


'''simple docstring'''






from typing import List, Union

import numpy as np

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


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

__A						:						int  =  logging.get_logger(__name__)
@add_end_docstrings(a__				)
class     __UpperCamelCase						(    a__				):





							def __init__(	self		:Optional[Any]	,*_UpperCamelCase		:str	,**_UpperCamelCase		:str      ):
									super().__init__(*_UpperCAmelCase	,**_UpperCAmelCase      )
									requires_backends(self	,"""vision"""      )
									self.check_model_type(_UpperCAmelCase      )





							def __call__(	self		:Optional[int]	,_UpperCamelCase		:Tuple	,**_UpperCamelCase		:str      ):
									return super().__call__(_UpperCAmelCase	,**_UpperCAmelCase      )





							def 		a__ (	self		:List[str]	,**_UpperCamelCase		:str      ):
									return {}, {}, {}





							def 		a__ (	self		:str	,_UpperCamelCase		:Optional[int]      ):
									snake_case_		:				str      							=    load_image(_UpperCAmelCase      )
									snake_case_		:				Optional[int]      							=    image.size
									snake_case_		:				Union[str, Any]      							=    self.image_processor(images=_UpperCAmelCase	,return_tensors=self.framework      )
									return model_inputs





							def 		a__ (	self		:Dict	,_UpperCamelCase		:List[Any]      ):
									snake_case_		:				Any      							=    self.model(**_UpperCAmelCase      )
									return model_outputs





							def 		a__ (	self		:List[Any]	,_UpperCamelCase		:str      ):
									snake_case_		:				List[str]      							=    model_outputs.predicted_depth
									snake_case_		:				Optional[int]      							=    torch.nn.functional.interpolate(
									    predicted_depth.unsqueeze(1      )	,size=self.image_size[::-1]	,mode="""bicubic"""	,align_corners=_UpperCAmelCase      )
									snake_case_		:				List[str]      							=    prediction.squeeze().cpu().numpy()
									snake_case_		:				Tuple      							=    (output * 2_5_5 / np.max(_UpperCAmelCase      )).astype("""uint8"""      )
									snake_case_		:				int      							=    Image.fromarray(_UpperCAmelCase      )
									snake_case_		:				Union[str, Any]      							=    {}
									snake_case_		:				Tuple      							=    predicted_depth
									snake_case_		:				List[str]      							=    depth
									return output_dict






'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]     )	->	Optional[Any]:  # noqa: E741
     __A		:					Tuple						=			len(__snake_case     )
     __A		:					Optional[int]						=			0
     __A		:					str						=			[0] * n
     __A		:					int						=			[False] * n
     __A		:					Tuple						=			[False] * n

     def dfs(__snake_case    :					List[str]					,				__snake_case    :					List[Any]					,				__snake_case    :					Optional[Any]					,				__snake_case    :					int     ):
          if parent == root:
               out_edge_count += 1
          __A		:					str						=			True
          __A		:					Tuple						=			at

          for to in l[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    __A		:					Optional[int]						=			dfs(__snake_case					,				__snake_case					,				__snake_case					,				__snake_case     )
                    __A		:					int						=			min(low[at]					,				low[to]     )

                    # AP found via bridge
                    if at < low[to]:
                         __A		:					Tuple						=			True
                    # AP found via cycle
                    if at == low[to]:
                         __A		:					Optional[Any]						=			True
               else:
                    __A		:					Any						=			min(low[at]					,				__snake_case     )
          return out_edge_count

     for i in range(__snake_case     ):
          if not visited[i]:
               __A		:					Tuple						=			0
               __A		:					List[Any]						=			dfs(__snake_case					,				__snake_case					,				-1					,				__snake_case     )
               __A		:					Union[str, Any]						=			out_edge_count > 1

     for x in range(len(__snake_case     )     ):
          if is_art[x] is True:
               print(__snake_case     )


# Adjacency list of graph
lowercase__       : Tuple         =       {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}







compute_ap(data)