




'''simple docstring'''


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_				(			UpperCAmelCase__						):
	lowercase_       :     Union[str, Any]			  =      ['''image_processor''']
	lowercase_       :     str			  =      '''SamImageProcessor'''




	def __init__(       self     :  Optional[int]       ,		__lowerCAmelCase     :  int      ):
							super().__init__(__lowerCAmelCase      )
							__snake_case						 =   self.image_processor
							__snake_case						 =   -1_0
							__snake_case						 =   self.image_processor.size['longest_edge']




	def __call__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]=None       ,		__lowerCAmelCase     :  Any=None       ,		__lowerCAmelCase     :  Union[str, Any]=None       ,		__lowerCAmelCase     :  Optional[int]=None       ,		__lowerCAmelCase     :  Optional[Union[str, TensorType]] = None       ,		**__lowerCAmelCase     :  Optional[int]       ,		):
							__snake_case						 =   self.image_processor(
							    __lowerCAmelCase       ,		return_tensors=__lowerCAmelCase       ,		**__lowerCAmelCase       ,		)

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

							if hasattr(__lowerCAmelCase       ,		'numpy'      ):  # Checks if Torch or TF tensor
													__snake_case						 =   original_sizes.numpy()

							__snake_case ,       __snake_case ,       __snake_case						 =   self._check_and_preprocess_points(
							    input_points=__lowerCAmelCase       ,		input_labels=__lowerCAmelCase       ,		input_boxes=__lowerCAmelCase       ,		)

							__snake_case						 =   self._normalize_and_convert(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		input_points=__lowerCAmelCase       ,		input_labels=__lowerCAmelCase       ,		input_boxes=__lowerCAmelCase       ,		return_tensors=__lowerCAmelCase       ,		)

							return encoding_image_processor




	def   lowercase__   (       self     :  int       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  List[Any]=None       ,		__lowerCAmelCase     :  List[Any]=None       ,		__lowerCAmelCase     :  Optional[Any]=None       ,		__lowerCAmelCase     :  List[str]="pt"       ,		):
							if input_points is not None:
													if len(__lowerCAmelCase      ) != len(__lowerCAmelCase      ):
																			__snake_case						 =   [
																			    self._normalize_coordinates(self.target_size       ,		__lowerCAmelCase       ,		original_sizes[0]      ) for point in input_points
																			]
													else:
																			__snake_case						 =   [
																			    self._normalize_coordinates(self.target_size       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )
																			    for point, original_size in zip(__lowerCAmelCase       ,		__lowerCAmelCase      )
																			]
													# 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:
																									__snake_case ,       __snake_case						 =   self._pad_points_and_labels(__lowerCAmelCase       ,		__lowerCAmelCase      )

													__snake_case						 =   np.array(__lowerCAmelCase      )

							if input_labels is not None:
													__snake_case						 =   np.array(__lowerCAmelCase      )

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

							if input_boxes is not None:
													if return_tensors == "pt":
																			__snake_case						 =   torch.from_numpy(__lowerCAmelCase      )
																			# boxes batch size of 1 by default
																			__snake_case						 =   input_boxes.unsqueeze(1      ) if len(input_boxes.shape      ) != 3 else input_boxes
													elif return_tensors == "tf":
																			__snake_case						 =   tf.convert_to_tensor(__lowerCAmelCase      )
																			# boxes batch size of 1 by default
																			__snake_case						 =   tf.expand_dims(__lowerCAmelCase       ,		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":
																			__snake_case						 =   torch.from_numpy(__lowerCAmelCase      )
																			# point batch size of 1 by default
																			__snake_case						 =   input_points.unsqueeze(1      ) if len(input_points.shape      ) != 4 else input_points
													elif return_tensors == "tf":
																			__snake_case						 =   tf.convert_to_tensor(__lowerCAmelCase      )
																			# point batch size of 1 by default
																			__snake_case						 =   tf.expand_dims(__lowerCAmelCase       ,		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":
																			__snake_case						 =   torch.from_numpy(__lowerCAmelCase      )
																			# point batch size of 1 by default
																			__snake_case						 =   input_labels.unsqueeze(1      ) if len(input_labels.shape      ) != 3 else input_labels
													elif return_tensors == "tf":
																			__snake_case						 =   tf.convert_to_tensor(__lowerCAmelCase      )
																			# point batch size of 1 by default
																			__snake_case						 =   tf.expand_dims(__lowerCAmelCase       ,		1      ) if len(input_labels.shape      ) != 3 else input_labels
													encoding_image_processor.update({'input_labels': input_labels}      )

							return encoding_image_processor




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Union[str, Any]      ):
							__snake_case						 =   max([point.shape[0] for point in input_points]      )
							__snake_case						 =   []
							for i, point in enumerate(__lowerCAmelCase      ):
													if point.shape[0] != expected_nb_points:
																			__snake_case						 =   np.concatenate(
																			    [point, np.zeros((expected_nb_points - point.shape[0], 2)      ) + self.point_pad_value]       ,		axis=0      )
																			__snake_case						 =   np.append(input_labels[i]       ,		[self.point_pad_value]      )
													processed_input_points.append(__lowerCAmelCase      )
							__snake_case						 =   processed_input_points
							return input_points, input_labels




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  np.ndarray       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Any=False      ):
							__snake_case ,       __snake_case						 =   original_size
							__snake_case ,       __snake_case						 =   self.image_processor._get_preprocess_shape(__lowerCAmelCase       ,		longest_edge=__lowerCAmelCase      )
							__snake_case						 =   deepcopy(__lowerCAmelCase      ).astype(__lowerCAmelCase      )

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

							__snake_case						 =   coords[..., 0] * (new_w / old_w)
							__snake_case						 =   coords[..., 1] * (new_h / old_h)

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

							return coords




	def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]=None       ,		__lowerCAmelCase     :  Dict=None       ,		__lowerCAmelCase     :  List[Any]=None       ,		):
							if input_points is not None:
													if hasattr(__lowerCAmelCase       ,		'numpy'      ):  # Checks for TF or Torch tensor
																			__snake_case						 =   input_points.numpy().tolist()

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

							if input_labels is not None:
													if hasattr(__lowerCAmelCase       ,		'numpy'      ):
																			__snake_case						 =   input_labels.numpy().tolist()

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

							if input_boxes is not None:
													if hasattr(__lowerCAmelCase       ,		'numpy'      ):
																			__snake_case						 =   input_boxes.numpy().tolist()

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

							return input_points, input_labels, input_boxes




	@property
	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.image_processor.model_input_names
							return list(dict.fromkeys(__lowerCAmelCase      )      )




	def   lowercase__   (       self     :  List[str]       ,		*__lowerCAmelCase     :  str       ,		**__lowerCAmelCase     :  int      ):
							return self.image_processor.post_process_masks(*__lowerCAmelCase       ,		**__lowerCAmelCase      )







'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   [0] * len(a							)
						__snake_case						 =   []
						__snake_case						 =   []
						__snake_case						 =   0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(a							)							):
												if indegree[i] == 0:
																		queue.append(a							)

						while queue:
												__snake_case						 =   queue.pop(0							)
												cnt += 1
												topo.append(a							)
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(a							)

						if cnt != len(a							):
												print('Cycle exists'							)
						else:
												print(a							)


# Adjacency List of Graph
_lowercase  =			{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}






topological_sort(graph)

