





'''simple docstring'''
from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


a__							:							List[Any] =8
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Union[str, Any]=BITS     ) ->      List[str]:



       """simple docstring"""






       __UpperCamelCase       =  x.device

       __UpperCamelCase       =  (x * 255).int().clamp(0				,  255     )

       __UpperCamelCase       =  2 ** torch.arange(bits - 1				,  -1				,  -1				,  device=SCREAMING_SNAKE_CASE__     )
       __UpperCamelCase       =  rearrange(SCREAMING_SNAKE_CASE__				,  'd -> d 1 1'     )
       __UpperCamelCase       =  rearrange(SCREAMING_SNAKE_CASE__				,  'b c h w -> b c 1 h w'     )

       __UpperCamelCase       =  ((x & mask) != 0).float()
       __UpperCamelCase       =  rearrange(SCREAMING_SNAKE_CASE__				,  'b c d h w -> b (c d) h w'     )
       __UpperCamelCase       =  bits * 2 - 1
       return bits
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Tuple=BITS     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  x.device

       __UpperCamelCase       =  (x > 0).int()
       __UpperCamelCase       =  2 ** torch.arange(bits - 1				,  -1				,  -1				,  device=SCREAMING_SNAKE_CASE__				,  dtype=torch.intaa     )

       __UpperCamelCase       =  rearrange(SCREAMING_SNAKE_CASE__				,  'd -> d 1 1'     )
       __UpperCamelCase       =  rearrange(SCREAMING_SNAKE_CASE__				,  'b (c d) h w -> b c d h w'				,  d=8     )
       __UpperCamelCase       =  reduce(x * mask				,  'b c d h w -> b c h w'				,  'sum'     )
       return (dec / 255).clamp(0.0				,  1.0     )
def 				lowercase__   (   self						:  Union[str, Any]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Optional[Any]				,  __lowercase						:  str = 0.0				,  __lowercase						:  int = True				,  __lowercase						:  str=None				,  __lowercase						:  Union[str, Any] = True				,  ) ->      Union[DDIMSchedulerOutput, Tuple]:



       """simple docstring"""






       if self.num_inference_steps is None:
              raise ValueError(
                  'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'     )

       # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
       # Ideally, read DDIM paper in-detail understanding

       # Notation (<variable name> -> <name in paper>
       # - pred_noise_t -> e_theta(x_t, t)
       # - pred_original_sample -> f_theta(x_t, t) or x_0
       # - std_dev_t -> sigma_t
       # - eta -> η
       # - pred_sample_direction -> "direction pointing to x_t"
       # - pred_prev_sample -> "x_t-1"

       # 1. get previous step value (=t-1)
       __UpperCamelCase       =  timestep - self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       __UpperCamelCase       =  self.alphas_cumprod[timestep]
       __UpperCamelCase       =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

       __UpperCamelCase       =  1 - alpha_prod_t

       # 3. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       __UpperCamelCase       =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

       # 4. Clip "predicted x_0"
       __UpperCamelCase       =  self.bit_scale
       if self.config.clip_sample:
              __UpperCamelCase       =  torch.clamp(SCREAMING_SNAKE_CASE__				,  -scale				,  SCREAMING_SNAKE_CASE__     )

       # 5. compute variance: "sigma_t(η)" -> see formula (16)
       # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
       __UpperCamelCase       =  self._get_variance(SCREAMING_SNAKE_CASE__				,  SCREAMING_SNAKE_CASE__     )
       __UpperCamelCase       =  eta * variance ** 0.5

       if use_clipped_model_output:
              # the model_output is always re-derived from the clipped x_0 in Glide
              __UpperCamelCase       =  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

       # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       __UpperCamelCase       =  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

       # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       __UpperCamelCase       =  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if eta > 0:
              # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
              __UpperCamelCase       =  model_output.device if torch.is_tensor(SCREAMING_SNAKE_CASE__     ) else """cpu"""
              __UpperCamelCase       =  torch.randn(model_output.shape				,  dtype=model_output.dtype				,  generator=SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )
              __UpperCamelCase       =  self._get_variance(SCREAMING_SNAKE_CASE__				,  SCREAMING_SNAKE_CASE__     ) ** 0.5 * eta * noise

              __UpperCamelCase       =  prev_sample + variance

       if not return_dict:
              return (prev_sample,)

       return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__				,  pred_original_sample=SCREAMING_SNAKE_CASE__     )
def 				lowercase__   (   self						:  str				,  __lowercase						:  List[Any]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Dict				,  __lowercase						:  List[str]="epsilon"				,  __lowercase						:  List[str]=None				,  __lowercase						:  Tuple = True				,  ) ->      Union[DDPMSchedulerOutput, Tuple]:



       """simple docstring"""






       __UpperCamelCase       =  timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
              __UpperCamelCase       =  torch.split(SCREAMING_SNAKE_CASE__				,  sample.shape[1]				,  dim=1     )
       else:
              __UpperCamelCase       =  None

       # 1. compute alphas, betas
       __UpperCamelCase       =  self.alphas_cumprod[t]
       __UpperCamelCase       =  self.alphas_cumprod[t - 1] if t > 0 else self.one
       __UpperCamelCase       =  1 - alpha_prod_t
       __UpperCamelCase       =  1 - alpha_prod_t_prev

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if prediction_type == "epsilon":
              __UpperCamelCase       =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif prediction_type == "sample":
              __UpperCamelCase       =  model_output
       else:
              raise ValueError(F'''Unsupported prediction_type {prediction_type}.'''     )

       # 3. Clip "predicted x_0"
       __UpperCamelCase       =  self.bit_scale
       if self.config.clip_sample:
              __UpperCamelCase       =  torch.clamp(SCREAMING_SNAKE_CASE__				,  -scale				,  SCREAMING_SNAKE_CASE__     )

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       __UpperCamelCase       =  (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
       __UpperCamelCase       =  self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       __UpperCamelCase       =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       __UpperCamelCase       =  0
       if t > 0:
              __UpperCamelCase       =  torch.randn(
                  model_output.size()				,  dtype=model_output.dtype				,  layout=model_output.layout				,  generator=SCREAMING_SNAKE_CASE__     ).to(model_output.device     )
              __UpperCamelCase       =  (self._get_variance(SCREAMING_SNAKE_CASE__				,  predicted_variance=SCREAMING_SNAKE_CASE__     ) ** 0.5) * noise

       __UpperCamelCase       =  pred_prev_sample + variance

       if not return_dict:
              return (pred_prev_sample,)

       return DDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__				,  pred_original_sample=SCREAMING_SNAKE_CASE__     )


class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							UNetaDConditionModel							,	__A       :							Union[DDIMScheduler, DDPMScheduler]							,	__A       :							Optional[float] = 1.0							,	):
        super().__init__()
        __UpperCamelCase       =  bit_scale
        __UpperCamelCase       =  (
            ddim_bit_scheduler_step if isinstance(__A							,	__A	) else ddpm_bit_scheduler_step
        )

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






 @torch.no_grad()
 def __call__(       self       :							Any							,	__A       :							Optional[int] = 2_5_6							,	__A       :							Optional[int] = 2_5_6							,	__A       :							Optional[int] = 5_0							,	__A       :							Optional[torch.Generator] = None							,	__A       :							Optional[int] = 1							,	__A       :							Optional[str] = "pil"							,	__A       :							bool = True							,	**__A       :							Tuple							,	):
        __UpperCamelCase       =  torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)							,	generator=__A							,	)
        __UpperCamelCase       =  decimal_to_bits(__A	) * self.bit_scale
        __UpperCamelCase       =  latents.to(self.device	)

        self.scheduler.set_timesteps(__A	)

        for t in self.progress_bar(self.scheduler.timesteps	):
               # predict the noise residual
               __UpperCamelCase       =  self.unet(__A							,	__A	).sample

               # compute the previous noisy sample x_t -> x_t-1
               __UpperCamelCase       =  self.scheduler.step(__A							,	__A							,	__A	).prev_sample

        __UpperCamelCase       =  bits_to_decimal(__A	)

        if output_type == "pil":
               __UpperCamelCase       =  self.numpy_to_pil(__A	)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=__A	)







import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__UpperCAmelCase       :   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",
}

__UpperCAmelCase       :   List[str]				       =							AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False)   ->    Union[str, Any]:
  __snake_case  ,   __snake_case:						int   =						create_model(
      """HTSAT-tiny"""					,					"""roberta"""					,					SCREAMING_SNAKE_CASE__					,					precision="""fp32"""					,					device="""cuda:0""" if torch.cuda.is_available() else """cpu"""					,					enable_fusion=SCREAMING_SNAKE_CASE__					,					fusion_type="""aff_2d""" if enable_fusion else None					,					)
  return model, model_cfg







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Any:
  __snake_case:						Optional[Any]   =						{}

  __snake_case:						int   =						r""".*sequential.(\d+).*"""
  __snake_case:						List[str]   =						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:
        __snake_case:						Tuple   =						key.replace(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)

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

      __snake_case:						str   =						key.replace(F'''sequential.{sequential_layer}.'''					,					F'''layers.{int(SCREAMING_SNAKE_CASE__)//3}.linear.''')
    elif re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__):
      __snake_case:						Any   =						int(re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__).group(1))

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

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

      __snake_case:						Union[str, Any]   =						mixed_qkv[:qkv_dim]
      __snake_case:						Dict   =						mixed_qkv[qkv_dim : qkv_dim * 2]
      __snake_case:						int   =						mixed_qkv[qkv_dim * 2 :]

      __snake_case:						Optional[Any]   =						query_layer
      __snake_case:						str   =						key_layer
      __snake_case:						int   =						value_layer
    else:
      __snake_case:						Dict   =						value

  return model_state_dict







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False)   ->    Optional[Any]:
  __snake_case  ,   __snake_case:						List[str]   =						init_clap(SCREAMING_SNAKE_CASE__					,					enable_fusion=SCREAMING_SNAKE_CASE__)

  clap_model.eval()
  __snake_case:						List[str]   =						clap_model.state_dict()
  __snake_case:						Optional[int]   =						rename_state_dict(SCREAMING_SNAKE_CASE__)

  __snake_case:						Any   =						ClapConfig()
  __snake_case:						Dict   =						enable_fusion
  __snake_case:						List[str]   =						ClapModel(SCREAMING_SNAKE_CASE__)

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

  model.save_pretrained(SCREAMING_SNAKE_CASE__)
  transformers_config.save_pretrained(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      __UpperCAmelCase       :   Union[str, Any]				       =							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")
      __UpperCAmelCase       :   Tuple				       =							parser.parse_args()

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






