

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 A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''image_processor''']
 _lowercase     :					Optional[Any]      =						'''SamImageProcessor'''

 def __init__(		self :	List[Any]     ,						A_ :	str       ):






      '''simple docstring'''




      super().__init__(A_       )
      _lowerCAmelCase   :					List[Any]        =    self.image_processor
      _lowerCAmelCase   :					Tuple        =    -1_0
      _lowerCAmelCase   :					Any        =    self.image_processor.size["longest_edge"]

 def __call__(		self :	Any     ,						A_ :	Tuple=None     ,						A_ :	Optional[Any]=None     ,						A_ :	Union[str, Any]=None     ,						A_ :	Optional[Any]=None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						**A_ :	Union[str, Any]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.image_processor(
          A_     ,						return_tensors=A_     ,						**A_     ,						)

      # pop arguments that are not used in the foward but used nevertheless
      _lowerCAmelCase   :					Optional[Any]        =    encoding_image_processor["original_sizes"]

      if hasattr(A_     ,						"numpy"       ):  # Checks if Torch or TF tensor
           _lowerCAmelCase   :					Any        =    original_sizes.numpy()

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

      _lowerCAmelCase   :					List[Any]        =    self._normalize_and_convert(
          A_     ,						A_     ,						input_points=A_     ,						input_labels=A_     ,						input_boxes=A_     ,						return_tensors=A_     ,						)

      return encoding_image_processor

 def    __magic_name__       (		self :	int     ,						A_ :	List[Any]     ,						A_ :	Tuple     ,						A_ :	str=None     ,						A_ :	Any=None     ,						A_ :	Tuple=None     ,						A_ :	List[str]="pt"     ,						):






      '''simple docstring'''




      if input_points is not None:
           if len(A_       ) != len(A_       ):
                _lowerCAmelCase   :					List[str]        =    [
                    self._normalize_coordinates(self.target_size     ,						A_     ,						original_sizes[0]       ) for point in input_points
                ]
           else:
                _lowerCAmelCase   :					Any        =    [
                    self._normalize_coordinates(self.target_size     ,						A_     ,						A_       )
                    for point, original_size in zip(A_     ,						A_       )
                ]
           # 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:
                     _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    self._pad_points_and_labels(A_     ,						A_       )

           _lowerCAmelCase   :					Tuple        =    np.array(A_       )

      if input_labels is not None:
           _lowerCAmelCase   :					List[str]        =    np.array(A_       )

      if input_boxes is not None:
           if len(A_       ) != len(A_       ):
                _lowerCAmelCase   :					Optional[Any]        =    [
                    self._normalize_coordinates(self.target_size     ,						A_     ,						original_sizes[0]     ,						is_bounding_box=A_       )
                    for box in input_boxes
                ]
           else:
                _lowerCAmelCase   :					Union[str, Any]        =    [
                    self._normalize_coordinates(self.target_size     ,						A_     ,						A_     ,						is_bounding_box=A_       )
                    for box, original_size in zip(A_     ,						A_       )
                ]
           _lowerCAmelCase   :					Any        =    np.array(A_       )

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

      return encoding_image_processor

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Tuple     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    max([point.shape[0] for point in input_points]       )
      _lowerCAmelCase   :					Any        =    []
      for i, point in enumerate(A_       ):
           if point.shape[0] != expected_nb_points:
                _lowerCAmelCase   :					str        =    np.concatenate(
                    [point, np.zeros((expected_nb_points - point.shape[0], 2)       ) + self.point_pad_value]     ,						axis=0       )
                _lowerCAmelCase   :					Optional[int]        =    np.append(input_labels[i]     ,						[self.point_pad_value]       )
           processed_input_points.append(A_       )
      _lowerCAmelCase   :					Optional[int]        =    processed_input_points
      return input_points, input_labels

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	int     ,						A_ :	np.ndarray     ,						A_ :	List[Any]     ,						A_ :	Tuple=False       ):






      '''simple docstring'''




      _lowerCAmelCase      ,			_lowerCAmelCase   :					List[Any]        =    original_size
      _lowerCAmelCase      ,			_lowerCAmelCase   :					Union[str, Any]        =    self.image_processor._get_preprocess_shape(A_     ,						longest_edge=A_       )
      _lowerCAmelCase   :					Any        =    deepcopy(A_       ).astype(A_       )

      if is_bounding_box:
           _lowerCAmelCase   :					Any        =    coords.reshape(-1     ,						2     ,						2       )

      _lowerCAmelCase   :					Dict        =    coords[..., 0] * (new_w / old_w)
      _lowerCAmelCase   :					str        =    coords[..., 1] * (new_h / old_h)

      if is_bounding_box:
           _lowerCAmelCase   :					Dict        =    coords.reshape(-1     ,						4       )

      return coords

 def    __magic_name__       (		self :	Any     ,						A_ :	Dict=None     ,						A_ :	Optional[Any]=None     ,						A_ :	Tuple=None     ,						):






      '''simple docstring'''




      if input_points is not None:
           if hasattr(A_     ,						"numpy"       ):  # Checks for TF or Torch tensor
                _lowerCAmelCase   :					List[Any]        =    input_points.numpy().tolist()

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

      if input_labels is not None:
           if hasattr(A_     ,						"numpy"       ):
                _lowerCAmelCase   :					Union[str, Any]        =    input_labels.numpy().tolist()

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

      if input_boxes is not None:
           if hasattr(A_     ,						"numpy"       ):
                _lowerCAmelCase   :					Optional[Any]        =    input_boxes.numpy().tolist()

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

      return input_points, input_labels, input_boxes

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.image_processor.model_input_names
      return list(dict.fromkeys(A_       )       )


 def    __magic_name__       (		self :	Tuple     ,						*A_ :	Any     ,						**A_ :	Tuple       ):






      '''simple docstring'''




      return self.image_processor.post_process_masks(*A_     ,						**A_       )









# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__UpperCAmelCase    				=     TypeVar('T')





class A__    ( Generic[T] ):



 """simple docstring"""







 def __init__(		self :	Optional[int]     ,						A_ :	bool = True       ):






      '''simple docstring'''




      _lowerCAmelCase   :					dict[T, list[T]]        =    {}  # dictionary of lists
      _lowerCAmelCase   :					List[Any]        =    directed

 def    __magic_name__       (		self :	Any     ,						A_ :	T     ,						A_ :	T       ):






      '''simple docstring'''




      if not self.directed:  # For undirected graphs
           # if both source vertex and destination vertex are both present in the
           # adjacency list, add destination vertex to source vertex list of adjacent
           # vertices and add source vertex to destination vertex list of adjacent
           # vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                self.adj_list[destination_vertex].append(A_       )
           # if only source vertex is present in adjacency list, add destination vertex
           # to source vertex list of adjacent vertices, then create a new vertex with
           # destination vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                _lowerCAmelCase   :					List[Any]        =    [source_vertex]
           # if only destination vertex is present in adjacency list, add source vertex
           # to destination vertex list of adjacent vertices, then create a new vertex
           # with source vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif destination_vertex in self.adj_list:
                self.adj_list[destination_vertex].append(A_       )
                _lowerCAmelCase   :					List[str]        =    [destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and assign a list
           # containing the destination vertex as it's first adjacent vertex also
           # create a new vertex with destination vertex as key and assign a list
           # containing the source vertex as it's first adjacent vertex.
           else:
                _lowerCAmelCase   :					List[str]        =    [destination_vertex]
                _lowerCAmelCase   :					Dict        =    [source_vertex]
      else:  # For directed graphs
           # if both source vertex and destination vertex are present in adjacency
           # list, add destination vertex to source vertex list of adjacent vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
           # if only source vertex is present in adjacency list, add destination
           # vertex to source vertex list of adjacent vertices and create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                _lowerCAmelCase   :					Tuple        =    []
           # if only destination vertex is present in adjacency list, create a new
           # vertex with source vertex as key and assign a list containing destination
           # vertex as first adjacent vertex
           elif destination_vertex in self.adj_list:
                _lowerCAmelCase   :					Dict        =    [destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and a list containing
           # destination vertex as it's first adjacent vertex. Then create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           else:
                _lowerCAmelCase   :					Any        =    [destination_vertex]
                _lowerCAmelCase   :					Union[str, Any]        =    []

      return self


 def __repr__(		self :	List[str]       ):






      '''simple docstring'''




      return pformat(self.adj_list       )






