


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

SCREAMING_SNAKE_CASE						:Optional[int]			=  logging.get_logger(__name__)


@add_end_docstrings(_UpperCAmelCase       )
class      __lowerCAmelCase  (			_UpperCAmelCase       ):






  """simple docstring"""





  def __init__(  self							:							Tuple     ,					**_lowerCAmelCase							:							str	) ->			List[str]:




     """simple docstring"""
     super().__init__(**SCREAMING_SNAKE_CASE_	)

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

     requires_backends(self     ,					"vision"	)
     self.check_model_type(SCREAMING_SNAKE_CASE_	)



  def __call__(  self							:							Any     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							Tuple = None     ,					**_lowerCAmelCase							:							List[str]     ,					) ->			Optional[Any]:




     """simple docstring"""
     if "text_queries" in kwargs:
        snake_case_            =       kwargs.pop("text_queries"	)

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



  def        lowerCAmelCase__	(  self							:							Optional[int]     ,					**_lowerCAmelCase							:							Union[str, Any]	) ->			List[Any]:




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



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Dict	) ->			List[str]:




     """simple docstring"""
     snake_case_            =       load_image(inputs["image"]	)
     snake_case_            =       inputs['candidate_labels']
     if isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_	):
        snake_case_            =       candidate_labels.split(","	)

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



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Optional[int]	) ->			Any:




     """simple docstring"""
     snake_case_            =       model_inputs.pop("target_size"	)
     snake_case_            =       model_inputs.pop("candidate_label"	)
     snake_case_            =       model_inputs.pop("is_last"	)

     snake_case_            =       self.model(**SCREAMING_SNAKE_CASE_	)

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



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Dict=0.1     ,					_lowerCAmelCase							:							Optional[Any]=None	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       []
     for model_output in model_outputs:
        snake_case_            =       model_output['candidate_label']
        snake_case_            =       BaseModelOutput(SCREAMING_SNAKE_CASE_	)
        snake_case_            =       self.image_processor.post_process_object_detection(
            outputs=SCREAMING_SNAKE_CASE_     ,					threshold=SCREAMING_SNAKE_CASE_     ,					target_sizes=model_output["target_size"]	)[0]

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

           snake_case_            =       {'score': score, 'label': label, 'box': box}
           results.append(SCREAMING_SNAKE_CASE_	)

     snake_case_            =       sorted(SCREAMING_SNAKE_CASE_     ,					key=lambda _lowerCAmelCase	: x["score"]     ,					reverse=SCREAMING_SNAKE_CASE_	)
     if top_k:
        snake_case_            =       results[:top_k]

     return results



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							List[Any]	) ->			Dict[str, int]:




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








'''simple docstring'''



from collections import namedtuple

import requests
from lxml import html  # type: ignore

A__						:   Tuple				=    namedtuple("""covid_data""", """cases deaths recovered""")



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "https://www.worldometers.info/coronavirus/"				)	->						covid_data:
     __lowerCamelCase  :      Union[str, Any]														=						'//div[@class = "maincounter-number"]/span/text()'
     return covid_data(*html.fromstring(requests.get(UpperCAmelCase_				).content				).xpath(UpperCAmelCase_				)				)


A__						:   str				=    """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""

print(fmt.format(*covid_stats()))






