



"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  (UnCLIPScheduler,)



       def   lowercase__       (							self    :						Any	,       **_UpperCAmelCase    :						List[str]					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							{
                 "num_train_timesteps": 1000,
                 "variance_type": "fixed_small_log",
                 "clip_sample": True,
                 "clip_sample_range": 1.0,
                 "prediction_type": "epsilon",
             }

             config.update(**_UpperCAmelCase					)
             return config



       def   lowercase__       (							self    :						Optional[Any]					)				->		Any:


             '''simple docstring'''




             for timesteps in [1, 5, 100, 1000]:
                   self.check_over_configs(num_train_timesteps=_UpperCAmelCase					)



       def   lowercase__       (							self    :						Optional[int]					)				->		List[str]:


             '''simple docstring'''




             for variance in ["fixed_small_log", "learned_range"]:
                   self.check_over_configs(variance_type=_UpperCAmelCase					)



       def   lowercase__       (							self    :						List[str]					)				->		int:


             '''simple docstring'''




             for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=_UpperCAmelCase					)



       def   lowercase__       (							self    :						int					)				->		Optional[int]:


             '''simple docstring'''




             for clip_sample_range in [1, 5, 10, 20]:
                   self.check_over_configs(clip_sample_range=_UpperCAmelCase					)



       def   lowercase__       (							self    :						Dict					)				->		int:


             '''simple docstring'''




             for prediction_type in ["epsilon", "sample"]:
                   self.check_over_configs(prediction_type=_UpperCAmelCase					)



       def   lowercase__       (							self    :						Union[str, Any]					)				->		List[Any]:


             '''simple docstring'''




             for time_step in [0, 500, 999]:
                   for prev_timestep in [None, 5, 100, 250, 500, 750]:
                         if prev_timestep is not None and prev_timestep >= time_step:
                               continue

                         self.check_over_forward(time_step=_UpperCAmelCase	,       prev_timestep=_UpperCAmelCase					)



       def   lowercase__       (							self    :						int					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.scheduler_classes[0]
             UpperCAmelCase_    				=							self.get_scheduler_config(variance_type="fixed_small_log"					)
             UpperCAmelCase_    				=							scheduler_class(**_UpperCAmelCase					)

             assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0000e-10					)					) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.054_9625					)					) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.999_4987					)					) < 1e-5



       def   lowercase__       (							self    :						Optional[Any]					)				->		str:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.scheduler_classes[0]
             UpperCAmelCase_    				=							self.get_scheduler_config(variance_type="learned_range"					)
             UpperCAmelCase_    				=							scheduler_class(**_UpperCAmelCase					)

             UpperCAmelCase_    				=							0.5

             assert scheduler._get_variance(1	,       predicted_variance=_UpperCAmelCase					) - -10.171_2790 < 1e-5
             assert scheduler._get_variance(487	,       predicted_variance=_UpperCAmelCase					) - -5.799_8052 < 1e-5
             assert scheduler._get_variance(999	,       predicted_variance=_UpperCAmelCase					) - -0.001_0011 < 1e-5



       def   lowercase__       (							self    :						List[Any]					)				->		Any:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.scheduler_classes[0]
             UpperCAmelCase_    				=							self.get_scheduler_config()
             UpperCAmelCase_    				=							scheduler_class(**_UpperCAmelCase					)

             UpperCAmelCase_    				=							scheduler.timesteps

             UpperCAmelCase_    				=							self.dummy_model()
             UpperCAmelCase_    				=							self.dummy_sample_deter
             UpperCAmelCase_    				=							torch.manual_seed(0					)

             for i, t in enumerate(_UpperCAmelCase					):
                   # 1. predict noise residual
                   UpperCAmelCase_    				=							model(_UpperCAmelCase	,       _UpperCAmelCase					)

                   # 2. predict previous mean of sample x_t-1
                   UpperCAmelCase_    				=							scheduler.step(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase	,       generator=_UpperCAmelCase					).prev_sample

                   UpperCAmelCase_    				=							pred_prev_sample

             UpperCAmelCase_    				=							torch.sum(torch.abs(_UpperCAmelCase					)					)
             UpperCAmelCase_    				=							torch.mean(torch.abs(_UpperCAmelCase					)					)

             assert abs(result_sum.item() - 252.268_2495					) < 1e-2
             assert abs(result_mean.item() - 0.328_4743					) < 1e-3



       def   lowercase__       (							self    :						int					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.scheduler_classes[0]
             UpperCAmelCase_    				=							self.get_scheduler_config()
             UpperCAmelCase_    				=							scheduler_class(**_UpperCAmelCase					)

             scheduler.set_timesteps(25					)

             UpperCAmelCase_    				=							scheduler.timesteps

             UpperCAmelCase_    				=							self.dummy_model()
             UpperCAmelCase_    				=							self.dummy_sample_deter
             UpperCAmelCase_    				=							torch.manual_seed(0					)

             for i, t in enumerate(_UpperCAmelCase					):
                   # 1. predict noise residual
                   UpperCAmelCase_    				=							model(_UpperCAmelCase	,       _UpperCAmelCase					)

                   if i + 1 == timesteps.shape[0]:
                         UpperCAmelCase_    				=							None
                   else:
                         UpperCAmelCase_    				=							timesteps[i + 1]

                   # 2. predict previous mean of sample x_t-1
                   UpperCAmelCase_    				=							scheduler.step(
                       _UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase	,       prev_timestep=_UpperCAmelCase	,       generator=_UpperCAmelCase					).prev_sample

                   UpperCAmelCase_    				=							pred_prev_sample

             UpperCAmelCase_    				=							torch.sum(torch.abs(_UpperCAmelCase					)					)
             UpperCAmelCase_    				=							torch.mean(torch.abs(_UpperCAmelCase					)					)

             assert abs(result_sum.item() - 258.204_4983					) < 1e-2
             assert abs(result_mean.item() - 0.336_2038					) < 1e-3



       def   lowercase__       (							self    :						List[str]					)				->		str:


             '''simple docstring'''




             pass







       def   lowercase__       (							self    :						int					)				->		Dict:


             '''simple docstring'''




             pass









"""simple docstring"""





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

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

                  UpperCAmelCase_    				=							key.replace(f"""sequential.{sequential_layer}."""			,    f"""layers.{int(lowerCAmelCase__   )//3}.linear."""   )
            elif re.match(lowerCAmelCase__			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							int(re.match(lowerCAmelCase__			,    lowerCAmelCase__   ).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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							init_clap(lowerCAmelCase__			,    enable_fusion=lowerCAmelCase__   )

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

      UpperCAmelCase_    				=							ClapConfig()
      UpperCAmelCase_    				=							enable_fusion
      UpperCAmelCase_    				=							ClapModel(lowerCAmelCase__   )

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

      model.save_pretrained(lowerCAmelCase__   )
      transformers_config.save_pretrained(lowerCAmelCase__   )


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)




