





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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






    """simple docstring"""


    snake_case_      ,							snake_case_						:   int           =  image.size
    snake_case_      ,							snake_case_						:   List[Any]           =  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    snake_case_						:   List[str]           =  image.resize((w, h)			,    resample=PIL_INTERPOLATION['''lanczos''']			)
    snake_case_						:   Optional[int]           =  np.array(UpperCamelCase__			).astype(np.floataa			) / 255.0
    snake_case_						:   Optional[Any]           =  image[None].transpose(0			,    3			,    1			,    2			)
    snake_case_						:   Optional[int]           =  torch.from_numpy(UpperCamelCase__			)
    return 2.0 * image - 1.0





class  __lowerCAmelCase	(      _a					):






   def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       int:


       '''simple docstring'''



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






   @torch.no_grad()
   def __call__(self ,							__magic_name__ = None ,							__magic_name__ = 1 ,							__magic_name__ = 100 ,							__magic_name__ = 0.0 ,							__magic_name__ = None ,							__magic_name__ = "pil" ,							__magic_name__ = True ,							)					->       str:


       '''simple docstring'''



       if isinstance(_a ,							PIL.Image.Image   ):
           snake_case_						:   Any           =  1
       elif isinstance(_a ,							torch.Tensor   ):
           snake_case_						:   Any           =  image.shape[0]
       else:
           raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_a   )}'''   )

       if isinstance(_a ,							PIL.Image.Image   ):
           snake_case_						:   int           =  preprocess(_a   )

       snake_case_      ,							snake_case_						:   int           =  image.shape[-2:]

       # in_channels should be 6: 3 for latents, 3 for low resolution image
       snake_case_						:   Optional[Any]           =  (batch_size, self.unet.config.in_channels // 2, height, width)
       snake_case_						:   str           =  next(self.unet.parameters()   ).dtype

       snake_case_						:   Tuple           =  randn_tensor(_a ,							generator=_a ,							device=self.device ,							dtype=_a   )

       snake_case_						:   List[str]           =  image.to(device=self.device ,							dtype=_a   )

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(_a ,							device=self.device   )
       snake_case_						:   Union[str, Any]           =  self.scheduler.timesteps

       # scale the initial noise by the standard deviation required by the scheduler
       snake_case_						:   Union[str, Any]           =  latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       snake_case_						:   Dict           =  '''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
       snake_case_						:   Dict           =  {}
       if accepts_eta:
           snake_case_						:   List[Any]           =  eta

       for t in self.progress_bar(_a   ):
           # concat latents and low resolution image in the channel dimension.
           snake_case_						:   List[Any]           =  torch.cat([latents, image] ,							dim=1   )
           snake_case_						:   Any           =  self.scheduler.scale_model_input(_a ,							_a   )
           # predict the noise residual
           snake_case_						:   Optional[Any]           =  self.unet(_a ,							_a   ).sample
           # compute the previous noisy sample x_t -> x_t-1
           snake_case_						:   Tuple           =  self.scheduler.step(_a ,							_a ,							_a ,							**_a   ).prev_sample

       # decode the image latents with the VQVAE
       snake_case_						:   Dict           =  self.vqvae.decode(_a   ).sample
       snake_case_						:   List[Any]           =  torch.clamp(_a ,							-1.0 ,							1.0   )
       snake_case_						:   List[Any]           =  image / 2 + 0.5
       snake_case_						:   int           =  image.cpu().permute(0 ,							2 ,							3 ,							1   ).numpy()

       if output_type == "pil":
           snake_case_						:   List[Any]           =  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


lowerCAmelCase_    =    '''__DUMMY_TRANSFORMERS_USER__'''
lowerCAmelCase_    =    '''Dummy User'''
lowerCAmelCase_    =    '''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'''

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






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				monkeypatch.setattr(
				    '''huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'''			,    _UpperCamelCase			)






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				monkeypatch.setattr('''datasets.config.HF_ENDPOINT'''			,    _UpperCamelCase			)
				monkeypatch.setattr('''datasets.config.HUB_DATASETS_URL'''			,    _UpperCamelCase			)






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				monkeypatch.setattr('''huggingface_hub.hf_api.HfFolder.path_token'''			,    _UpperCamelCase			)






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				HfFolder.save_token(_UpperCamelCase			)
				yield
				HfFolder.delete_token()






@pytest.fixture(scope='''session'''			)
def 					lowerCamelCase_						(    )		->						List[str]:






				"""simple docstring"""


				return HfApi(endpoint=_UpperCamelCase			)






@pytest.fixture(scope='''session'''			)
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  HfFolder.get_token()
				HfFolder.save_token(_UpperCamelCase			)
				yield CI_HUB_USER_TOKEN
				if previous_token is not None:
								HfFolder.save_token(_UpperCamelCase			)






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				def _cleanup_repo(_UpperCamelCase			):
								hf_api.delete_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			)

				return _cleanup_repo






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


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

				return _temporary_repo






@pytest.fixture(scope='''session'''			)
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				snake_case_						:   Optional[int]           =  f'''repo_txt_data-{int(time.time() * 10E3			)}'''
				snake_case_						:   Any           =  f'''{CI_HUB_USER}/{repo_name}'''
				hf_api.create_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			,    private=_UpperCamelCase			)
				hf_api.upload_file(
				    token=_UpperCamelCase			,    path_or_fileobj=str(_UpperCamelCase			)			,    path_in_repo='''data/text_data.txt'''			,    repo_id=_UpperCamelCase			,    repo_type='''dataset'''			,    )
				yield repo_id
				try:
								hf_api.delete_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			)
				except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
								pass






@pytest.fixture()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				return hf_private_dataset_repo_txt_data_






@pytest.fixture(scope='''session'''			)
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   int           =  f'''repo_zipped_txt_data-{int(time.time() * 10E3			)}'''
				snake_case_						:   Tuple           =  f'''{CI_HUB_USER}/{repo_name}'''
				hf_api.create_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			,    private=_UpperCamelCase			)
				hf_api.upload_file(
				    token=_UpperCamelCase			,    path_or_fileobj=str(_UpperCamelCase			)			,    path_in_repo='''data.zip'''			,    repo_id=_UpperCamelCase			,    repo_type='''dataset'''			,    )
				yield repo_id
				try:
								hf_api.delete_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			)
				except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
								pass






@pytest.fixture()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				return hf_private_dataset_repo_zipped_txt_data_






@pytest.fixture(scope='''session'''			)
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   List[str]           =  f'''repo_zipped_img_data-{int(time.time() * 10E3			)}'''
				snake_case_						:   str           =  f'''{CI_HUB_USER}/{repo_name}'''
				hf_api.create_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			,    private=_UpperCamelCase			)
				hf_api.upload_file(
				    token=_UpperCamelCase			,    path_or_fileobj=str(_UpperCamelCase			)			,    path_in_repo='''data.zip'''			,    repo_id=_UpperCamelCase			,    repo_type='''dataset'''			,    )
				yield repo_id
				try:
								hf_api.delete_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			)
				except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
								pass






@pytest.fixture()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return hf_private_dataset_repo_zipped_img_data_
