



"""simple docstring"""







from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class 				UpperCamelCase__       (     a__  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					["vqvae"]



      def __init__(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[str]   ,      ):
        super().__init__()
        self.register_modules(unet=SCREAMING_SNAKE_CASE_   ,      scheduler=SCREAMING_SNAKE_CASE_   ,      mel=SCREAMING_SNAKE_CASE_   ,      vqvae=SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        return 5_0 if isinstance(self.scheduler   ,      SCREAMING_SNAKE_CASE_	) else 1_0_0_0



      @torch.no_grad()
      def __call__(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int] = 1   ,      SCREAMING_SNAKE_CASE_    :				str = None   ,      SCREAMING_SNAKE_CASE_    :				List[Any] = None   ,      SCREAMING_SNAKE_CASE_    :				List[Any] = 0   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any] = 0   ,      SCREAMING_SNAKE_CASE_    :				Optional[int] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any] = None   ,      SCREAMING_SNAKE_CASE_    :				Dict = 0   ,      SCREAMING_SNAKE_CASE_    :				int = 0   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any] = None   ,      SCREAMING_SNAKE_CASE_    :				Tuple = 0   ,      SCREAMING_SNAKE_CASE_    :				Tuple = None   ,      SCREAMING_SNAKE_CASE_    :				Tuple = None   ,      SCREAMING_SNAKE_CASE_    :				Tuple=True   ,      ):
        lowerCAmelCase_       :    Any								=			steps or self.get_default_steps()
        self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Optional[int]								=			step_generator or generator
        # For backwards compatibility
        if type(self.unet.config.sample_size	) == int:
          lowerCAmelCase_       :    List[Any]								=			(self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
          lowerCAmelCase_       :    Optional[int]								=			randn_tensor(
              (
                  batch_size,
                  self.unet.config.in_channels,
                  self.unet.config.sample_size[0],
                  self.unet.config.sample_size[1],
              )   ,      generator=SCREAMING_SNAKE_CASE_   ,      device=self.device   ,      )
        lowerCAmelCase_       :    Dict								=			noise
        lowerCAmelCase_       :    Optional[Any]								=			None

        if audio_file is not None or raw_audio is not None:
          self.mel.load_audio(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    Optional[Any]								=			self.mel.audio_slice_to_image(SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    Any								=			np.frombuffer(input_image.tobytes()   ,      dtype='uint8'	).reshape(
              (input_image.height, input_image.width)	)
          lowerCAmelCase_       :    Any								=			(input_image / 2_5_5) * 2 - 1
          lowerCAmelCase_       :    List[str]								=			torch.tensor(input_image[np.newaxis, :, :]   ,      dtype=torch.float	).to(self.device	)

          if self.vqvae is not None:
            lowerCAmelCase_       :    List[str]								=			self.vqvae.encode(torch.unsqueeze(SCREAMING_SNAKE_CASE_   ,      0	)	).latent_dist.sample(
                generator=SCREAMING_SNAKE_CASE_	)[0]
            lowerCAmelCase_       :    int								=			self.vqvae.config.scaling_factor * input_images

          if start_step > 0:
            lowerCAmelCase_       :    int								=			self.scheduler.add_noise(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      self.scheduler.timesteps[start_step - 1]	)

          lowerCAmelCase_       :    Tuple								=			(
              self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
          )
          lowerCAmelCase_       :    Union[str, Any]								=			int(mask_start_secs * pixels_per_second	)
          lowerCAmelCase_       :    Any								=			int(mask_end_secs * pixels_per_second	)
          lowerCAmelCase_       :    Tuple								=			self.scheduler.add_noise(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      torch.tensor(self.scheduler.timesteps[start_step:]	)	)

        for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]	)	):
          if isinstance(self.unet   ,      SCREAMING_SNAKE_CASE_	):
            lowerCAmelCase_       :    List[str]								=			self.unet(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)['sample']
          else:
            lowerCAmelCase_       :    int								=			self.unet(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)['sample']

          if isinstance(self.scheduler   ,      SCREAMING_SNAKE_CASE_	):
            lowerCAmelCase_       :    Dict								=			self.scheduler.step(
                model_output=SCREAMING_SNAKE_CASE_   ,      timestep=SCREAMING_SNAKE_CASE_   ,      sample=SCREAMING_SNAKE_CASE_   ,      eta=SCREAMING_SNAKE_CASE_   ,      generator=SCREAMING_SNAKE_CASE_   ,      )['prev_sample']
          else:
            lowerCAmelCase_       :    str								=			self.scheduler.step(
                model_output=SCREAMING_SNAKE_CASE_   ,      timestep=SCREAMING_SNAKE_CASE_   ,      sample=SCREAMING_SNAKE_CASE_   ,      generator=SCREAMING_SNAKE_CASE_   ,      )['prev_sample']

          if mask is not None:
            if mask_start > 0:
              lowerCAmelCase_       :    str								=			mask[:, step, :, :mask_start]
            if mask_end > 0:
              lowerCAmelCase_       :    Dict								=			mask[:, step, :, -mask_end:]

        if self.vqvae is not None:
          # 0.18215 was scaling factor used in training to ensure unit variance
          lowerCAmelCase_       :    str								=			1 / self.vqvae.config.scaling_factor * images
          lowerCAmelCase_       :    Union[str, Any]								=			self.vqvae.decode(SCREAMING_SNAKE_CASE_	)['sample']

        lowerCAmelCase_       :    int								=			(images / 2 + 0.5).clamp(0   ,      1	)
        lowerCAmelCase_       :    Any								=			images.cpu().permute(0   ,      2   ,      3   ,      1	).numpy()
        lowerCAmelCase_       :    Dict								=			(images * 2_5_5).round().astype('uint8'	)
        lowerCAmelCase_       :    List[str]								=			list(
            (Image.fromarray(_[:, :, 0]	) for _ in images)
            if images.shape[3] == 1
            else (Image.fromarray(SCREAMING_SNAKE_CASE_   ,      mode='RGB'	).convert('L'	) for _ in images)	)

        lowerCAmelCase_       :    Any								=			[self.mel.image_to_audio(SCREAMING_SNAKE_CASE_	) for _ in images]
        if not return_dict:
          return images, (self.mel.get_sample_rate(), audios)

        return BaseOutput(**AudioPipelineOutput(np.array(SCREAMING_SNAKE_CASE_	)[:, np.newaxis, :]	)   ,      **ImagePipelineOutput(SCREAMING_SNAKE_CASE_	)	)



      @torch.no_grad()
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any] = 5_0	):
        assert isinstance(self.scheduler   ,      SCREAMING_SNAKE_CASE_	)
        self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    List[Any]								=			np.array(
            [np.frombuffer(image.tobytes()   ,      dtype='uint8'	).reshape((1, image.height, image.width)	) for image in images]	)
        lowerCAmelCase_       :    str								=			(sample / 2_5_5) * 2 - 1
        lowerCAmelCase_       :    Optional[Any]								=			torch.Tensor(SCREAMING_SNAKE_CASE_	).to(self.device	)

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps   ,      (0,)	)	):
          lowerCAmelCase_       :    str								=			t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
          lowerCAmelCase_       :    Any								=			self.scheduler.alphas_cumprod[t]
          lowerCAmelCase_       :    str								=			(
              self.scheduler.alphas_cumprod[prev_timestep]
              if prev_timestep >= 0
              else self.scheduler.final_alpha_cumprod
          )
          lowerCAmelCase_       :    str								=			1 - alpha_prod_t
          lowerCAmelCase_       :    int								=			self.unet(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)['sample']
          lowerCAmelCase_       :    str								=			(1 - alpha_prod_t_prev) ** 0.5 * model_output
          lowerCAmelCase_       :    List[str]								=			(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
          lowerCAmelCase_       :    int								=			sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

        return sample





      @staticmethod
      def 					SCREAMING_SNAKE_CASE__			(				SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        lowerCAmelCase_       :    List[str]								=			acos(torch.dot(torch.flatten(SCREAMING_SNAKE_CASE_	)   ,      torch.flatten(SCREAMING_SNAKE_CASE_	)	) / torch.norm(SCREAMING_SNAKE_CASE_	) / torch.norm(SCREAMING_SNAKE_CASE_	)	)
        return sin((1 - alpha) * theta	) * xa / sin(SCREAMING_SNAKE_CASE_	) + sin(alpha * theta	) * xa / sin(SCREAMING_SNAKE_CASE_	)










"""simple docstring"""







import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowercase__		:    int				   =     HUGGINGFACE_HUB_CACHE


lowercase__		:    Tuple				   =     """config.json"""
lowercase__		:    Union[str, Any]				   =     """diffusion_pytorch_model.bin"""
lowercase__		:    List[Any]				   =     """diffusion_flax_model.msgpack"""
lowercase__		:    List[str]				   =     """model.onnx"""
lowercase__		:    List[Any]				   =     """diffusion_pytorch_model.safetensors"""
lowercase__		:    Dict				   =     """weights.pb"""
lowercase__		:    List[Any]				   =     """https://huggingface.co"""
lowercase__		:    List[Any]				   =     default_cache_path
lowercase__		:    Tuple				   =     """diffusers_modules"""
lowercase__		:    Tuple				   =     os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowercase__		:    List[str]				   =     ["""fp16""", """non-ema"""]
lowercase__		:    Optional[Any]				   =     """.self_attn"""





