


"""simple docstring"""







from __future__ import annotations

from typing import Generic, TypeVar

lowerCamelCase_       :       List[Any]      						=  TypeVar("""T""")






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self			,   __A						) ->					None:
     a					=data
     a					=self
     a					=0






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self						) ->					None:
     # map from node name to the node object
     a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					None:
     # create a new set with x as its member
     a					=DisjointSetTreeNode(__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					DisjointSetTreeNode[T]:
     # find the set x belongs to (with path-compression)
     a					=self.map[data]
     if elem_ref != elem_ref.parent:
         a					=self.find_set(elem_ref.parent.data						)
     return elem_ref.parent


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					None:
     # helper function for union operation
     if nodea.rank > nodea.rank:
         a					=nodea
     else:
         a					=nodea
         if nodea.rank == nodea.rank:
             nodea.rank += 1





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					None:
     # merge 2 disjoint sets
     self.link(self.find_set(__A						)			,   self.find_set(__A						)						)






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self						) ->					None:
     # connections: map from the node to the neighbouring nodes (with weights)
     a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					None:
     # add a node ONLY if its not present in the graph
     if node not in self.connections:
         a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					None:
     # add an edge with the given weight
     self.add_node(__A						)
     self.add_node(__A						)
     a					=weight
     a					=weight





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					GraphUndirectedWeighted[T]:
     a					=[]
     a					=set()
     for start in self.connections:
         for end in self.connections[start]:
             if (start, end) not in seen:
                 seen.add((end, start)						)
                 edges.append((start, end, self.connections[start][end])						)
     edges.sort(key=lambda __A						: x[2]						)

     # creating the disjoint set
     a					=DisjointSetTree[T]()
     for node in self.connections:
         disjoint_set.make_set(__A						)

     # MST generation
     a					=0
     a					=0
     a					=GraphUndirectedWeighted[T]()
     while num_edges < len(self.connections						) - 1:
         a		,				a		,				a					=edges[index]
         index += 1
         a					=disjoint_set.find_set(__A						)
         a					=disjoint_set.find_set(__A						)
         if parent_u != parent_v:
             num_edges += 1
             graph.add_edge(__A			,   __A			,   __A						)
             disjoint_set.union(__A			,   __A						)
     return graph



"""simple docstring"""







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

lowerCamelCase_       :       Dict      						=  logging.get_logger(__name__)






@add_end_docstrings(_SCREAMING_SNAKE_CASE      )
class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 def __init__(		self			,   **__A						) ->					Dict:
     super().__init__(**__A						)

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

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


 def __call__(		self			,   __A			,   __A = None			,   **__A			,   ) ->					List[str]:
     if "text_queries" in kwargs:
         a					=kwargs.pop('''text_queries'''						)

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Optional[Any]:
     a					={}
     if "threshold" in kwargs:
         a					=kwargs['''threshold''']
     if "top_k" in kwargs:
         a					=kwargs['''top_k''']
     return {}, {}, postprocess_params


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					str:
     a					=load_image(inputs['''image''']						)
     a					=inputs['''candidate_labels''']
     if isinstance(__A			,   __A						):
         a					=candidate_labels.split(''','''						)

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[Any]:
     a					=model_inputs.pop('''target_size'''						)
     a					=model_inputs.pop('''candidate_label'''						)
     a					=model_inputs.pop('''is_last'''						)

     a					=self.model(**__A						)

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=0.1			,   __A=None						) ->					List[str]:
     a					=[]
     for model_output in model_outputs:
         a					=model_output['''candidate_label''']
         a					=BaseModelOutput(__A						)
         a					=self.image_processor.post_process_object_detection(
             outputs=__A			,   threshold=__A			,   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(__A						)

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

     return results





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Dict[str, int]:
     if self.framework != "pt":
         raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''						)
     a		,				a		,				a		,				a					=box.int().tolist()
     a					={
         '''xmin''': xmin,
         '''ymin''': ymin,
         '''xmax''': xmax,
         '''ymax''': ymax,
     }
     return bbox