






class     a    :



      """simple docstring"""


      def __init__(      self					:			Dict					)							->      Dict:
        __UpperCAmelCase							:	Optional[int]           =  0
        __UpperCAmelCase							:	str           =  0
        __UpperCAmelCase							:	int           =  {}
      def 	UpperCAmelCase      (      self					:			int		,      __lowercase					:			Any					)							->      Dict:




        if vertex not in self.adjacency:
          __UpperCAmelCase							:	Any           =  {}
          self.num_vertices += 1
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			Union[str, Any]		,      __lowercase					:			Any		,      __lowercase					:			str					)							->      Dict:
        self.add_vertex(SCREAMING_SNAKE_CASE__					)
        self.add_vertex(SCREAMING_SNAKE_CASE__					)

        if head == tail:
          return

        __UpperCAmelCase							:	int           =  weight
        __UpperCAmelCase							:	Optional[Any]           =  weight
      def 	UpperCAmelCase      (      self					:			str					)							->      List[str]:
        __UpperCAmelCase							:	Any           =  self.get_edges()
        for edge in edges:
          __UpperCAmelCase							:	List[Any]           =  edge
          edges.remove((tail, head, weight)					)
        for i in range(len(SCREAMING_SNAKE_CASE__					)					):
          __UpperCAmelCase							:	List[str]           =  list(edges[i]					)

        edges.sort(key=lambda __lowercase					: e[2]					)
        for i in range(len(SCREAMING_SNAKE_CASE__					) - 1					):
          if edges[i][2] >= edges[i + 1][2]:
            __UpperCAmelCase							:	Any           =  edges[i][2] + 1
        for edge in edges:
          __UpperCAmelCase							:	Tuple           =  edge
          __UpperCAmelCase							:	Any           =  weight
          __UpperCAmelCase							:	List[str]           =  weight
      def __str__(      self					:			Any					)							->      Dict:
        __UpperCAmelCase							:	Union[str, Any]           =  """"""
        for tail in self.adjacency:
          for head in self.adjacency[tail]:
            __UpperCAmelCase							:	Union[str, Any]           =  self.adjacency[head][tail]
            string += f"""{head} -> {tail} == {weight}\n"""
        return string.rstrip("""\n"""					)
      def 	UpperCAmelCase      (      self					:			int					)							->      Optional[Any]:
        __UpperCAmelCase							:	Tuple           =  []
        for tail in self.adjacency:
          for head in self.adjacency[tail]:
            output.append((tail, head, self.adjacency[head][tail])					)
        return output
      def 	UpperCAmelCase      (      self					:			int					)							->      List[str]:
        return self.adjacency.keys()
      @staticmethod
      def 	UpperCAmelCase      (      __lowercase					:			Tuple=None		,      __lowercase					:			str=None					)							->      str:
        __UpperCAmelCase							:	Any           =  Graph()
        if vertices is None:
          __UpperCAmelCase							:	Union[str, Any]           =  []
        if edges is None:
          __UpperCAmelCase							:	List[str]           =  []
        for vertex in vertices:
          g.add_vertex(SCREAMING_SNAKE_CASE__					)
        for edge in edges:
          g.add_edge(*SCREAMING_SNAKE_CASE__					)
        return g
      class     a    :



            """simple docstring"""


            def __init__(      self					:			int					)							->      List[str]:
              __UpperCAmelCase							:	Dict           =  {}
              __UpperCAmelCase							:	List[Any]           =  {}
            def __len__(      self					:			str					)							->      Any:
              return len(self.parent					)
            def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			List[Any]					)							->      Union[str, Any]:




              if item in self.parent:
                return self.find(SCREAMING_SNAKE_CASE__					)

              __UpperCAmelCase							:	int           =  item
              __UpperCAmelCase							:	Dict           =  0
              return item
            def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			Any					)							->      str:




              if item not in self.parent:
                return self.make_set(SCREAMING_SNAKE_CASE__					)
              if item != self.parent[item]:
                __UpperCAmelCase							:	List[Any]           =  self.find(self.parent[item]					)
              return self.parent[item]




            def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			Optional[Any]		,      __lowercase					:			Optional[Any]					)							->      Tuple:
              __UpperCAmelCase							:	str           =  self.find(SCREAMING_SNAKE_CASE__					)
              __UpperCAmelCase							:	List[Any]           =  self.find(SCREAMING_SNAKE_CASE__					)

              if roota == roota:
                return roota

              if self.rank[roota] > self.rank[roota]:
                __UpperCAmelCase							:	str           =  roota
                return roota

              if self.rank[roota] < self.rank[roota]:
                __UpperCAmelCase							:	Union[str, Any]           =  roota
                return roota

              if self.rank[roota] == self.rank[roota]:
                self.rank[roota] += 1
                __UpperCAmelCase							:	Optional[Any]           =  roota
                return roota
              return None


      @staticmethod
      def 	UpperCAmelCase      (      __lowercase					:			Union[str, Any]					)							->      Any:
        __UpperCAmelCase							:	Any           =  graph.num_vertices

        __UpperCAmelCase							:	List[str]           =  Graph.UnionFind()
        __UpperCAmelCase							:	Tuple           =  []
        while num_components > 1:
          __UpperCAmelCase							:	List[str]           =  {}
          for vertex in graph.get_vertices():
            __UpperCAmelCase							:	List[str]           =  -1

          __UpperCAmelCase							:	str           =  graph.get_edges()
          for edge in edges:
            __UpperCAmelCase							:	Dict           =  edge
            edges.remove((tail, head, weight)					)
          for edge in edges:
            __UpperCAmelCase							:	List[Any]           =  edge
            __UpperCAmelCase							:	int           =  union_find.find(SCREAMING_SNAKE_CASE__					)
            __UpperCAmelCase							:	Optional[int]           =  union_find.find(SCREAMING_SNAKE_CASE__					)
            if seta != seta:
              if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                __UpperCAmelCase							:	Dict           =  [head, tail, weight]

              if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                __UpperCAmelCase							:	List[Any]           =  [head, tail, weight]
          for vertex in cheap_edge:
            if cheap_edge[vertex] != -1:
              __UpperCAmelCase							:	Tuple           =  cheap_edge[vertex]
              if union_find.find(SCREAMING_SNAKE_CASE__					) != union_find.find(SCREAMING_SNAKE_CASE__					):
                union_find.union(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
                mst_edges.append(cheap_edge[vertex]					)
                __UpperCAmelCase							:	Tuple           =  num_components - 1
        __UpperCAmelCase							:	int           =  Graph.build(edges=SCREAMING_SNAKE_CASE__					)
        return mst




"""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

UpperCAmelCase__      :    List[str]    							= logging.get_logger(__name__)



@add_end_docstrings(a__  )
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											super().__init__(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											requires_backends(self   ,       """vision"""    )
											self.check_model_type(SCREAMING_SNAKE_CASE__    )



				def __call__(self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											return super().__call__(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return {}, {}, {}



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   load_image(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   image.size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors=self.framework    )
											return model_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model(**SCREAMING_SNAKE_CASE__    )
											return model_outputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   model_outputs.predicted_depth
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1    )   ,       size=self.image_size[::-1]   ,       mode="""bicubic"""   ,       align_corners=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prediction.squeeze().cpu().numpy()
											SCREAMING_SNAKE_CASE__						:       Any           =   (output * 2_55 / np.max(SCREAMING_SNAKE_CASE__    )).astype("""uint8"""    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.fromarray(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   {}
											SCREAMING_SNAKE_CASE__						:       Any           =   predicted_depth
											SCREAMING_SNAKE_CASE__						:       Dict           =   depth
											return output_dict




