
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 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[str]       	=      ['image_processor']
	a     :Tuple       	=      'SamImageProcessor'
	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     int			)			->	List[str]:
					super().__init__(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   self.image_processor
					lowercase_	    =   -1_0
					lowercase_	    =   self.image_processor.size['''longest_edge''']
	def __call__(			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     Any=None			,				SCREAMING_SNAKE_CASE_    :     str=None			,				SCREAMING_SNAKE_CASE_    :     Tuple=None			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, TensorType]] = None			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			,				)			->	BatchEncoding:
					lowercase_	    =   self.image_processor(
					    SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

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

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

					lowercase_    ,      lowercase_    ,      lowercase_	    =   self._check_and_preprocess_points(
					    input_points=SCREAMING_SNAKE_CASE_			,				input_labels=SCREAMING_SNAKE_CASE_			,				input_boxes=SCREAMING_SNAKE_CASE_			,				)

					lowercase_	    =   self._normalize_and_convert(
					    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				input_points=SCREAMING_SNAKE_CASE_			,				input_labels=SCREAMING_SNAKE_CASE_			,				input_boxes=SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_			,				)

					return encoding_image_processor
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     str=None			,				SCREAMING_SNAKE_CASE_    :     int=None			,				SCREAMING_SNAKE_CASE_    :     Tuple=None			,				SCREAMING_SNAKE_CASE_    :     List[str]="pt"			,				)			->	Dict:
					if input_points is not None:
									if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
													lowercase_	    =   [
													    self._normalize_coordinates(self.target_size			,				SCREAMING_SNAKE_CASE_			,				original_sizes[0]			) for point in input_points
													]
									else:
													lowercase_	    =   [
													    self._normalize_coordinates(self.target_size			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
													    for point, original_size in zip(SCREAMING_SNAKE_CASE_			,				SCREAMING_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:
																	lowercase_    ,      lowercase_	    =   self._pad_points_and_labels(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   np.array(SCREAMING_SNAKE_CASE_			)

					if input_labels is not None:
									lowercase_	    =   np.array(SCREAMING_SNAKE_CASE_			)

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

					if input_boxes is not None:
									if return_tensors == "pt":
													lowercase_	    =   torch.from_numpy(SCREAMING_SNAKE_CASE_			)
													# boxes batch size of 1 by default
													lowercase_	    =   input_boxes.unsqueeze(1			) if len(input_boxes.shape			) != 3 else input_boxes
									elif return_tensors == "tf":
													lowercase_	    =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE_			)
													# boxes batch size of 1 by default
													lowercase_	    =   tf.expand_dims(SCREAMING_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":
													lowercase_	    =   torch.from_numpy(SCREAMING_SNAKE_CASE_			)
													# point batch size of 1 by default
													lowercase_	    =   input_points.unsqueeze(1			) if len(input_points.shape			) != 4 else input_points
									elif return_tensors == "tf":
													lowercase_	    =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE_			)
													# point batch size of 1 by default
													lowercase_	    =   tf.expand_dims(SCREAMING_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":
													lowercase_	    =   torch.from_numpy(SCREAMING_SNAKE_CASE_			)
													# point batch size of 1 by default
													lowercase_	    =   input_labels.unsqueeze(1			) if len(input_labels.shape			) != 3 else input_labels
									elif return_tensors == "tf":
													lowercase_	    =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE_			)
													# point batch size of 1 by default
													lowercase_	    =   tf.expand_dims(SCREAMING_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 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Dict			)			->	Union[str, Any]:
					lowercase_	    =   max([point.shape[0] for point in input_points]			)
					lowercase_	    =   []
					for i, point in enumerate(SCREAMING_SNAKE_CASE_			):
									if point.shape[0] != expected_nb_points:
													lowercase_	    =   np.concatenate(
													    [point, np.zeros((expected_nb_points - point.shape[0], 2)			) + self.point_pad_value]			,				axis=0			)
													lowercase_	    =   np.append(input_labels[i]			,				[self.point_pad_value]			)
									processed_input_points.append(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   processed_input_points
					return input_points, input_labels
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Dict=False			)			->	np.ndarray:
					lowercase_    ,      lowercase_	    =   original_size
					lowercase_    ,      lowercase_	    =   self.image_processor._get_preprocess_shape(SCREAMING_SNAKE_CASE_			,				longest_edge=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   deepcopy(SCREAMING_SNAKE_CASE_			).astype(SCREAMING_SNAKE_CASE_			)

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

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

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

					return coords
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=None			,				SCREAMING_SNAKE_CASE_    :     Tuple=None			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=None			,				)			->	List[str]:
					if input_points is not None:
									if hasattr(SCREAMING_SNAKE_CASE_			,				'''numpy'''			):  # Checks for TF or Torch tensor
													lowercase_	    =   input_points.numpy().tolist()

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

					if input_labels is not None:
									if hasattr(SCREAMING_SNAKE_CASE_			,				'''numpy'''			):
													lowercase_	    =   input_labels.numpy().tolist()

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

					if input_boxes is not None:
									if hasattr(SCREAMING_SNAKE_CASE_			,				'''numpy'''			):
													lowercase_	    =   input_boxes.numpy().tolist()

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

					return input_points, input_labels, input_boxes
	@property
	def 		_lowercase    (			self    :     int			)			->	List[Any]:
					lowercase_	    =   self.image_processor.model_input_names
					return list(dict.fromkeys(SCREAMING_SNAKE_CASE_			)			)




	def 		_lowercase    (			self    :     Optional[int]			,				*SCREAMING_SNAKE_CASE_    :     str			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Tuple:
					return self.image_processor.post_process_masks(*SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)






import unittest

from knapsack import knapsack as k
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     Optional[int]			)			->	str:
					lowercase_	    =   0
					lowercase_	    =   [0]
					lowercase_	    =   [0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)

					lowercase_	    =   [6_0]
					lowercase_	    =   [1_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)
	def 		_lowercase    (			self    :     int			)			->	str:
					lowercase_	    =   3
					lowercase_	    =   [1, 2, 3]
					lowercase_	    =   [3, 2, 1]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				5			)




	def 		_lowercase    (			self    :     str			)			->	Tuple:
					lowercase_	    =   5_0
					lowercase_	    =   [6_0, 1_0_0, 1_2_0]
					lowercase_	    =   [1_0, 2_0, 3_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				2_2_0			)


if __name__ == "__main__":
						unittest.main()




