


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







@add_end_docstrings(_snake_case							)
class 		_snake_case  (      _snake_case							):




 def __init__(		self					,  **_lowerCamelCase    ):
       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					,  _lowerCamelCase					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       if "text_queries" in kwargs:
             a						:Tuple 							=       kwargs.pop('''text_queries'''    )

       if isinstance(_lowerCamelCase					,  (str, Image.Image)    ):
             a						:Any 							=       {'''image''': image, '''candidate_labels''': candidate_labels}
       else:
             a						:Optional[int] 							=       image
       a						:Optional[Any] 							=       super().__call__(_lowerCamelCase					,  **_lowerCamelCase    )
       return results




 def        SCREAMING_SNAKE_CASE__				(		self					,  **_lowerCamelCase    ):
       a						:Any 							=       {}
       if "threshold" in kwargs:
             a						:List[str] 							=       kwargs['''threshold''']
       if "top_k" in kwargs:
             a						:Optional[int] 							=       kwargs['''top_k''']
       return {}, {}, postprocess_params




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Dict 							=       load_image(inputs['''image''']    )
       a						:List[Any] 							=       inputs['''candidate_labels''']
       if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
             a						:Tuple 							=       candidate_labels.split(''','''    )

       a						:Optional[int] 							=       torch.tensor([[image.height, image.width]]					,  dtype=torch.intaa    )
       for i, candidate_label in enumerate(_lowerCamelCase    ):
             a						:Optional[Any] 							=       self.tokenizer(_lowerCamelCase					,  return_tensors=self.framework    )
             a						:Optional[int] 							=       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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:int 							=       model_inputs.pop('''target_size'''    )
       a						:int 							=       model_inputs.pop('''candidate_label'''    )
       a						:Optional[Any] 							=       model_inputs.pop('''is_last'''    )

       a						:Dict 							=       self.model(**_lowerCamelCase    )

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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0.1					,  _lowerCamelCase=None    ):
       a						:int 							=       []
       for model_output in model_outputs:
             a						:List[Any] 							=       model_output['''candidate_label''']
             a						:Union[str, Any] 							=       BaseModelOutput(_lowerCamelCase    )
             a						:Union[str, Any] 							=       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						:str 							=       outputs['''scores'''][index].item()
                   a						:Dict 							=       self._get_bounding_box(outputs['''boxes'''][index][0]    )

                   a						:Tuple 							=       {'''score''': score, '''label''': label, '''box''': box}
                   results.append(_lowerCamelCase    )

       a						:Optional[int] 							=       sorted(_lowerCamelCase					,  key=lambda _lowerCamelCase    : x["score"]					,  reverse=_lowerCamelCase    )
       if top_k:
             a						:Optional[int] 							=       results[:top_k]

       return results




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       if self.framework != "pt":
             raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''    )
       a							,				a							,				a							,				a						:int 							=       box.int().tolist()
       a						:List[Any] 							=       {
           '''xmin''': xmin,
           '''ymin''': ymin,
           '''xmax''': xmax,
           '''ymax''': ymax,
       }
       return bbox









import math







class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase=0    ):  # a graph with Node 0,1,...,N-1
       a						:Optional[int] 							=       n
       a						:Union[str, Any] 							=       [
           [math.inf for j in range(0					,  _lowerCamelCase    )] for i in range(0					,  _lowerCamelCase    )
       ]  # adjacency matrix for weight
       a						:List[Any] 							=       [
           [math.inf for j in range(0					,  _lowerCamelCase    )] for i in range(0					,  _lowerCamelCase    )
       ]  # dp[i][j] stores minimum distance from i to j




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Tuple 							=       w




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for k in range(0					,  self.n    ):
             for i in range(0					,  self.n    ):
                   for j in range(0					,  self.n    ):
                         a						:Union[str, Any] 							=       min(self.dp[i][j]					,  self.dp[i][k] + self.dp[k][j]    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       return self.dp[u][v]


if __name__ == "__main__":
     snake_case				:    str       				=     Graph(5)
     graph.add_edge(0, 2, 9)
     graph.add_edge(0, 4, 10)
     graph.add_edge(1, 3, 5)
     graph.add_edge(2, 3, 7)
     graph.add_edge(3, 0, 10)
     graph.add_edge(3, 1, 2)
     graph.add_edge(3, 2, 1)
     graph.add_edge(3, 4, 6)
     graph.add_edge(4, 1, 3)
     graph.add_edge(4, 2, 4)
     graph.add_edge(4, 3, 9)
     graph.floyd_warshall()
     graph.show_min(1, 4)
     graph.show_min(0, 3)





