



import argparse
from collections import defaultdict

import yaml


_lowerCamelCase						    =  """docs/source/en/_toctree.yml"""






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[Any]     )     ->   Optional[Any]:



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	defaultdict(SCREAMING_SNAKE_CASE_     )
 for doc in model_doc:
  counts[doc["local"]] += 1
 __SCREAMING_SNAKE_CASE       :    str       =	[key for key, value in counts.items() if value > 1]

 __SCREAMING_SNAKE_CASE       :    Any       =	[]
 for duplicate_key in duplicates:
  __SCREAMING_SNAKE_CASE       :    Any       =	list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}     )
  if len(SCREAMING_SNAKE_CASE_     ) > 1:
   raise ValueError(
       F"""{duplicate_key} is present several times in the documentation table of content at """
       "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
       "others."     )
  # Only add this once
  new_doc.append({"local": duplicate_key, "title": titles[0]}     )

 # Add none duplicate-keys
 new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]     )

 # Sort
 return sorted(SCREAMING_SNAKE_CASE_		,	key=lambda __lowerCamelCase     : s["title"].lower()     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str=False     )     ->   Tuple:



 """simple docstring"""





 with open(SCREAMING_SNAKE_CASE_		,	encoding="utf-8"     ) as f:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	yaml.safe_load(f.read()     )

 # Get to the API doc
 __SCREAMING_SNAKE_CASE       :    Any       =	0
 while content[api_idx]["title"] != "API":
  api_idx += 1
 __SCREAMING_SNAKE_CASE       :    Dict       =	content[api_idx]["sections"]

 # Then to the model doc
 __SCREAMING_SNAKE_CASE       :    int       =	0
 while api_doc[model_idx]["title"] != "Models":
  model_idx += 1

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	api_doc[model_idx]["sections"]

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	[(idx, section) for idx, section in enumerate(SCREAMING_SNAKE_CASE_     ) if "sections" in section]
 __SCREAMING_SNAKE_CASE       :    Tuple       =	False
 for idx, modality_doc in modalities_docs:
  __SCREAMING_SNAKE_CASE       :    str       =	modality_doc["sections"]
  __SCREAMING_SNAKE_CASE       :    int       =	clean_model_doc_toc(SCREAMING_SNAKE_CASE_     )

  if old_modality_doc != new_modality_doc:
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	True
   if overwrite:
    __SCREAMING_SNAKE_CASE       :    Optional[int]       =	new_modality_doc

 if diff:
  if overwrite:
   __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model_doc
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	api_doc
   with open(SCREAMING_SNAKE_CASE_		,	"w"		,	encoding="utf-8"     ) as f:
    f.write(yaml.dump(SCREAMING_SNAKE_CASE_		,	allow_unicode=SCREAMING_SNAKE_CASE_     )     )
  else:
   raise ValueError(
       "The model doc part of the table of content is not properly sorted, run `make style` to fix this."     )


if __name__ == "__main__":
      _lowerCamelCase						    =  argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      _lowerCamelCase						    =  parser.parse_args()

      check_model_doc(args.fix_and_overwrite)











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       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			["""vqvae"""]





     def __init__(       self :							Tuple     ,  UpperCamelCase :							AutoencoderKL     ,  UpperCamelCase :							UNetaDConditionModel     ,  UpperCamelCase :							Mel     ,  UpperCamelCase :							Union[DDIMScheduler, DDPMScheduler]     ,  )->Tuple:
      super().__init__()
      self.register_modules(unet=UpperCamelCase     ,  scheduler=UpperCamelCase     ,  mel=UpperCamelCase     ,  vqvae=UpperCamelCase				)





     def 	__snake_case    (       self :							List[Any]				)->int:
      return 5_0 if isinstance(self.scheduler     ,  UpperCamelCase				) else 1_0_0_0





     @torch.no_grad()
     def __call__(       self :							Dict     ,  UpperCamelCase :							int = 1     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							np.ndarray = None     ,  UpperCamelCase :							int = 0     ,  UpperCamelCase :							int = 0     ,  UpperCamelCase :							int = None     ,  UpperCamelCase :							torch.Generator = None     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							torch.Generator = None     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							torch.Tensor = None     ,  UpperCamelCase :							torch.Tensor = None     ,  UpperCamelCase :							Any=True     ,  )->Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	steps or self.get_default_steps()
      self.scheduler.set_timesteps(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size				) == int:
       __SCREAMING_SNAKE_CASE       :    Dict       =	(self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
       __SCREAMING_SNAKE_CASE       :    Any       =	randn_tensor(
           (
               batch_size,
               self.unet.config.in_channels,
               self.unet.config.sample_size[0],
               self.unet.config.sample_size[1],
           )     ,  generator=UpperCamelCase     ,  device=self.device     ,  )
      __SCREAMING_SNAKE_CASE       :    Any       =	noise
      __SCREAMING_SNAKE_CASE       :    Any       =	None

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

       if self.vqvae is not None:
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.vqvae.encode(torch.unsqueeze(UpperCamelCase     ,  0				)				).latent_dist.sample(
            generator=UpperCamelCase				)[0]
        __SCREAMING_SNAKE_CASE       :    int       =	self.vqvae.config.scaling_factor * input_images

       if start_step > 0:
        __SCREAMING_SNAKE_CASE       :    List[str]       =	self.scheduler.add_noise(UpperCamelCase     ,  UpperCamelCase     ,  self.scheduler.timesteps[start_step - 1]				)

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

      for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
       if isinstance(self.unet     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    str       =	self.unet(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)["sample"]
       else:
        __SCREAMING_SNAKE_CASE       :    int       =	self.unet(UpperCamelCase     ,  UpperCamelCase				)["sample"]

       if isinstance(self.scheduler     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    int       =	self.scheduler.step(
            model_output=UpperCamelCase     ,  timestep=UpperCamelCase     ,  sample=UpperCamelCase     ,  eta=UpperCamelCase     ,  generator=UpperCamelCase     ,  )["prev_sample"]
       else:
        __SCREAMING_SNAKE_CASE       :    Tuple       =	self.scheduler.step(
            model_output=UpperCamelCase     ,  timestep=UpperCamelCase     ,  sample=UpperCamelCase     ,  generator=UpperCamelCase     ,  )["prev_sample"]

       if mask is not None:
        if mask_start > 0:
         __SCREAMING_SNAKE_CASE       :    int       =	mask[:, step, :, :mask_start]
        if mask_end > 0:
         __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	mask[:, step, :, -mask_end:]

      if self.vqvae is not None:
       # 0.18215 was scaling factor used in training to ensure unit variance
       __SCREAMING_SNAKE_CASE       :    Any       =	1 / self.vqvae.config.scaling_factor * images
       __SCREAMING_SNAKE_CASE       :    Any       =	self.vqvae.decode(UpperCamelCase				)["sample"]

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(images / 2 + 0.5).clamp(0     ,  1				)
      __SCREAMING_SNAKE_CASE       :    str       =	images.cpu().permute(0     ,  2     ,  3     ,  1				).numpy()
      __SCREAMING_SNAKE_CASE       :    Tuple       =	(images * 2_5_5).round().astype("uint8"				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	list(
          (Image.fromarray(_[:, :, 0]				) for _ in images)
          if images.shape[3] == 1
          else (Image.fromarray(UpperCamelCase     ,  mode="RGB"				).convert("L"				) for _ in images)				)

      __SCREAMING_SNAKE_CASE       :    List[str]       =	[self.mel.image_to_audio(UpperCamelCase				) for _ in images]
      if not return_dict:
       return images, (self.mel.get_sample_rate(), audios)

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





     @torch.no_grad()
     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Image.Image]     ,  UpperCamelCase :							int = 5_0				)->np.ndarray:
      assert isinstance(self.scheduler     ,  UpperCamelCase				)
      self.scheduler.set_timesteps(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	np.array(
          [np.frombuffer(image.tobytes()     ,  dtype="uint8"				).reshape((1, image.height, image.width)				) for image in images]				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	(sample / 2_5_5) * 2 - 1
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.Tensor(UpperCamelCase				).to(self.device				)

      for t in self.progress_bar(torch.flip(self.scheduler.timesteps     ,  (0,)				)				):
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.scheduler.alphas_cumprod[t]
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	(
           self.scheduler.alphas_cumprod[prev_timestep]
           if prev_timestep >= 0
           else self.scheduler.final_alpha_cumprod
       )
       __SCREAMING_SNAKE_CASE       :    Dict       =	1 - alpha_prod_t
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.unet(UpperCamelCase     ,  UpperCamelCase				)["sample"]
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(1 - alpha_prod_t_prev) ** 0.5 * model_output
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

      return sample






     @staticmethod
     def 	__snake_case    (       UpperCamelCase :							torch.Tensor     ,  UpperCamelCase :							torch.Tensor     ,  UpperCamelCase :							float				)->torch.Tensor:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	acos(torch.dot(torch.flatten(UpperCamelCase				)     ,  torch.flatten(UpperCamelCase				)				) / torch.norm(UpperCamelCase				) / torch.norm(UpperCamelCase				)				)
      return sin((1 - alpha) * theta				) * xa / sin(UpperCamelCase				) + sin(alpha * theta				) * xa / sin(UpperCamelCase				)






