Any]        =							floats_tensor((batch_size, num_channels) + sizes      ).to(A      )
          lowerCamelCase__       :						List[Any]        =							torch.tensor([1_0]      ).to(A      )

          return {"sample": noise, "timestep": time_step}







       @property
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Optional[int]:
          return (3, 3_2, 3_2)







       @property
       def 						__lowerCamelCase   (				self					:     int      )       ->Dict:
          return (3, 3_2, 3_2)







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Optional[Any]:
          lowerCamelCase__       :						Optional[int]        =							{
              '''block_out_channels''': (3_2, 6_4),
              '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
              '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
              '''attention_head_dim''': 3,
              '''out_channels''': 3,
              '''in_channels''': 3,
              '''layers_per_block''': 2,
              '''sample_size''': 3_2,
          }
          lowerCamelCase__       :						Dict        =							self.dummy_input
          return init_dict, inputs_dict


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[Any]			   =   UNetaDModel
       _UpperCAmelCase				:  Dict			   =   "sample"







       @property
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->int:
          lowerCamelCase__       :						Tuple        =							4
          lowerCamelCase__       :						Dict        =							4
          lowerCamelCase__       :						Dict        =							(3_2, 3_2)

          lowerCamelCase__       :						List[Any]        =							floats_tensor((batch_size, num_channels) + sizes      ).to(A      )
          lowerCamelCase__       :						Optional[int]        =							torch.tensor([1_0]      ).to(A      )

          return {"sample": noise, "timestep": time_step}







       @property
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->int:
          return (4, 3_2, 3_2)







       @property
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Union[str, Any]:
          return (4, 3_2, 3_2)







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Any:
          lowerCamelCase__       :						Tuple        =							{
              '''sample_size''': 3_2,
              '''in_channels''': 4,
              '''out_channels''': 4,
              '''layers_per_block''': 2,
              '''block_out_channels''': (3_2, 6_4),
              '''attention_head_dim''': 3_2,
              '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
              '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
          }
          lowerCamelCase__       :						Tuple        =							self.dummy_input
          return init_dict, inputs_dict







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Any:
          lowerCamelCase__       ,							lowerCamelCase__       :						int        =							UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		, output_loading_info=A      )

          self.assertIsNotNone(A      )
          self.assertEqual(len(loading_info['''missing_keys''']      )		, 0      )

          model.to(A      )
          lowerCamelCase__       :						Optional[int]        =							model(**self.dummy_input      ).sample

          assert image is not None, "Make sure output is not None"







       @unittest.skipIf(torch_device != '''cuda'''		, '''This test is supposed to run on GPU'''      )
       def 						__lowerCamelCase   (				self					:     str      )       ->str:
          lowerCamelCase__       ,							lowerCamelCase__       :						Union[str, Any]        =							UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		, output_loading_info=A      )
          model.to(A      )
          lowerCamelCase__       :						Optional[Any]        =							model(**self.dummy_input      ).sample

          assert image is not None, "Make sure output is not None"







       @unittest.skipIf(torch_device != '''cuda'''		, '''This test is supposed to run on GPU'''      )
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Dict:
          # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
          lowerCamelCase__       ,							lowerCamelCase__       :						Optional[int]        =							UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		, output_loading_info=A      )
          model_accelerate.to(A      )
          model_accelerate.eval()

          lowerCamelCase__       :						Tuple        =							torch.randn(
              1		, model_accelerate.config.in_channels		, model_accelerate.config.sample_size		, model_accelerate.config.sample_size		, generator=torch.manual_seed(0      )		, )
          lowerCamelCase__       :						Any        =							noise.to(A      )
          lowerCamelCase__       :						List[Any]        =							torch.tensor([1_0] * noise.shape[0]      ).to(A      )

          lowerCamelCase__       :						Optional[Any]        =							model_accelerate(A		, A      )['''sample''']

          # two models don't need to stay in the device at the same time
          del model_accelerate
          torch.cuda.empty_cache()
          gc.collect()

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							UNetaDModel.from_pretrained(
              '''fusing/unet-ldm-dummy-update'''		, output_loading_info=A		, low_cpu_mem_usage=A      )
          model_normal_load.to(A      )
          model_normal_load.eval()
          lowerCamelCase__       :						Union[str, Any]        =							model_normal_load(A		, A      )['''sample''']

          assert torch_all_close(A		, A		, rtol=1e-3      )







       def 						__lowerCamelCase   (				self					:     str      )       ->Dict:
          lowerCamelCase__       :						int        =							UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''      )
          model.eval()
          model.to(A      )

          lowerCamelCase__       :						Union[str, Any]        =							torch.randn(
              1		, model.config.in_channels		, model.config.sample_size		, model.config.sample_size		, generator=torch.manual_seed(0      )		, )
          lowerCamelCase__       :						List[Any]        =							noise.to(A      )
          lowerCamelCase__       :						Optional[Any]        =							torch.tensor([1_0] * noise.shape[0]      ).to(A      )

          with torch.no_grad():
             lowerCamelCase__       :						List[str]        =							model(A		, A      ).sample

          lowerCamelCase__       :						str        =							output[0, -1, -3:, -3:].flatten().cpu()
          # fmt: off
          lowerCamelCase__       :						List[str]        =							torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]      )
          # fmt: on

          self.assertTrue(torch_all_close(A		, A		, rtol=1e-3      )      )


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  str			   =   UNetaDModel
       _UpperCAmelCase				:  List[str]			   =   "sample"







       @property
       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Union[str, Any]=(3_2, 3_2)      )       ->Any:
          lowerCamelCase__       :						List[str]        =							4
          lowerCamelCase__       :						Dict        =							3

          lowerCamelCase__       :						Optional[Any]        =							floats_tensor((batch_size, num_channels) + sizes      ).to(A      )
          lowerCamelCase__       :						List[Any]        =							torch.tensor(batch_size * [1_0]      ).to(dtype=torch.intaa		, device=A      )

          return {"sample": noise, "timestep": time_step}







       @property
       def 						__lowerCamelCase   (				self					:     int      )       ->Any:
          return (3, 3_2, 3_2)







       @property
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Tuple:
          return (3, 3_2, 3_2)







       def 						__lowerCamelCase   (				self					:     str      )       ->Optional[Any]:
          lowerCamelCase__       :						Union[str, Any]        =							{
              '''block_out_channels''': [3_2, 6_4, 6_4, 6_4],
              '''in_channels''': 3,
              '''layers_per_block''': 1,
              '''out_channels''': 3,
              '''time_embedding_type''': '''fourier''',
              '''norm_eps''': 1e-6,
              '''mid_block_scale_factor''': math.sqrt(2.0      ),
              '''norm_num_groups''': None,
              '''down_block_types''': [
                  '''SkipDownBlock2D''',
                  '''AttnSkipDownBlock2D''',
                  '''SkipDownBlock2D''',
                  '''SkipDownBlock2D''',
              ],
              '''up_block_types''': [
                  '''SkipUpBlock2D''',
                  '''SkipUpBlock2D''',
                  '''AttnSkipUpBlock2D''',
                  '''SkipUpBlock2D''',
              ],
          }
          lowerCamelCase__       :						Any        =							self.dummy_input
          return init_dict, inputs_dict







       @slow
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->List[Any]:
          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''		, output_loading_info=A      )
          self.assertIsNotNone(A      )
          self.assertEqual(len(loading_info['''missing_keys''']      )		, 0      )

          model.to(A      )
          lowerCamelCase__       :						Optional[Any]        =							self.dummy_input
          lowerCamelCase__       :						Union[str, Any]        =							floats_tensor((4, 3) + (2_5_6, 2_5_6)      ).to(A      )
          lowerCamelCase__       :						str        =							noise
          lowerCamelCase__       :						int        =							model(**A      )

          assert image is not None, "Make sure output is not None"







       @slow
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[str]:
          lowerCamelCase__       :						Optional[int]        =							UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''      )
          model.to(A      )

          lowerCamelCase__       :						Dict        =							4
          lowerCamelCase__       :						Tuple        =							3
          lowerCamelCase__       :						Dict        =							(2_5_6, 2_5_6)

          lowerCamelCase__       :						Optional[Any]        =							torch.ones((batch_size, num_channels) + sizes      ).to(A      )
          lowerCamelCase__       :						Optional[Any]        =							torch.tensor(batch_size * [1e-4]      ).to(A      )

          with torch.no_grad():
             lowerCamelCase__       :						Tuple        =							model(A		, A      ).sample

          lowerCamelCase__       :						str        =							output[0, -3:, -3:, -1].flatten().cpu()
          # fmt: off
          lowerCamelCase__       :						Optional[int]        =							torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]      )
          # fmt: on

          self.assertTrue(torch_all_close(A		, A		, rtol=1e-2      )      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[int]:
          lowerCamelCase__       :						Tuple        =							UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''      )
          model.to(A      )

          lowerCamelCase__       :						List[Any]        =							4
          lowerCamelCase__       :						int        =							3
          lowerCamelCase__       :						Union[str, Any]        =							(3_2, 3_2)

          lowerCamelCase__       :						Union[str, Any]        =							torch.ones((batch_size, num_channels) + sizes      ).to(A      )
          lowerCamelCase__       :						Dict        =							torch.tensor(batch_size * [1e-4]      ).to(A      )

          with torch.no_grad():
             lowerCamelCase__       :						Union[str, Any]        =							model(A		, A      ).sample

          lowerCamelCase__       :						int        =							output[0, -3:, -3:, -1].flatten().cpu()
          # fmt: off
          lowerCamelCase__       :						str        =							torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]      )
          # fmt: on

          self.assertTrue(torch_all_close(A		, A		, rtol=1e-2      )      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Any:
          # not required for this model
          pass










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)



