

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							int										=     (KDPMaDiscreteScheduler,)
			_UpperCAmelCase   :							Optional[Any]										=     10



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						**lowerCAmelCase__  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       {
									    "num_train_timesteps": 1100,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**lowerCAmelCase__)
									return config



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									for timesteps in [10, 50, 100, 1000]:
															self.check_over_configs(num_train_timesteps=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]   ,						[0.0002, 0.002, 0.02]):
															self.check_over_configs(beta_start=lowerCAmelCase__   ,						beta_end=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									for schedule in ["linear", "scaled_linear"]:
															self.check_over_configs(beta_schedule=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: int  		=       self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_scheduler_config(prediction_type="v_prediction")
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       scheduler_class(**lowerCAmelCase__)

									scheduler.set_timesteps(self.num_inference_steps)

									SCREAMING_SNAKE_CASE_: Any  		=       self.dummy_model()
									SCREAMING_SNAKE_CASE_: int  		=       self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       sample.to(lowerCAmelCase__)

									for i, t in enumerate(scheduler.timesteps):
															SCREAMING_SNAKE_CASE_: Tuple  		=       scheduler.scale_model_input(lowerCAmelCase__   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(lowerCAmelCase__   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       scheduler.step(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Tuple  		=       output.prev_sample

									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.sum(torch.abs(lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.mean(torch.abs(lowerCAmelCase__))

									if torch_device in ["cpu", "mps"]:
															assert abs(result_sum.item() - 4.6934E-07) < 1E-2
															assert abs(result_mean.item() - 6.1112E-10) < 1E-3
									else:
															# CUDA
															assert abs(result_sum.item() - 4.693_4286_5017_0972E-07) < 1E-2
															assert abs(result_mean.item() - 0.0002) < 1E-3



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									if torch_device == "mps":
															return
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_: Any  		=       scheduler_class(**lowerCAmelCase__)

									scheduler.set_timesteps(self.num_inference_steps)

									SCREAMING_SNAKE_CASE_: Dict  		=       self.dummy_model()
									SCREAMING_SNAKE_CASE_: str  		=       self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE_: Any  		=       sample.to(lowerCAmelCase__)

									for i, t in enumerate(scheduler.timesteps):
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       scheduler.scale_model_input(lowerCAmelCase__   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Tuple  		=       model(lowerCAmelCase__   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       scheduler.step(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Tuple  		=       output.prev_sample

									SCREAMING_SNAKE_CASE_: List[Any]  		=       torch.sum(torch.abs(lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_: str  		=       torch.mean(torch.abs(lowerCAmelCase__))

									if torch_device in ["cpu", "mps"]:
															assert abs(result_sum.item() - 20.4125) < 1E-2
															assert abs(result_mean.item() - 0.0266) < 1E-3
									else:
															# CUDA
															assert abs(result_sum.item() - 20.4125) < 1E-2
															assert abs(result_mean.item() - 0.0266) < 1E-3




			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									if torch_device == "mps":
															return
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_: List[Any]  		=       scheduler_class(**lowerCAmelCase__)

									scheduler.set_timesteps(self.num_inference_steps   ,						device=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.dummy_model()
									SCREAMING_SNAKE_CASE_: Dict  		=       self.dummy_sample_deter.to(lowerCAmelCase__) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
															SCREAMING_SNAKE_CASE_: Tuple  		=       scheduler.scale_model_input(lowerCAmelCase__   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model(lowerCAmelCase__   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       scheduler.step(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Any  		=       output.prev_sample

									SCREAMING_SNAKE_CASE_: List[Any]  		=       torch.sum(torch.abs(lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_: Dict  		=       torch.mean(torch.abs(lowerCAmelCase__))

									if str(lowerCAmelCase__).startswith("cpu"):
															# The following sum varies between 148 and 156 on mps. Why?
															assert abs(result_sum.item() - 20.4125) < 1E-2
															assert abs(result_mean.item() - 0.0266) < 1E-3
									else:
															# CUDA
															assert abs(result_sum.item() - 20.4125) < 1E-2
															assert abs(result_mean.item() - 0.0266) < 1E-3









import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase							:  Union[str, 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""",
}

lowerCAmelCase							:  int   	=						AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       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    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {}

						SCREAMING_SNAKE_CASE_: Tuple  		=       R".*sequential.(\d+).*"
						SCREAMING_SNAKE_CASE_: Dict  		=       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:
																								SCREAMING_SNAKE_CASE_: Any  		=       key.replace(_UpperCAmelCase				,	_UpperCAmelCase    )

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

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

																		# Because in CLAP they use `nn.Sequential`...
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       1 if projecton_layer == 0 else 2

																		SCREAMING_SNAKE_CASE_: Dict  		=       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
																		SCREAMING_SNAKE_CASE_: Tuple  		=       value
																		SCREAMING_SNAKE_CASE_: List[str]  		=       mixed_qkv.size(0    ) // 3

																		SCREAMING_SNAKE_CASE_: Any  		=       mixed_qkv[:qkv_dim]
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       mixed_qkv[qkv_dim : qkv_dim * 2]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       mixed_qkv[qkv_dim * 2 :]

																		SCREAMING_SNAKE_CASE_: str  		=       query_layer
																		SCREAMING_SNAKE_CASE_: int  		=       key_layer
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       value_layer
												else:
																		SCREAMING_SNAKE_CASE_: int  		=       value

						return model_state_dict







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       init_clap(_UpperCAmelCase				,	enable_fusion=_UpperCAmelCase    )

						clap_model.eval()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       clap_model.state_dict()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_state_dict(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       ClapConfig()
						SCREAMING_SNAKE_CASE_: Tuple  		=       enable_fusion
						SCREAMING_SNAKE_CASE_: Tuple  		=       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__":
						lowerCAmelCase							:  Tuple   	=						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							:  int   	=						parser.parse_args()

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






