






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      _lowercase (     __UpperCAmelCase						):
      _a   :     List[Any]								=   ''''''
      _a   :     Optional[Any]								=   '''hf-legacy'''  # "hf://"" is reserved for hffs


      def __init__(    self				:	Tuple							,   a				:	str = None							,   a				:	Any = None							,   **a				:	Dict							,   ):





            """simple docstring"""







            super().__init__(self							,   **a						)
            __snake_case      :    Optional[int]  			=repo_info
            __snake_case      :    Tuple  			=token
            __snake_case      :    str  			=None


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            if self.dir_cache is None:
                  __snake_case      :    Optional[int]  			={}
                  for hf_file in self.repo_info.siblings:
                        # TODO(QL): add sizes
                        __snake_case      :    List[Any]  			={
                            '''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 _UpperCamelCase						(    self				:	List[str]							,   a				:	Tuple							,   a				:	Optional[Any] = "rb"							,   **a				:	str							,   ):





            """simple docstring"""







            if not isinstance(self.repo_info							,   a						):
                  raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}'''						)
            __snake_case      :    Union[str, Any]  			=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 _UpperCamelCase						(    self				:	Tuple							,   a				:	int							,   **a				:	Optional[int]						):





            """simple docstring"""







            self._get_dirs()
            __snake_case      :    int  			=self._strip_protocol(a						)
            if path in self.dir_cache:
                  return self.dir_cache[path]
            else:
                  raise FileNotFoundError(a						)







      def _UpperCamelCase						(    self				:	Dict							,   a				:	Tuple							,   a				:	Any=False							,   **a				:	Any						):





            """simple docstring"""







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










"""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      _lowercase (     lowerCAmelCase						):
      _a   :     Optional[Any]								=   ['''vqvae''']


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





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







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


      @torch.no_grad()
      def __call__(    self				:	Optional[int]							,   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				:	Optional[int]=True							,   ):





            """simple docstring"""







            __snake_case      :    List[Any]  			=steps or self.get_default_steps()
            self.scheduler.set_timesteps(a						)
            __snake_case      :    int  			=step_generator or generator
            # For backwards compatibility
            if type(self.unet.config.sample_size						) == int:
                  __snake_case      :    Any  			=(self.unet.config.sample_size, self.unet.config.sample_size)
            if noise is None:
                  __snake_case      :    List[str]  			=randn_tensor(
                      (
                          batch_size,
                          self.unet.config.in_channels,
                          self.unet.config.sample_size[0],
                          self.unet.config.sample_size[1],
                      )							,   generator=a							,   device=self.device							,   )
            __snake_case      :    int  			=noise
            __snake_case      :    List[str]  			=None

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

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

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

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

            for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
                  if isinstance(self.unet							,   a						):
                        __snake_case      :    List[str]  			=self.unet(a							,   a							,   a						)['''sample''']
                  else:
                        __snake_case      :    Union[str, Any]  			=self.unet(a							,   a						)['''sample''']

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

                  if mask is not None:
                        if mask_start > 0:
                              __snake_case      :    Any  			=mask[:, step, :, :mask_start]
                        if mask_end > 0:
                              __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
                  __snake_case      :    str  			=1 / self.vqvae.config.scaling_factor * images
                  __snake_case      :    Optional[int]  			=self.vqvae.decode(a						)['''sample''']

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

            __snake_case      :    Union[str, Any]  			=[self.mel.image_to_audio(a						) for _ in images]
            if not return_dict:
                  return images, (self.mel.get_sample_rate(), audios)

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


      @torch.no_grad()
      def _UpperCamelCase						(    self				:	int							,   a				:	List[Image.Image]							,   a				:	int = 5_0						):





            """simple docstring"""







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

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

            return sample







      @staticmethod
      def _UpperCamelCase						(    a				:	torch.Tensor							,   a				:	torch.Tensor							,   a				:	float						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=acos(torch.dot(torch.flatten(a						)							,   torch.flatten(a						)						) / torch.norm(a						) / torch.norm(a						)						)
            return sin((1 - alpha) * theta						) * xa / sin(a						) + sin(alpha * theta						) * xa / sin(a						)


