


'''simple docstring'''







from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

A__:    str					    =       logging.get_logger(__name__)







@add_end_docstrings(UpperCAmelCase__      )
class A__       (						UpperCAmelCase__      ):






      def __init__( self			:Tuple     ,						**SCREAMING_SNAKE_CASE			:Optional[int]	)       ->					List[str]:




             '''simple docstring'''
             super().__init__(**SCREAMING_SNAKE_CASE	)
             requires_backends(self     ,						"""vision"""	)
             requires_backends(self     ,						"""torch"""	)

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

             self.check_model_type(SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Any     ,						**SCREAMING_SNAKE_CASE			:Tuple	)       ->					Dict:




             '''simple docstring'''
             _a    : int     					={}
             _a    : Optional[int]     					={}
             _a    : Union[str, Any]     					={}
             # preprocess args
             if "points_per_batch" in kwargs:
                    _a    : Union[str, Any]     					=kwargs["""points_per_batch"""]
             if "points_per_crop" in kwargs:
                    _a    : Tuple     					=kwargs["""points_per_crop"""]
             if "crops_n_layers" in kwargs:
                    _a    : Any     					=kwargs["""crops_n_layers"""]
             if "crop_overlap_ratio" in kwargs:
                    _a    : Optional[Any]     					=kwargs["""crop_overlap_ratio"""]
             if "crop_n_points_downscale_factor" in kwargs:
                    _a    : Dict     					=kwargs["""crop_n_points_downscale_factor"""]
             # postprocess args
             if "pred_iou_thresh" in kwargs:
                    _a    : List[Any]     					=kwargs["""pred_iou_thresh"""]
             if "stability_score_offset" in kwargs:
                    _a    : Tuple     					=kwargs["""stability_score_offset"""]
             if "mask_threshold" in kwargs:
                    _a    : List[str]     					=kwargs["""mask_threshold"""]
             if "stability_score_thresh" in kwargs:
                    _a    : Optional[Any]     					=kwargs["""stability_score_thresh"""]
             if "crops_nms_thresh" in kwargs:
                    _a    : List[Any]     					=kwargs["""crops_nms_thresh"""]
             if "output_rle_mask" in kwargs:
                    _a    : int     					=kwargs["""output_rle_mask"""]
             if "output_bboxes_mask" in kwargs:
                    _a    : Optional[Any]     					=kwargs["""output_bboxes_mask"""]
             return preprocess_kwargs, forward_params, postprocess_kwargs






      def __call__( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]     ,						*SCREAMING_SNAKE_CASE			:Tuple     ,						SCREAMING_SNAKE_CASE			:List[Any]=None     ,						SCREAMING_SNAKE_CASE			:str=None     ,						**SCREAMING_SNAKE_CASE			:List[Any]	)       ->					int:




             '''simple docstring'''
             return super().__call__(SCREAMING_SNAKE_CASE     ,						*SCREAMING_SNAKE_CASE     ,						num_workers=SCREAMING_SNAKE_CASE     ,						batch_size=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:int     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:Any=6_4     ,						SCREAMING_SNAKE_CASE			:int = 0     ,						SCREAMING_SNAKE_CASE			:float = 5_1_2 / 1_5_0_0     ,						SCREAMING_SNAKE_CASE			:Optional[int] = 3_2     ,						SCREAMING_SNAKE_CASE			:Optional[int] = 1     ,						)       ->					Any:




             '''simple docstring'''
             _a    : Union[str, Any]     					=load_image(SCREAMING_SNAKE_CASE	)
             _a    : List[str]     					=self.image_processor.size["""longest_edge"""]
             _a					,    _a					,    _a					,    _a    : int     					=self.image_processor.generate_crop_boxes(
                 SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
             _a    : Any     					=self.image_processor(images=SCREAMING_SNAKE_CASE     ,						return_tensors="""pt"""	)

             with self.device_placement():
                    if self.framework == "pt":
                           _a    : Dict     					=self.get_inference_context()
                           with inference_context():
                                  _a    : Union[str, Any]     					=self._ensure_tensor_on_device(SCREAMING_SNAKE_CASE     ,						device=self.device	)
                                  _a    : Optional[Any]     					=self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""	)	)
                                  _a    : Any     					=image_embeddings

             _a    : Dict     					=grid_points.shape[1]
             _a    : Optional[Any]     					=points_per_batch if points_per_batch is not None else n_points

             if points_per_batch <= 0:
                    raise ValueError(
                        """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
                        """To return all points at once, set points_per_batch to None"""	)

             for i in range(0     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	):
                    _a    : Optional[int]     					=grid_points[:, i : i + points_per_batch, :, :]
                    _a    : str     					=input_labels[:, i : i + points_per_batch]
                    _a    : str     					=i == n_points - points_per_batch
                    yield {
                        "input_points": batched_points,
                        "input_labels": labels,
                        "input_boxes": crop_boxes,
                        "is_last": is_last,
                        **model_inputs,
                    }






      def __UpperCAmelCase       ( self			:int     ,						SCREAMING_SNAKE_CASE			:List[Any]     ,						SCREAMING_SNAKE_CASE			:Dict=0.88     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=0.95     ,						SCREAMING_SNAKE_CASE			:Tuple=0     ,						SCREAMING_SNAKE_CASE			:Optional[int]=1     ,						)       ->					Optional[Any]:




             '''simple docstring'''
             _a    : List[Any]     					=model_inputs.pop("""input_boxes"""	)
             _a    : Tuple     					=model_inputs.pop("""is_last"""	)
             _a    : Any     					=model_inputs.pop("""original_sizes"""	).tolist()
             _a    : Optional[Any]     					=model_inputs.pop("""reshaped_input_sizes"""	).tolist()

             _a    : Optional[Any]     					=self.model(**SCREAMING_SNAKE_CASE	)

             # post processing happens here in order to avoid CPU GPU copies of ALL the masks
             _a    : Dict     					=model_outputs["""pred_masks"""]
             _a    : Tuple     					=self.image_processor.post_process_masks(
                 SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						binarize=SCREAMING_SNAKE_CASE	)
             _a    : Any     					=model_outputs["""iou_scores"""]
             _a					,    _a					,    _a    : int     					=self.image_processor.filter_masks(
                 masks[0]     ,						iou_scores[0]     ,						original_sizes[0]     ,						input_boxes[0]     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						)
             return {
                 "masks": masks,
                 "is_last": is_last,
                 "boxes": boxes,
                 "iou_scores": iou_scores,
             }






      def __UpperCAmelCase       ( self			:Tuple     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:Optional[int]=False     ,						SCREAMING_SNAKE_CASE			:str=False     ,						SCREAMING_SNAKE_CASE			:str=0.7     ,						)       ->					Dict:




             '''simple docstring'''
             _a    : str     					=[]
             _a    : List[Any]     					=[]
             _a    : List[str]     					=[]
             for model_output in model_outputs:
                    all_scores.append(model_output.pop("""iou_scores"""	)	)
                    all_masks.extend(model_output.pop("""masks"""	)	)
                    all_boxes.append(model_output.pop("""boxes"""	)	)

             _a    : Dict     					=torch.cat(SCREAMING_SNAKE_CASE	)
             _a    : Optional[Any]     					=torch.cat(SCREAMING_SNAKE_CASE	)
             _a					,    _a					,    _a					,    _a    : Any     					=self.image_processor.post_process_for_mask_generation(
                 SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)

             _a    : Dict     					=defaultdict(SCREAMING_SNAKE_CASE	)
             for output in model_outputs:
                    for k, v in output.items():
                           extra[k].append(SCREAMING_SNAKE_CASE	)

             _a    : Optional[Any]     					={}
             if output_rle_mask:
                    _a    : Union[str, Any]     					=rle_mask

             if output_bboxes_mask:
                    _a    : Optional[int]     					=bounding_boxes

             return {"masks": output_masks, "scores": iou_scores, **optional, **extra}






'''simple docstring'''







class A__       :






      def __init__( self			:List[Any]	)       ->					None:




             '''simple docstring'''
             _a    : dict[str, TrieNode]     					={}  # Mapping from char to TrieNode
             _a    : List[str]     					=False






      def __UpperCAmelCase       ( self			:Tuple     ,						SCREAMING_SNAKE_CASE			:list[str]	)       ->					None:




             '''simple docstring'''
             for word in words:
                    self.insert(SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:str	)       ->					None:




             '''simple docstring'''
             _a    : str     					=self
             for char in word:
                    if char not in curr.nodes:
                           _a    : Dict     					=TrieNode()
                    _a    : List[Any]     					=curr.nodes[char]
             _a    : int     					=True






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:str	)       ->					bool:




             '''simple docstring'''
             _a    : int     					=self
             for char in word:
                    if char not in curr.nodes:
                           return False
                    _a    : List[Any]     					=curr.nodes[char]
             return curr.is_leaf






      def __UpperCAmelCase       ( self			:Dict     ,						SCREAMING_SNAKE_CASE			:str	)       ->					None:




             '''simple docstring'''

             def _delete(SCREAMING_SNAKE_CASE			:TrieNode     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:int	) -> bool:
                    if index == len(SCREAMING_SNAKE_CASE	):
                           # If word does not exist
                           if not curr.is_leaf:
                                  return False
                           _a    : Any     					=False
                           return len(curr.nodes	) == 0
                    _a    : int     					=word[index]
                    _a    : int     					=curr.nodes.get(SCREAMING_SNAKE_CASE	)
                    # If char not in current trie node
                    if not char_node:
                           return False
                    # Flag to check if node can be deleted
                    _a    : List[Any]     					=_delete(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						index + 1	)
                    if delete_curr:
                           del curr.nodes[char]
                           return len(curr.nodes	) == 0
                    return delete_curr

             _delete(self     ,						SCREAMING_SNAKE_CASE     ,						0	)


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	TrieNode    ,_UpperCAmelCase	:	str  )  ->			None:
       if node.is_leaf:
              print(_UpperCAmelCase    ,end=""" """  )

       for key, value in node.nodes.items():
              print_words(_UpperCAmelCase    ,word + key  )


def 	SCREAMING_SNAKE_CASE_					(  )  ->			bool:
       _a    : List[str]     					="""banana bananas bandana band apple all beast""".split()
       _a    : List[Any]     					=TrieNode()
       root.insert_many(_UpperCAmelCase  )
       # print_words(root, "")
       assert all(root.find(_UpperCAmelCase  ) for word in words  )
       assert root.find("""banana"""  )
       assert not root.find("""bandanas"""  )
       assert not root.find("""apps"""  )
       assert root.find("""apple"""  )
       assert root.find("""all"""  )
       root.delete("""all"""  )
       assert not root.find("""all"""  )
       root.delete("""banana"""  )
       assert not root.find("""banana"""  )
       assert root.find("""bananas"""  )
       return True


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	bool  )  ->			None:
       print(str(_UpperCAmelCase  )    ,"""works!""" if passes else """doesn't work :("""  )


def 	SCREAMING_SNAKE_CASE_					(  )  ->			None:
       assert test_trie()


def 	SCREAMING_SNAKE_CASE_					(  )  ->			None:
       print_results("""Testing trie functionality"""    ,test_trie()  )


if __name__ == "__main__":
   main()


