

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 A__	( UpperCamelCase_ ):



 """simple docstring"""







 _lowercase     :					Optional[Any]      =						['''vqvae''']

 def __init__(		self :	List[Any]     ,						A_ :	AutoencoderKL     ,						A_ :	UNetaDConditionModel     ,						A_ :	Mel     ,						A_ :	Union[DDIMScheduler, DDPMScheduler]     ,						):






      '''simple docstring'''




      super().__init__()
      self.register_modules(unet=UpperCamelCase__     ,						scheduler=UpperCamelCase__     ,						mel=UpperCamelCase__     ,						vqvae=UpperCamelCase__       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return 5_0 if isinstance(self.scheduler     ,						UpperCamelCase__       ) else 1_0_0_0

 @torch.no_grad()
 def __call__(		self :	str     ,						A_ :	int = 1     ,						A_ :	str = None     ,						A_ :	np.ndarray = None     ,						A_ :	int = 0     ,						A_ :	int = 0     ,						A_ :	int = None     ,						A_ :	torch.Generator = None     ,						A_ :	float = 0     ,						A_ :	float = 0     ,						A_ :	torch.Generator = None     ,						A_ :	float = 0     ,						A_ :	torch.Tensor = None     ,						A_ :	torch.Tensor = None     ,						A_ :	Union[str, Any]=True     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    steps or self.get_default_steps()
      self.scheduler.set_timesteps(UpperCamelCase__       )
      _lowerCAmelCase   :					Optional[Any]        =    step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size       ) == int:
           _lowerCAmelCase   :					int        =    (self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
           _lowerCAmelCase   :					List[str]        =    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     ,						)
      _lowerCAmelCase   :					Optional[Any]        =    noise
      _lowerCAmelCase   :					List[str]        =    None

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

           if self.vqvae is not None:
                _lowerCAmelCase   :					Union[str, Any]        =    self.vqvae.encode(torch.unsqueeze(UpperCamelCase__     ,						0       )       ).latent_dist.sample(
                    generator=UpperCamelCase__       )[0]
                _lowerCAmelCase   :					Any        =    self.vqvae.config.scaling_factor * input_images

           if start_step > 0:
                _lowerCAmelCase   :					Optional[int]        =    self.scheduler.add_noise(UpperCamelCase__     ,						UpperCamelCase__     ,						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   :					List[Any]        =    int(mask_start_secs * pixels_per_second       )
           _lowerCAmelCase   :					Any        =    int(mask_end_secs * pixels_per_second       )
           _lowerCAmelCase   :					Dict        =    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__       ):
                _lowerCAmelCase   :					Tuple        =    self.unet(UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__       )['''sample''']
           else:
                _lowerCAmelCase   :					int        =    self.unet(UpperCamelCase__     ,						UpperCamelCase__       )['''sample''']

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

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

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

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

      _lowerCAmelCase   :					Any        =    [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    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	List[Image.Image]     ,						A_ :	int = 5_0       ):






      '''simple docstring'''




      assert isinstance(self.scheduler     ,						UpperCamelCase__       )
      self.scheduler.set_timesteps(UpperCamelCase__       )
      _lowerCAmelCase   :					List[str]        =    np.array(
          [np.frombuffer(image.tobytes()     ,						dtype="uint8"       ).reshape((1, image.height, image.width)       ) for image in images]       )
      _lowerCAmelCase   :					Any        =    (sample / 2_5_5) * 2 - 1
      _lowerCAmelCase   :					str        =    torch.Tensor(UpperCamelCase__       ).to(self.device       )

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

      return sample


 @staticmethod
 def    __magic_name__       (		A_ :	torch.Tensor     ,						A_ :	torch.Tensor     ,						A_ :	float       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    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__       )









from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[str]      =						''''''
 _lowercase     :					Optional[Any]      =						'''hf-legacy'''  # "hf://"" is reserved for hffs

 def __init__(		self :	Dict     ,						A_ :	Optional[DatasetInfo] = None     ,						A_ :	Optional[str] = None     ,						**A_ :	int     ,						):






      '''simple docstring'''




      super().__init__(self     ,						**A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    repo_info
      _lowerCAmelCase   :					Optional[int]        =    token
      _lowerCAmelCase   :					str        =    None

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      if self.dir_cache is None:
           _lowerCAmelCase   :					int        =    {}
           for hf_file in self.repo_info.siblings:
                # TODO(QL): add sizes
                _lowerCAmelCase   :					Optional[int]        =    {
                    "name": hf_file.rfilename,
                    "size": None,
                    "type": "file",
                }
                self.dir_cache.update(
                    {
                        str(A_       ): {"name": str(A_       ), "size": None, "type": "directory"}
                        for d in list(PurePosixPath(hf_file.rfilename       ).parents       )[:-1]
                    }       )

 def    __magic_name__       (		self :	Dict     ,						A_ :	str     ,						A_ :	str = "rb"     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      if not isinstance(self.repo_info     ,						A_       ):
           raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''       )
      _lowerCAmelCase   :					str        =    hf_hub_url(self.repo_info.id     ,						A_     ,						revision=self.repo_info.sha       )
      return fsspec.open(
          A_     ,						mode=A_     ,						headers=get_authentication_headers_for_url(A_     ,						use_auth_token=self.token       )     ,						client_kwargs={"trust_env": True}     ,						).open()

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	List[Any]     ,						**A_ :	str       ):






      '''simple docstring'''




      self._get_dirs()
      _lowerCAmelCase   :					Optional[int]        =    self._strip_protocol(A_       )
      if path in self.dir_cache:
           return self.dir_cache[path]
      else:
           raise FileNotFoundError(A_       )


 def    __magic_name__       (		self :	Any     ,						A_ :	str     ,						A_ :	Any=False     ,						**A_ :	Dict       ):






      '''simple docstring'''




      self._get_dirs()
      _lowerCAmelCase   :					Dict        =    PurePosixPath(path.strip("/"       )       )
      _lowerCAmelCase   :					str        =    {}
      for p, f in self.dir_cache.items():
           _lowerCAmelCase   :					List[str]        =    PurePosixPath(p.strip("/"       )       )
           _lowerCAmelCase   :					Tuple        =    p.parent
           if root == path:
                _lowerCAmelCase   :					Union[str, Any]        =    f
      _lowerCAmelCase   :					List[str]        =    list(paths.values()       )
      if detail:
           return out
      else:
           return sorted(f["name"] for f in out       )






