



from typing import Any, Dict, List, Union

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


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from transformers.modeling_outputs import BaseModelOutput

 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__snake_case						:Any      		=     logging.get_logger(__name__)

@add_end_docstrings(a__    )
class     _A       (	a__    ):



       def __init__(		self							:     str     ,		**__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

           super().__init__(**__lowerCAmelCase)

           if self.framework == "tf":
               raise ValueError(F'The {self.__class__} is only available in PyTorch.')

           requires_backends(self     ,		'''vision''')
           self.check_model_type(__lowerCAmelCase)



       def __call__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Any = None     ,		**__SCREAMING_SNAKE_CASE							:     Dict     ,		):


           '''simple docstring'''

           if "text_queries" in kwargs:
               __a						       =				kwargs.pop('''text_queries''')

           if isinstance(__lowerCAmelCase     ,		(str, Image.Image)):
               __a						       =				{'''image''': image, '''candidate_labels''': candidate_labels}
           else:
               __a						       =				image
           __a						       =				super().__call__(__lowerCAmelCase     ,		**__lowerCAmelCase)
           return results



       def 		_lowerCamelCase			(		self							:     List[str]     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           __a						       =				{}
           if "threshold" in kwargs:
               __a						       =				kwargs['''threshold''']
           if "top_k" in kwargs:
               __a						       =				kwargs['''top_k''']
           return {}, {}, postprocess_params



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           __a						       =				load_image(inputs['''image'''])
           __a						       =				inputs['''candidate_labels''']
           if isinstance(__lowerCAmelCase     ,		__lowerCAmelCase):
               __a						       =				candidate_labels.split(''',''')

           __a						       =				torch.tensor([[image.height, image.width]]     ,		dtype=torch.intaa)
           for i, candidate_label in enumerate(__lowerCAmelCase):
               __a						       =				self.tokenizer(__lowerCAmelCase     ,		return_tensors=self.framework)
               __a						       =				self.image_processor(__lowerCAmelCase     ,		return_tensors=self.framework)
               yield {
                   "is_last": i == len(__lowerCAmelCase) - 1,
                   "target_size": target_size,
                   "candidate_label": candidate_label,
                   **text_inputs,
                   **image_features,
               }



       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				model_inputs.pop('''target_size''')
           __a						       =				model_inputs.pop('''candidate_label''')
           __a						       =				model_inputs.pop('''is_last''')

           __a						       =				self.model(**__lowerCAmelCase)

           __a						       =				{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
           return model_outputs



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.1     ,		__SCREAMING_SNAKE_CASE							:     Any=None):


           '''simple docstring'''

           __a						       =				[]
           for model_output in model_outputs:
               __a						       =				model_output['''candidate_label''']
               __a						       =				BaseModelOutput(__lowerCAmelCase)
               __a						       =				self.image_processor.post_process_object_detection(
                   outputs=__lowerCAmelCase     ,		threshold=__lowerCAmelCase     ,		target_sizes=model_output['''target_size'''])[0]

               for index in outputs["scores"].nonzero():
                   __a						       =				outputs['''scores'''][index].item()
                   __a						       =				self._get_bounding_box(outputs['''boxes'''][index][0])

                   __a						       =				{'''score''': score, '''label''': label, '''box''': box}
                   results.append(__lowerCAmelCase)

           __a						       =				sorted(__lowerCAmelCase     ,		key=lambda __SCREAMING_SNAKE_CASE: x["score"]     ,		reverse=__lowerCAmelCase)
           if top_k:
               __a						       =				results[:top_k]

           return results





       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           if self.framework != "pt":
               raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.''')
           __a						       =				box.int().tolist()
           __a						       =				{
               '''xmin''': xmin,
               '''ymin''': ymin,
               '''xmax''': xmax,
               '''ymax''': ymax,
           }
           return bbox






'''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>""")

