
from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
      import torch

if is_tf_available():
      import tensorflow as tf



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      ["image_processor"]
   _SCREAMING_SNAKE_CASE      :							Tuple	       =      "SamImageProcessor"
   def __init__(self :					List[Any]					,				snake_case_ :					int   ):
    super().__init__(snake_case_   )
    __a :  Union[str, Any]						=  self.image_processor
    __a :  int						=  -1_0
    __a :  Optional[int]						=  self.image_processor.size['''longest_edge''']
   def __call__(self :					Any					,				snake_case_ :					List[Any]=None					,				snake_case_ :					Union[str, Any]=None					,				snake_case_ :					int=None					,				snake_case_ :					Optional[Any]=None					,				snake_case_ :					Optional[Union[str, TensorType]] = None					,				**snake_case_ :					Optional[Any]					,				):
    __a :  Union[str, Any]						=  self.image_processor(
        snake_case_					,				return_tensors=snake_case_					,				**snake_case_					,				)

    # pop arguments that are not used in the foward but used nevertheless
    __a :  int						=  encoding_image_processor['''original_sizes''']

    if hasattr(snake_case_					,				'''numpy'''   ):  # Checks if Torch or TF tensor
     __a :  str						=  original_sizes.numpy()

    __a				,       __a				,       __a :  Union[str, Any]						=  self._check_and_preprocess_points(
        input_points=snake_case_					,				input_labels=snake_case_					,				input_boxes=snake_case_					,				)

    __a :  Dict						=  self._normalize_and_convert(
        snake_case_					,				snake_case_					,				input_points=snake_case_					,				input_labels=snake_case_					,				input_boxes=snake_case_					,				return_tensors=snake_case_					,				)

    return encoding_image_processor
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					Any					,				snake_case_ :					Tuple					,				snake_case_ :					Optional[Any]=None					,				snake_case_ :					Tuple=None					,				snake_case_ :					Union[str, Any]=None					,				snake_case_ :					List[Any]="pt"					,				):
    if input_points is not None:
     if len(snake_case_   ) != len(snake_case_   ):
      __a :  Union[str, Any]						=  [
          self._normalize_coordinates(self.target_size					,				snake_case_					,				original_sizes[0]   ) for point in input_points
      ]
     else:
      __a :  Dict						=  [
          self._normalize_coordinates(self.target_size					,				snake_case_					,				snake_case_   )
          for point, original_size in zip(snake_case_					,				snake_case_   )
      ]
     # check that all arrays have the same shape
     if not all(point.shape == input_points[0].shape for point in input_points   ):
      if input_labels is not None:
       __a				,       __a :  List[str]						=  self._pad_points_and_labels(snake_case_					,				snake_case_   )

     __a :  List[str]						=  np.array(snake_case_   )

    if input_labels is not None:
     __a :  Dict						=  np.array(snake_case_   )

    if input_boxes is not None:
     if len(snake_case_   ) != len(snake_case_   ):
      __a :  Any						=  [
          self._normalize_coordinates(self.target_size					,				snake_case_					,				original_sizes[0]					,				is_bounding_box=snake_case_   )
          for box in input_boxes
      ]
     else:
      __a :  int						=  [
          self._normalize_coordinates(self.target_size					,				snake_case_					,				snake_case_					,				is_bounding_box=snake_case_   )
          for box, original_size in zip(snake_case_					,				snake_case_   )
      ]
     __a :  Dict						=  np.array(snake_case_   )

    if input_boxes is not None:
     if return_tensors == "pt":
      __a :  Dict						=  torch.from_numpy(snake_case_   )
      # boxes batch size of 1 by default
      __a :  Any						=  input_boxes.unsqueeze(1   ) if len(input_boxes.shape   ) != 3 else input_boxes
     elif return_tensors == "tf":
      __a :  Union[str, Any]						=  tf.convert_to_tensor(snake_case_   )
      # boxes batch size of 1 by default
      __a :  str						=  tf.expand_dims(snake_case_					,				1   ) if len(input_boxes.shape   ) != 3 else input_boxes
     encoding_image_processor.update({'''input_boxes''': input_boxes}   )
    if input_points is not None:
     if return_tensors == "pt":
      __a :  Optional[Any]						=  torch.from_numpy(snake_case_   )
      # point batch size of 1 by default
      __a :  Optional[Any]						=  input_points.unsqueeze(1   ) if len(input_points.shape   ) != 4 else input_points
     elif return_tensors == "tf":
      __a :  Any						=  tf.convert_to_tensor(snake_case_   )
      # point batch size of 1 by default
      __a :  List[Any]						=  tf.expand_dims(snake_case_					,				1   ) if len(input_points.shape   ) != 4 else input_points
     encoding_image_processor.update({'''input_points''': input_points}   )
    if input_labels is not None:
     if return_tensors == "pt":
      __a :  int						=  torch.from_numpy(snake_case_   )
      # point batch size of 1 by default
      __a :  int						=  input_labels.unsqueeze(1   ) if len(input_labels.shape   ) != 3 else input_labels
     elif return_tensors == "tf":
      __a :  List[Any]						=  tf.convert_to_tensor(snake_case_   )
      # point batch size of 1 by default
      __a :  Optional[Any]						=  tf.expand_dims(snake_case_					,				1   ) if len(input_labels.shape   ) != 3 else input_labels
     encoding_image_processor.update({'''input_labels''': input_labels}   )

    return encoding_image_processor
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					List[str]   ):
    __a :  List[str]						=  max([point.shape[0] for point in input_points]   )
    __a :  List[str]						=  []
    for i, point in enumerate(snake_case_   ):
     if point.shape[0] != expected_nb_points:
      __a :  str						=  np.concatenate(
          [point, np.zeros((expected_nb_points - point.shape[0], 2)   ) + self.point_pad_value]					,				axis=0   )
      __a :  List[Any]						=  np.append(input_labels[i]					,				[self.point_pad_value]   )
     processed_input_points.append(snake_case_   )
    __a :  Any						=  processed_input_points
    return input_points, input_labels
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					int					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					str=False   ):
    __a				,       __a :  Any						=  original_size
    __a				,       __a :  Tuple						=  self.image_processor._get_preprocess_shape(snake_case_					,				longest_edge=snake_case_   )
    __a :  str						=  deepcopy(snake_case_   ).astype(snake_case_   )

    if is_bounding_box:
     __a :  int						=  coords.reshape(-1					,				2					,				2   )

    __a :  List[str]						=  coords[..., 0] * (new_w / old_w)
    __a :  Optional[Any]						=  coords[..., 1] * (new_h / old_h)

    if is_bounding_box:
     __a :  List[str]						=  coords.reshape(-1					,				4   )

    return coords
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					Union[str, Any]=None					,				snake_case_ :					List[Any]=None					,				snake_case_ :					Any=None					,				):
    if input_points is not None:
     if hasattr(snake_case_					,				'''numpy'''   ):  # Checks for TF or Torch tensor
      __a :  Union[str, Any]						=  input_points.numpy().tolist()

     if not isinstance(snake_case_					,				snake_case_   ) or not isinstance(input_points[0]					,				snake_case_   ):
      raise ValueError('''Input points must be a list of list of floating points.'''   )
     __a :  Dict						=  [np.array(snake_case_   ) for input_point in input_points]
    else:
     __a :  int						=  None

    if input_labels is not None:
     if hasattr(snake_case_					,				'''numpy'''   ):
      __a :  str						=  input_labels.numpy().tolist()

     if not isinstance(snake_case_					,				snake_case_   ) or not isinstance(input_labels[0]					,				snake_case_   ):
      raise ValueError('''Input labels must be a list of list integers.'''   )
     __a :  List[str]						=  [np.array(snake_case_   ) for label in input_labels]
    else:
     __a :  Optional[Any]						=  None

    if input_boxes is not None:
     if hasattr(snake_case_					,				'''numpy'''   ):
      __a :  List[str]						=  input_boxes.numpy().tolist()

     if (
         not isinstance(snake_case_					,				snake_case_   )
         or not isinstance(input_boxes[0]					,				snake_case_   )
         or not isinstance(input_boxes[0][0]					,				snake_case_   )
     ):
      raise ValueError('''Input boxes must be a list of list of list of floating points.'''   )
     __a :  List[str]						=  [np.array(snake_case_   ).astype(np.floataa   ) for box in input_boxes]
    else:
     __a :  Any						=  None

    return input_points, input_labels, input_boxes
   @property
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Any						=  self.image_processor.model_input_names
    return list(dict.fromkeys(snake_case_   )   )







   def       lowerCAmelCase			(self :					str					,				*snake_case_ :					Union[str, Any]					,				**snake_case_ :					Any   ):
    return self.image_processor.post_process_masks(*snake_case_					,				**snake_case_   )


from __future__ import annotations

from typing import Any



class 		UpperCamelCase__  :
   def __init__(self :					Union[str, Any]					,				snake_case_ :					int   ):
    __a :  Dict						=  num_of_nodes
    __a :  list[list[int]]						=  []
    __a :  dict[int, int]						=  {}
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					int   ):
    self.m_edges.append([u_node, v_node, weight]   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					int   ):

    if self.m_component[u_node] == u_node:
     return u_node
    return self.find_component(self.m_component[u_node]   )
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					int   ):

    if self.m_component[u_node] != u_node:
     for k in self.m_component:
      __a :  Optional[int]						=  self.find_component(snake_case_   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					list[int]					,				snake_case_ :					int					,				snake_case_ :					int   ):

    if component_size[u_node] <= component_size[v_node]:
     __a :  List[str]						=  v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(snake_case_   )

    elif component_size[u_node] >= component_size[v_node]:
     __a :  Optional[int]						=  self.find_component(snake_case_   )
     component_size[u_node] += component_size[v_node]
     self.set_component(snake_case_   )







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  str						=  []
    __a :  int						=  0

    __a :  list[Any]						=  [-1] * self.m_num_of_nodes

    # A list of components (initialized to all of the nodes)
    for node in range(self.m_num_of_nodes   ):
     self.m_component.update({node: node}   )
     component_size.append(1   )

    __a :  Union[str, Any]						=  self.m_num_of_nodes

    while num_of_components > 1:
     for edge in self.m_edges:
      __a				,       __a				,       __a :  Optional[Any]						=  edge

      __a :  List[str]						=  self.m_component[u]
      __a :  List[Any]						=  self.m_component[v]

      if u_component != v_component:

       for component in (u_component, v_component):
        if (
            minimum_weight_edge[component] == -1
            or minimum_weight_edge[component][2] > w
        ):
         __a :  str						=  [u, v, w]

     for edge in minimum_weight_edge:
      if isinstance(snake_case_					,				snake_case_   ):
       __a				,       __a				,       __a :  str						=  edge

       __a :  Any						=  self.m_component[u]
       __a :  Any						=  self.m_component[v]

       if u_component != v_component:
        mst_weight += w
        self.union(snake_case_					,				snake_case_					,				snake_case_   )
        print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n"   )
        num_of_components -= 1

     __a :  Optional[int]						=  [-1] * self.m_num_of_nodes
    print(f"The total weight of the minimal spanning tree is: {mst_weight}"   )





def    __UpperCamelCase     (				):
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()
