
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__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Dict     	=				["vqvae"]
   def __init__(self			: Optional[Any] ,    snake_case_			: AutoencoderKL ,    snake_case_			: UNetaDConditionModel ,    snake_case_			: Mel ,    snake_case_			: Union[DDIMScheduler, DDPMScheduler] ,    ):
    super().__init__()
    self.register_modules(unet=snake_case_ ,    scheduler=snake_case_ ,    mel=snake_case_ ,    vqvae=snake_case_       )
   def 				lowerCAmelCase      (self			: Tuple       ):
    return 5_0 if isinstance(self.scheduler ,    snake_case_       ) else 1_0_0_0
   @torch.no_grad()
   def __call__(self			: Optional[int] ,    snake_case_			: int = 1 ,    snake_case_			: str = None ,    snake_case_			: np.ndarray = None ,    snake_case_			: int = 0 ,    snake_case_			: int = 0 ,    snake_case_			: int = None ,    snake_case_			: torch.Generator = None ,    snake_case_			: float = 0 ,    snake_case_			: float = 0 ,    snake_case_			: torch.Generator = None ,    snake_case_			: float = 0 ,    snake_case_			: torch.Tensor = None ,    snake_case_			: torch.Tensor = None ,    snake_case_			: Tuple=True ,    ):
    __a			:  List[str]		    =  steps or self.get_default_steps()
    self.scheduler.set_timesteps(snake_case_       )
    __a			:  List[Any]		    =  step_generator or generator
    # For backwards compatibility
    if type(self.unet.config.sample_size       ) == int:
     __a			:  Dict		    =  (self.unet.config.sample_size, self.unet.config.sample_size)
    if noise is None:
     __a			:  Any		    =  randn_tensor(
         (
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size[0],
             self.unet.config.sample_size[1],
         ) ,    generator=snake_case_ ,    device=self.device ,    )
    __a			:  Tuple		    =  noise
    __a			:  List[Any]		    =  None

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

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

     if start_step > 0:
      __a			:  str		    =  self.scheduler.add_noise(snake_case_ ,    snake_case_ ,    self.scheduler.timesteps[start_step - 1]       )

     __a			:  str		    =  (
         self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
     )
     __a			:  Tuple		    =  int(mask_start_secs * pixels_per_second       )
     __a			:  str		    =  int(mask_end_secs * pixels_per_second       )
     __a			:  Any		    =  self.scheduler.add_noise(snake_case_ ,    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 ,    snake_case_       ):
      __a			:  List[Any]		    =  self.unet(snake_case_ ,    snake_case_ ,    snake_case_       )['''sample''']
     else:
      __a			:  Any		    =  self.unet(snake_case_ ,    snake_case_       )['''sample''']

     if isinstance(self.scheduler ,    snake_case_       ):
      __a			:  Any		    =  self.scheduler.step(
          model_output=snake_case_ ,    timestep=snake_case_ ,    sample=snake_case_ ,    eta=snake_case_ ,    generator=snake_case_ ,    )['''prev_sample''']
     else:
      __a			:  str		    =  self.scheduler.step(
          model_output=snake_case_ ,    timestep=snake_case_ ,    sample=snake_case_ ,    generator=snake_case_ ,    )['''prev_sample''']

     if mask is not None:
      if mask_start > 0:
       __a			:  Union[str, Any]		    =  mask[:, step, :, :mask_start]
      if mask_end > 0:
       __a			:  List[Any]		    =  mask[:, step, :, -mask_end:]

    if self.vqvae is not None:
     # 0.18215 was scaling factor used in training to ensure unit variance
     __a			:  Dict		    =  1 / self.vqvae.config.scaling_factor * images
     __a			:  Tuple		    =  self.vqvae.decode(snake_case_       )['''sample''']

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

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

    return BaseOutput(**AudioPipelineOutput(np.array(snake_case_       )[:, np.newaxis, :]       ) ,    **ImagePipelineOutput(snake_case_       )       )
   @torch.no_grad()
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[Image.Image] ,    snake_case_			: int = 5_0       ):
    assert isinstance(self.scheduler ,    snake_case_       )
    self.scheduler.set_timesteps(snake_case_       )
    __a			:  Dict		    =  np.array(
        [np.frombuffer(image.tobytes() ,    dtype='''uint8'''       ).reshape((1, image.height, image.width)       ) for image in images]       )
    __a			:  str		    =  (sample / 2_5_5) * 2 - 1
    __a			:  int		    =  torch.Tensor(snake_case_       ).to(self.device       )

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

    return sample







   @staticmethod
   def 				lowerCAmelCase      (snake_case_			: torch.Tensor ,    snake_case_			: torch.Tensor ,    snake_case_			: float       ):
    __a			:  Optional[Any]		    =  acos(torch.dot(torch.flatten(snake_case_       ) ,    torch.flatten(snake_case_       )       ) / torch.norm(snake_case_       ) / torch.norm(snake_case_       )       )
    return sin((1 - alpha) * theta       ) * xa / sin(snake_case_       ) + sin(alpha * theta       ) * xa / sin(snake_case_       )


import argparse
import os
import re

import packaging.version


lowercase__          ='examples/'
lowercase__          ={
    'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(R'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), R'\1version="VERSION",'),
    'doc': (re.compile(R'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
lowercase__          ={
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
lowercase__          ='README.md'




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    Tuple  ):
 with open(lowerCAmelCase__  ,       '''r'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  __a			:  Tuple		    =  f.read()
 __a    ,	__a			:  Optional[int]		    =  REPLACE_PATTERNS[pattern]
 __a			:  List[Any]		    =  replace.replace('''VERSION'''  ,       lowerCAmelCase__  )
 __a			:  Any		    =  re_pattern.sub(lowerCAmelCase__  ,       lowerCAmelCase__  )
 with open(lowerCAmelCase__  ,       '''w'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  f.write(lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ):
 for folder, directories, fnames in os.walk(lowerCAmelCase__  ):
  # Removing some of the folders with non-actively maintained examples from the walk
  if "research_projects" in directories:
   directories.remove('''research_projects'''  )
  if "legacy" in directories:
   directories.remove('''legacy'''  )
  for fname in fnames:
   if fname.endswith('''.py'''  ):
    update_version_in_file(os.path.join(lowerCAmelCase__  ,       lowerCAmelCase__  )  ,       lowerCAmelCase__  ,       pattern='''examples'''  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Dict=False  ):
 for pattern, fname in REPLACE_FILES.items():
  update_version_in_file(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
 if not patch:
  update_version_in_examples(lowerCAmelCase__  )




def        __UpperCamelCase					(    ):
 __a			:  Optional[int]		    =  '''🤗 Transformers currently provides the following architectures'''
 __a			:  int		    =  '''1. Want to contribute a new model?'''
 with open(lowerCAmelCase__  ,       '''r'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  __a			:  Tuple		    =  f.readlines()

 # Find the start of the list.
 __a			:  Optional[int]		    =  0
 while not lines[start_index].startswith(_start_prompt  ):
  start_index += 1
 start_index += 1

 __a			:  Any		    =  start_index
 # Update the lines in the model list.
 while not lines[index].startswith(_end_prompt  ):
  if lines[index].startswith('''1.'''  ):
   __a			:  str		    =  lines[index].replace(
       '''https://huggingface.co/docs/transformers/main/model_doc'''  ,       '''https://huggingface.co/docs/transformers/model_doc'''  ,       )
  index += 1

 with open(lowerCAmelCase__  ,       '''w'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  f.writelines(lowerCAmelCase__  )




def        __UpperCamelCase					(    ):
 with open(REPLACE_FILES['''init''']  ,       '''r'''  ) as f:
  __a			:  Optional[int]		    =  f.read()
 __a			:  str		    =  REPLACE_PATTERNS['''init'''][0].search(lowerCAmelCase__  ).groups()[0]
 return packaging.version.parse(lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]=False  ):
 __a			:  str		    =  get_version()
 if patch and default_version.is_devrelease:
  raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''  )
 if default_version.is_devrelease:
  __a			:  Union[str, Any]		    =  default_version.base_version
 elif patch:
  __a			:  Tuple		    =  f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
 else:
  __a			:  List[str]		    =  f"{default_version.major}.{default_version.minor + 1}.0"

 # Now let's ask nicely if that's the right one.
 __a			:  List[str]		    =  input(f"Which version are you releasing? [{default_version}]"  )
 if len(lowerCAmelCase__  ) == 0:
  __a			:  Tuple		    =  default_version

 print(f"Updating version to {version}."  )
 global_version_update(lowerCAmelCase__  ,       patch=lowerCAmelCase__  )
 if not patch:
  print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''  )
  clean_main_ref_in_model_list()




def        __UpperCamelCase					(    ):
 __a			:  Dict		    =  get_version()
 __a			:  str		    =  f"{current_version.major}.{current_version.minor + 1}.0.dev0"
 __a			:  Any		    =  current_version.base_version

 # Check with the user we got that right.
 __a			:  Any		    =  input(f"Which version are we developing now? [{dev_version}]"  )
 if len(lowerCAmelCase__  ) == 0:
  __a			:  Any		    =  dev_version

 print(f"Updating version to {version}."  )
 global_version_update(lowerCAmelCase__  )
 print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''  )
 clean_main_ref_in_model_list()


if __name__ == "__main__":
      lowercase__          =argparse.ArgumentParser()
      parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
      parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
      lowercase__          =parser.parse_args()
      if not args.post_release:
            pre_release_work(patch=args.patch)
      elif args.patch:
            print('Nothing to do after a patch :-)')
      else:
            post_release_work()
