






import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


UpperCAmelCase     :     str							=	"""__DUMMY_TRANSFORMERS_USER__"""
UpperCAmelCase     :     int							=	"""Dummy User"""
UpperCAmelCase     :     str							=	"""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"""

UpperCAmelCase     :     Any							=	"""https://hub-ci.huggingface.co"""
UpperCAmelCase     :     List[Any]							=	CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
UpperCAmelCase     :     Any							=	CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
UpperCAmelCase     :     List[Any]							=	Path("""~/.huggingface/hub_ci_token""").expanduser()
@pytest.fixture
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]						):





  """simple docstring"""



  monkeypatch.setattr(
      "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"	,	SCREAMING_SNAKE_CASE						)
@pytest.fixture
def 					_A	(		SCREAMING_SNAKE_CASE					:       Tuple						):





  """simple docstring"""



  monkeypatch.setattr("datasets.config.HF_ENDPOINT"	,	SCREAMING_SNAKE_CASE						)
  monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"	,	SCREAMING_SNAKE_CASE						)
@pytest.fixture
def 					_A	(		SCREAMING_SNAKE_CASE					:       Tuple						):





  """simple docstring"""



  monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"	,	SCREAMING_SNAKE_CASE						)
@pytest.fixture
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  HfFolder.save_token(SCREAMING_SNAKE_CASE						)
  yield
  HfFolder.delete_token()
@pytest.fixture(scope="session"						)
def 					_A	(		):





  """simple docstring"""



  return HfApi(endpoint=SCREAMING_SNAKE_CASE						)
@pytest.fixture(scope="session"						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       HfApi						):





  """simple docstring"""



  a__     : Optional[int]  =HfFolder.get_token()
  HfFolder.save_token(SCREAMING_SNAKE_CASE						)
  yield CI_HUB_USER_TOKEN
  if previous_token is not None:
    HfFolder.save_token(SCREAMING_SNAKE_CASE						)
@pytest.fixture
def 					_A	(		SCREAMING_SNAKE_CASE					:       Any						):





  """simple docstring"""



  def _cleanup_repo(SCREAMING_SNAKE_CASE					:       Optional[Any]						):
    hf_api.delete_repo(SCREAMING_SNAKE_CASE	,	token=SCREAMING_SNAKE_CASE	,	repo_type="dataset"						)

  return _cleanup_repo
@pytest.fixture
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict						):





  """simple docstring"""



  @contextmanager
  def _temporary_repo(SCREAMING_SNAKE_CASE					:       Any						):
    try:
      yield repo_id
    finally:
      cleanup_repo(SCREAMING_SNAKE_CASE						)

  return _temporary_repo
@pytest.fixture(scope="session"						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       HfApi	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       List[str]						):





  """simple docstring"""



  a__     : int  =f'''repo_txt_data-{int(time.time() * 10e3						)}'''
  a__     : Any  =f'''{CI_HUB_USER}/{repo_name}'''
  hf_api.create_repo(SCREAMING_SNAKE_CASE	,	token=SCREAMING_SNAKE_CASE	,	repo_type="dataset"	,	private=SCREAMING_SNAKE_CASE						)
  hf_api.upload_file(
      token=SCREAMING_SNAKE_CASE	,	path_or_fileobj=str(SCREAMING_SNAKE_CASE						)	,	path_in_repo="data/text_data.txt"	,	repo_id=SCREAMING_SNAKE_CASE	,	repo_type="dataset"	,	)
  yield repo_id
  try:
    hf_api.delete_repo(SCREAMING_SNAKE_CASE	,	token=SCREAMING_SNAKE_CASE	,	repo_type="dataset"						)
  except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
    pass
@pytest.fixture()
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]						):





  """simple docstring"""



  return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope="session"						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       HfApi	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       str						):





  """simple docstring"""



  a__     : Optional[int]  =f'''repo_zipped_txt_data-{int(time.time() * 10e3						)}'''
  a__     : List[Any]  =f'''{CI_HUB_USER}/{repo_name}'''
  hf_api.create_repo(SCREAMING_SNAKE_CASE	,	token=SCREAMING_SNAKE_CASE	,	repo_type="dataset"	,	private=SCREAMING_SNAKE_CASE						)
  hf_api.upload_file(
      token=SCREAMING_SNAKE_CASE	,	path_or_fileobj=str(SCREAMING_SNAKE_CASE						)	,	path_in_repo="data.zip"	,	repo_id=SCREAMING_SNAKE_CASE	,	repo_type="dataset"	,	)
  yield repo_id
  try:
    hf_api.delete_repo(SCREAMING_SNAKE_CASE	,	token=SCREAMING_SNAKE_CASE	,	repo_type="dataset"						)
  except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
    pass
@pytest.fixture()
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Tuple						):





  """simple docstring"""



  return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope="session"						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       HfApi	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       Optional[Any]						):





  """simple docstring"""



  a__     : int  =f'''repo_zipped_img_data-{int(time.time() * 10e3						)}'''
  a__     : Optional[int]  =f'''{CI_HUB_USER}/{repo_name}'''
  hf_api.create_repo(SCREAMING_SNAKE_CASE	,	token=SCREAMING_SNAKE_CASE	,	repo_type="dataset"	,	private=SCREAMING_SNAKE_CASE						)
  hf_api.upload_file(
      token=SCREAMING_SNAKE_CASE	,	path_or_fileobj=str(SCREAMING_SNAKE_CASE						)	,	path_in_repo="data.zip"	,	repo_id=SCREAMING_SNAKE_CASE	,	repo_type="dataset"	,	)
  yield repo_id
  try:
    hf_api.delete_repo(SCREAMING_SNAKE_CASE	,	token=SCREAMING_SNAKE_CASE	,	repo_type="dataset"						)
  except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
    pass





@pytest.fixture()
def 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  return hf_private_dataset_repo_zipped_img_data_













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     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : str            =      ["""vqvae"""]







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				)       ->						Union[str, Any]:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




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







      @torch.no_grad()
      def __call__(   self		,				lowerCAmelCase__ = 1		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__=True		,				)       ->						Union[
          Union[AudioPipelineOutput, ImagePipelineOutput],
          Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
      ]:



        '''simple docstring'''




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

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

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

          if start_step > 0:
            a__     : Optional[int]  =self.scheduler.add_noise(lowerCAmelCase__		,				lowerCAmelCase__		,				self.scheduler.timesteps[start_step - 1]							)

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

        for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
          if isinstance(self.unet		,				lowerCAmelCase__							):
            a__     : List[str]  =self.unet(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)["sample"]
          else:
            a__     : Optional[Any]  =self.unet(lowerCAmelCase__		,				lowerCAmelCase__							)["sample"]

          if isinstance(self.scheduler		,				lowerCAmelCase__							):
            a__     : int  =self.scheduler.step(
                model_output=lowerCAmelCase__		,				timestep=lowerCAmelCase__		,				sample=lowerCAmelCase__		,				eta=lowerCAmelCase__		,				generator=lowerCAmelCase__		,				)["prev_sample"]
          else:
            a__     : str  =self.scheduler.step(
                model_output=lowerCAmelCase__		,				timestep=lowerCAmelCase__		,				sample=lowerCAmelCase__		,				generator=lowerCAmelCase__		,				)["prev_sample"]

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

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

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

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

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







      @torch.no_grad()
      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = 5_0							)       ->						np.ndarray:



        '''simple docstring'''




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

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps		,				(0,)							)							):
          a__     : str  =t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
          a__     : Dict  =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__     : Optional[Any]  =1 - alpha_prod_t
          a__     : str  =self.unet(lowerCAmelCase__		,				lowerCAmelCase__							)["sample"]
          a__     : Optional[Any]  =(1 - alpha_prod_t_prev) ** 0.5 * model_output
          a__     : List[str]  =(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
          a__     : Optional[Any]  =sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

        return sample







      @staticmethod
      def        _lowercase  (   lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						torch.Tensor:



        '''simple docstring'''




        a__     : Any  =acos(torch.dot(torch.flatten(lowerCAmelCase__							)		,				torch.flatten(lowerCAmelCase__							)							) / torch.norm(lowerCAmelCase__							) / torch.norm(lowerCAmelCase__							)							)
        return sin((1 - alpha) * theta							) * xa / sin(lowerCAmelCase__							) + sin(alpha * theta							) * xa / sin(lowerCAmelCase__							)






