





'''simple docstring'''






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


A__				:   str      =transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      if isinstance(lowerCAmelCase							,	torch.Tensor  ):
            return image
      elif isinstance(lowerCAmelCase							,	PIL.Image.Image  ):
            _lowerCAmelCase							     =							[image]

      _lowerCAmelCase							     =							[trans(img.convert("""RGB"""  )  ) for img in image]
      _lowerCAmelCase							     =							torch.stack(lowerCAmelCase  )
      return image





class  UpperCAmelCase     (			snake_case_   ):




      def __init__(			self			: Dict			,			__snake_case			: List[str]			,			__snake_case			: Tuple  )				->      Optional[int]:
            super().__init__()

            # make sure scheduler can always be converted to DDIM
            _lowerCAmelCase							     =							DDIMScheduler.from_config(scheduler.config  )

            self.register_modules(unet=__snake_case			,			scheduler=__snake_case  )




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: Union[str, Any]  )				->      str:
            if strength < 0 or strength > 1:
                  raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}"  )




      def 				lowercase__    (			self			: Dict			,			__snake_case			: Optional[Any]			,			__snake_case			: str			,			__snake_case			: Any  )				->      Any:
            # get the original timestep using init_timestep
            _lowerCAmelCase							     =							min(int(num_inference_steps * strength  )			,			__snake_case  )

            _lowerCAmelCase							     =							max(num_inference_steps - init_timestep			,			0  )
            _lowerCAmelCase							     =							self.scheduler.timesteps[t_start:]

            return timesteps, num_inference_steps - t_start




      def 				lowercase__    (			self			: str			,			__snake_case			: Optional[Any]			,			__snake_case			: Any			,			__snake_case			: Any			,			__snake_case			: Optional[Any]			,			__snake_case			: List[str]			,			__snake_case			: Optional[Any]=None  )				->      Tuple:
            if not isinstance(__snake_case			,			(torch.Tensor, PIL.Image.Image, list)  ):
                  raise ValueError(
                      f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case  )}"  )

            _lowerCAmelCase							     =							image.to(device=__snake_case			,			dtype=__snake_case  )

            if isinstance(__snake_case			,			__snake_case  ) and len(__snake_case  ) != batch_size:
                  raise ValueError(
                      f"You have passed a list of generators of length {len(__snake_case  )}, but requested an effective batch"
                      f" size of {batch_size}. Make sure the batch size matches the length of the generators."  )

            _lowerCAmelCase							     =							init_latents.shape
            _lowerCAmelCase							     =							randn_tensor(__snake_case			,			generator=__snake_case			,			device=__snake_case			,			dtype=__snake_case  )

            # get latents
            print("""add noise to latents at timestep"""			,			__snake_case  )
            _lowerCAmelCase							     =							self.scheduler.add_noise(__snake_case			,			__snake_case			,			__snake_case  )
            _lowerCAmelCase							     =							init_latents

            return latents




      @torch.no_grad()
      def __call__(			self			: Optional[Any]			,			__snake_case			: Union[torch.FloatTensor, PIL.Image.Image] = None			,			__snake_case			: float = 0.8			,			__snake_case			: int = 1			,			__snake_case			: Optional[Union[torch.Generator, List[torch.Generator]]] = None			,			__snake_case			: float = 0.0			,			__snake_case			: int = 50			,			__snake_case			: Optional[bool] = None			,			__snake_case			: Optional[str] = "pil"			,			__snake_case			: bool = True			,			)				->      Union[ImagePipelineOutput, Tuple]:
            self.check_inputs(__snake_case  )

            # 2. Preprocess image
            _lowerCAmelCase							     =							preprocess(__snake_case  )

            # 3. set timesteps
            self.scheduler.set_timesteps(__snake_case			,			device=self.device  )
            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.get_timesteps(__snake_case			,			__snake_case			,			self.device  )
            _lowerCAmelCase							     =							timesteps[:1].repeat(__snake_case  )

            # 4. Prepare latent variables
            _lowerCAmelCase							     =							self.prepare_latents(__snake_case			,			__snake_case			,			__snake_case			,			self.unet.dtype			,			self.device			,			__snake_case  )
            _lowerCAmelCase							     =							latents

            # 5. Denoising loop
            for t in self.progress_bar(__snake_case  ):
                  # 1. predict noise model_output
                  _lowerCAmelCase							     =							self.unet(__snake_case			,			__snake_case  ).sample

                  # 2. predict previous mean of image x_t-1 and add variance depending on eta
                  # eta corresponds to η in paper and should be between [0, 1]
                  # do x_t -> x_t-1
                  _lowerCAmelCase							     =							self.scheduler.step(
                      __snake_case			,			__snake_case			,			__snake_case			,			eta=__snake_case			,			use_clipped_model_output=__snake_case			,			generator=__snake_case			,			).prev_sample

            _lowerCAmelCase							     =							(image / 2 + 0.5).clamp(0			,			1  )
            _lowerCAmelCase							     =							image.cpu().permute(0			,			2			,			3			,			1  ).numpy()
            if output_type == "pil":
                  _lowerCAmelCase							     =							self.numpy_to_pil(__snake_case  )

            if not return_dict:
                  return (image, latent_timestep.item())

            return ImagePipelineOutput(images=__snake_case  )









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)






