




"""simple docstring"""




import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 			__lowerCamelCase	(   SCREAMING_SNAKE_CASE__   ):




    """simple docstring"""

    def __init__(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					VQModel       ,     SCREAMING_SNAKE_CASE__					:					UNetaDModel       ,     SCREAMING_SNAKE_CASE__					:					DDIMScheduler       )				->	int:
         super().__init__()
         self.register_modules(vqvae=a_       ,     unet=a_       ,     scheduler=a_       )


    @torch.no_grad()
    def __call__(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					int = 1       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,     SCREAMING_SNAKE_CASE__					:					float = 0.0       ,     SCREAMING_SNAKE_CASE__					:					int = 50       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = "pil"       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       ,     )				->	List[Any]:
         lowerCAmelCase__          =			randn_tensor(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,     generator=a_       ,     )
         lowerCAmelCase__          =			latents.to(self.device       )

         # scale the initial noise by the standard deviation required by the scheduler
         lowerCAmelCase__          =			latents * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(a_       )

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         lowerCAmelCase__          =			"eta" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

         lowerCAmelCase__          =			{}
         if accepts_eta:
              lowerCAmelCase__          =			eta

         for t in self.progress_bar(self.scheduler.timesteps       ):
              lowerCAmelCase__          =			self.scheduler.scale_model_input(a_       ,     a_       )
              # predict the noise residual
              lowerCAmelCase__          =			self.unet(a_       ,     a_       ).sample
              # compute the previous noisy sample x_t -> x_t-1
              lowerCAmelCase__          =			self.scheduler.step(a_       ,     a_       ,     a_       ,     **a_       ).prev_sample

         # decode the image latents with the VAE
         lowerCAmelCase__          =			self.vqvae.decode(a_       ).sample

         lowerCAmelCase__          =			(image / 2 + 0.5).clamp(0       ,     1       )
         lowerCAmelCase__          =			image.cpu().permute(0       ,     2       ,     3       ,     1       ).numpy()
         if output_type == "pil":
              lowerCAmelCase__          =			self.numpy_to_pil(a_       )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=a_       )






import time
from contextlib import contextmanager
from pathlib import Path

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


UpperCamelCase								=					'__DUMMY_TRANSFORMERS_USER__'
UpperCamelCase								=					'Dummy User'
UpperCamelCase								=					'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'

UpperCamelCase								=					'https://hub-ci.huggingface.co'
UpperCamelCase								=					CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
UpperCamelCase								=					CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
UpperCamelCase								=					Path('~/.huggingface/hub_ci_token').expanduser()



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     monkeypatch.setattr(
         "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     monkeypatch.setattr("datasets.config.HF_ENDPOINT"       ,    lowerCAmelCase_      )
     monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: str      ):
     """simple docstring"""

     monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: List[Any]      ):
     """simple docstring"""

     HfFolder.save_token(lowerCAmelCase_      )
     yield
     HfFolder.delete_token()



@pytest.fixture(scope="session"      )
def 							_A       (						):
     """simple docstring"""

     return HfApi(endpoint=lowerCAmelCase_      )



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi      ):
     """simple docstring"""

     lowerCAmelCase__          =			HfFolder.get_token()
     HfFolder.save_token(lowerCAmelCase_      )
     yield CI_HUB_USER_TOKEN
     if previous_token is not None:
          HfFolder.save_token(lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

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

     return _cleanup_repo



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     @contextmanager
     def _temporary_repo(lowerCAmelCase_					: str      ):
          try:
               yield repo_id
          finally:
               cleanup_repo(lowerCAmelCase_      )

     return _temporary_repo



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_txt_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data/text_data.txt"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass



@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_zipped_txt_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data.zip"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass



@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_zipped_img_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data.zip"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass






@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     return hf_private_dataset_repo_zipped_img_data_
