'''simple docstring'''

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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__a: str          =   logging.get_logger(__name__)  # pylint: disable=invalid-name
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 warnings.warn(
     '''The preprocess method is deprecated and will be removed in a future version. Please'''
     ''' use VaeImageProcessor.preprocess instead'''		,     UpperCAmelCase		,     )
 if isinstance(UpperCAmelCase		,     torch.Tensor     ):
  return image
 elif isinstance(UpperCAmelCase		,     PIL.Image.Image     ):
  lowercase__				:    Optional[int]  =					[image]

 if isinstance(image[0]		,     PIL.Image.Image     ):
  lowercase__ ,							lowercase__				:    Any  =					image[0].size
  lowercase__ ,							lowercase__				:    Dict  =					(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

  lowercase__				:    Dict  =					[np.array(i.resize((w, h)		,     resample=PIL_INTERPOLATION['''lanczos''']     )     )[None, :] for i in image]
  lowercase__				:    str  =					np.concatenate(UpperCAmelCase		,     axis=0     )
  lowercase__				:    Union[str, Any]  =					np.array(UpperCAmelCase     ).astype(np.floataa     ) / 2_5_5.0
  lowercase__				:    List[str]  =					image.transpose(0		,     3		,     1		,     2     )
  lowercase__				:    str  =					2.0 * image - 1.0
  lowercase__				:    Tuple  =					torch.from_numpy(UpperCAmelCase     )
 elif isinstance(image[0]		,     torch.Tensor     ):
  lowercase__				:    List[str]  =					torch.cat(UpperCAmelCase		,     dim=0     )
 return image
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 if isinstance(UpperCAmelCase		,     torch.Tensor     ):
  return mask
 elif isinstance(UpperCAmelCase		,     PIL.Image.Image     ):
  lowercase__				:    str  =					[mask]

 if isinstance(mask[0]		,     PIL.Image.Image     ):
  lowercase__ ,							lowercase__				:    Optional[int]  =					mask[0].size
  lowercase__ ,							lowercase__				:    int  =					(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  lowercase__				:    Any  =					[np.array(m.convert('''L'''     ).resize((w, h)		,     resample=PIL_INTERPOLATION['''nearest''']     )     )[None, :] for m in mask]
  lowercase__				:    str  =					np.concatenate(UpperCAmelCase		,     axis=0     )
  lowercase__				:    Optional[Any]  =					mask.astype(np.floataa     ) / 2_5_5.0
  lowercase__				:    Optional[Any]  =					0
  lowercase__				:    List[Any]  =					1
  lowercase__				:    Optional[int]  =					torch.from_numpy(UpperCAmelCase     )
 elif isinstance(mask[0]		,     torch.Tensor     ):
  lowercase__				:    Union[str, Any]  =					torch.cat(UpperCAmelCase		,     dim=0     )
 return mask





class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    42
       SCREAMING_SNAKE_CASE							=    42

       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  List[str]:
        super().__init__()
        self.register_modules(unet=__lowerCAmelCase	,   scheduler=__lowerCAmelCase )


       @torch.no_grad()
       def __call__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = 250	,   __lowerCAmelCase = 0.0	,   __lowerCAmelCase = 10	,   __lowerCAmelCase = 10	,   __lowerCAmelCase = None	,   __lowerCAmelCase = "pil"	,   __lowerCAmelCase = True	,   )  ->  Union[ImagePipelineOutput, Tuple]:
        lowercase__				:    str  =					image

        lowercase__				:    List[Any]  =					_preprocess_image(__lowerCAmelCase )
        lowercase__				:    Any  =					original_image.to(device=self.device	,   dtype=self.unet.dtype )
        lowercase__				:    int  =					_preprocess_mask(__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					mask_image.to(device=self.device	,   dtype=self.unet.dtype )

        lowercase__				:    Optional[Any]  =					original_image.shape[0]

        # sample gaussian noise to begin the loop
        if isinstance(__lowerCAmelCase	,   __lowerCAmelCase ) and len(__lowerCAmelCase ) != batch_size:
         raise ValueError(
             F"""You have passed a list of generators of length {len(__lowerCAmelCase )}, but requested an effective batch"""
             F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

        lowercase__				:    int  =					original_image.shape
        lowercase__				:    List[str]  =					randn_tensor(__lowerCAmelCase	,   generator=__lowerCAmelCase	,   device=self.device	,   dtype=self.unet.dtype )

        # set step values
        self.scheduler.set_timesteps(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   self.device )
        lowercase__				:    str  =					eta

        lowercase__				:    List[str]  =					self.scheduler.timesteps[0] + 1
        lowercase__				:    Union[str, Any]  =					generator[0] if isinstance(__lowerCAmelCase	,   __lowerCAmelCase ) else generator
        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
         if t < t_last:
          # predict the noise residual
          lowercase__				:    Optional[Any]  =					self.unet(__lowerCAmelCase	,   __lowerCAmelCase ).sample
          # compute previous image: x_t -> x_t-1
          lowercase__				:    Dict  =					self.scheduler.step(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase ).prev_sample

         else:
          # compute the reverse: x_t-1 -> x_t
          lowercase__				:    int  =					self.scheduler.undo_step(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )
         lowercase__				:    int  =					t

        lowercase__				:    int  =					(image / 2 + 0.5).clamp(0	,   1 )
        lowercase__				:    Optional[int]  =					image.cpu().permute(0	,   2	,   3	,   1 ).numpy()
        if output_type == "pil":
         lowercase__				:    Union[str, Any]  =					self.numpy_to_pil(__lowerCAmelCase )

        if not return_dict:
         return (image,)

        return ImagePipelineOutput(images=__lowerCAmelCase )


'''simple docstring'''

import requests

__a: str          =   """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 # fetching a list of articles in json format
 lowercase__				:    Optional[Any]  =					requests.get(_NEWS_API + bbc_news_api_key     ).json()
 # each article in the list is a dict
 for i, article in enumerate(bbc_news_page['''articles''']		,     1     ):
  print(F"""{i}.) {article['title']}"""     )


if __name__ == "__main__":
  fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")

