



'''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 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Any			    =		['''image_processor''']
			_A      :		int			    =		'''SamImageProcessor'''

			def __init__(							self     : int							,     lowerCAmelCase__     : Dict						):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		str           =		self.image_processor
					__SCREAMING_SNAKE_CASE       :		Any           =		-1_0
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.image_processor.size["""longest_edge"""]

			def __call__(							self     : List[Any]							,     lowerCAmelCase__     : List[Any]=None							,     lowerCAmelCase__     : Optional[int]=None							,     lowerCAmelCase__     : List[str]=None							,     lowerCAmelCase__     : List[str]=None							,     lowerCAmelCase__     : Optional[Union[str, TensorType]] = None							,     **lowerCAmelCase__     : List[str]							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.image_processor(
					    lowerCAmelCase__							,     return_tensors=lowerCAmelCase__							,     **lowerCAmelCase__							,     )

					# pop arguments that are not used in the foward but used nevertheless
					__SCREAMING_SNAKE_CASE       :		Tuple           =		encoding_image_processor["""original_sizes"""]

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

					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		List[Any]           =		self._check_and_preprocess_points(
					    input_points=lowerCAmelCase__							,     input_labels=lowerCAmelCase__							,     input_boxes=lowerCAmelCase__							,     )

					__SCREAMING_SNAKE_CASE       :		List[str]           =		self._normalize_and_convert(
					    lowerCAmelCase__							,     lowerCAmelCase__							,     input_points=lowerCAmelCase__							,     input_labels=lowerCAmelCase__							,     input_boxes=lowerCAmelCase__							,     return_tensors=lowerCAmelCase__							,     )

					return encoding_image_processor

			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : Any=None							,     lowerCAmelCase__     : Any=None							,     lowerCAmelCase__     : Dict=None							,     lowerCAmelCase__     : List[str]="pt"							,     ):





					"""simple docstring"""






					if input_points is not None:
							if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
									__SCREAMING_SNAKE_CASE       :		Dict           =		[
									    self._normalize_coordinates(self.target_size							,     lowerCAmelCase__							,     original_sizes[0]						) for point in input_points
									]
							else:
									__SCREAMING_SNAKE_CASE       :		str           =		[
									    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:
											__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self._pad_points_and_labels(lowerCAmelCase__							,     lowerCAmelCase__						)

							__SCREAMING_SNAKE_CASE       :		int           =		np.array(lowerCAmelCase__						)

					if input_labels is not None:
							__SCREAMING_SNAKE_CASE       :		Dict           =		np.array(lowerCAmelCase__						)

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

					if input_boxes is not None:
							if return_tensors == "pt":
									__SCREAMING_SNAKE_CASE       :		Optional[int]           =		torch.from_numpy(lowerCAmelCase__						)
									# boxes batch size of 1 by default
									__SCREAMING_SNAKE_CASE       :		List[Any]           =		input_boxes.unsqueeze(1						) if len(input_boxes.shape						) != 3 else input_boxes
							elif return_tensors == "tf":
									__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		tf.convert_to_tensor(lowerCAmelCase__						)
									# boxes batch size of 1 by default
									__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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":
									__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		torch.from_numpy(lowerCAmelCase__						)
									# point batch size of 1 by default
									__SCREAMING_SNAKE_CASE       :		List[Any]           =		input_points.unsqueeze(1						) if len(input_points.shape						) != 4 else input_points
							elif return_tensors == "tf":
									__SCREAMING_SNAKE_CASE       :		Any           =		tf.convert_to_tensor(lowerCAmelCase__						)
									# point batch size of 1 by default
									__SCREAMING_SNAKE_CASE       :		int           =		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":
									__SCREAMING_SNAKE_CASE       :		List[Any]           =		torch.from_numpy(lowerCAmelCase__						)
									# point batch size of 1 by default
									__SCREAMING_SNAKE_CASE       :		List[str]           =		input_labels.unsqueeze(1						) if len(input_labels.shape						) != 3 else input_labels
							elif return_tensors == "tf":
									__SCREAMING_SNAKE_CASE       :		str           =		tf.convert_to_tensor(lowerCAmelCase__						)
									# point batch size of 1 by default
									__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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 UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : np.ndarray							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : Dict=False						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[int]           =		original_size
					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		self.image_processor._get_preprocess_shape(lowerCAmelCase__							,     longest_edge=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		deepcopy(lowerCAmelCase__						).astype(lowerCAmelCase__						)

					if is_bounding_box:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		coords.reshape(-1							,     2							,     2						)

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

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

					return coords

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : Dict=None							,     lowerCAmelCase__     : str=None							,     lowerCAmelCase__     : List[Any]=None							,     ):





					"""simple docstring"""






					if input_points is not None:
							if hasattr(lowerCAmelCase__							,     """numpy"""						):  # Checks for TF or Torch tensor
									__SCREAMING_SNAKE_CASE       :		List[str]           =		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."""						)
							__SCREAMING_SNAKE_CASE       :		Any           =		[np.array(lowerCAmelCase__						) for input_point in input_points]
					else:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		None

					if input_labels is not None:
							if hasattr(lowerCAmelCase__							,     """numpy"""						):
									__SCREAMING_SNAKE_CASE       :		int           =		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."""						)
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[np.array(lowerCAmelCase__						) for label in input_labels]
					else:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		None

					if input_boxes is not None:
							if hasattr(lowerCAmelCase__							,     """numpy"""						):
									__SCREAMING_SNAKE_CASE       :		Any           =		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."""						)
							__SCREAMING_SNAKE_CASE       :		Any           =		[np.array(lowerCAmelCase__						).astype(np.floataa						) for box in input_boxes]
					else:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		None

					return input_points, input_labels, input_boxes

			@property
			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		self.image_processor.model_input_names
					return list(dict.fromkeys(lowerCAmelCase__						)						)



			def UpperCamelCase__					(							self     : Tuple							,     *lowerCAmelCase__     : str							,     **lowerCAmelCase__     : Any						):





					"""simple docstring"""






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




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list					):
		if any(not isinstance(_lowerCamelCase       ,      _lowerCamelCase					) or x < 0 for x in sequence					):
				raise TypeError("""Sequence must be list of non-negative integers"""					)
		for _ in range(len(_lowerCamelCase					)					):
				for i, (rod_upper, rod_lower) in enumerate(zip(_lowerCamelCase       ,      sequence[1:]					)					):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
						assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]