'''simple docstring'''

from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

__a: List[str]          =   logging.get_logger(__name__)



@add_end_docstrings(a__					)
class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       def __init__(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  List[Any]:
        super().__init__(*__lowerCAmelCase	,   **__lowerCAmelCase )
        requires_backends(self	,   '''vision''' )
        self.check_model_type(__lowerCAmelCase )

       def __call__(						self	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  int:
        return super().__call__(__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  Optional[Any]:
        return {}, {}, {}

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  Tuple:
        lowercase__				:    Any  =					load_image(__lowerCAmelCase )
        lowercase__				:    List[Any]  =					image.size
        lowercase__				:    int  =					self.image_processor(images=__lowerCAmelCase	,   return_tensors=self.framework )
        return model_inputs

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  int:
        lowercase__				:    Tuple  =					self.model(**__lowerCAmelCase )
        return model_outputs


       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  Dict:
        lowercase__				:    List[Any]  =					model_outputs.predicted_depth
        lowercase__				:    int  =					torch.nn.functional.interpolate(
            predicted_depth.unsqueeze(1 )	,   size=self.image_size[::-1]	,   mode='''bicubic'''	,   align_corners=__lowerCAmelCase )
        lowercase__				:    int  =					prediction.squeeze().cpu().numpy()
        lowercase__				:    List[Any]  =					(output * 255 / np.max(__lowerCAmelCase )).astype('''uint8''' )
        lowercase__				:    List[str]  =					Image.fromarray(__lowerCAmelCase )
        lowercase__				:    int  =					{}
        lowercase__				:    List[str]  =					predicted_depth
        lowercase__				:    Any  =					depth
        return output_dict


'''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>""")

