


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      ):
		_a     :  List[str]           =     ['image_processor']
		_a     :  Optional[Any]           =     'SamImageProcessor'
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.image_processor
									_UpperCAmelCase					=      -10
									_UpperCAmelCase					=      self.image_processor.size['longest_edge']
		def __call__(      self      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	BatchEncoding:




									"""simple docstring"""





									_UpperCAmelCase					=      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
									_UpperCAmelCase					=      encoding_image_processor['original_sizes']

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

									_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      self._check_and_preprocess_points(
									    input_points=_SCREAMING_SNAKE_CASE      ,   input_labels=_SCREAMING_SNAKE_CASE      ,   input_boxes=_SCREAMING_SNAKE_CASE      ,   )

									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE="pt"      ,   )			->	Optional[int]:




									"""simple docstring"""





									if input_points is not None:
																if len(_SCREAMING_SNAKE_CASE						) != len(_SCREAMING_SNAKE_CASE						):
																							_UpperCAmelCase					=      [
																							    self._normalize_coordinates(self.target_size      ,   _SCREAMING_SNAKE_CASE      ,   original_sizes[0]						) for point in input_points
																							]
																else:
																							_UpperCAmelCase					=      [
																							    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:
																														_UpperCAmelCase		,	_UpperCAmelCase					=      self._pad_points_and_labels(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      np.array(_SCREAMING_SNAKE_CASE						)

									if input_labels is not None:
																_UpperCAmelCase					=      np.array(_SCREAMING_SNAKE_CASE						)

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

									if input_boxes is not None:
																if return_tensors == "pt":
																							_UpperCAmelCase					=      torch.from_numpy(_SCREAMING_SNAKE_CASE						)
																							# boxes batch size of 1 by default
																							_UpperCAmelCase					=      input_boxes.unsqueeze(1						) if len(input_boxes.shape						) != 3 else input_boxes
																elif return_tensors == "tf":
																							_UpperCAmelCase					=      tf.convert_to_tensor(_SCREAMING_SNAKE_CASE						)
																							# boxes batch size of 1 by default
																							_UpperCAmelCase					=      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":
																							_UpperCAmelCase					=      torch.from_numpy(_SCREAMING_SNAKE_CASE						)
																							# point batch size of 1 by default
																							_UpperCAmelCase					=      input_points.unsqueeze(1						) if len(input_points.shape						) != 4 else input_points
																elif return_tensors == "tf":
																							_UpperCAmelCase					=      tf.convert_to_tensor(_SCREAMING_SNAKE_CASE						)
																							# point batch size of 1 by default
																							_UpperCAmelCase					=      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":
																							_UpperCAmelCase					=      torch.from_numpy(_SCREAMING_SNAKE_CASE						)
																							# point batch size of 1 by default
																							_UpperCAmelCase					=      input_labels.unsqueeze(1						) if len(input_labels.shape						) != 3 else input_labels
																elif return_tensors == "tf":
																							_UpperCAmelCase					=      tf.convert_to_tensor(_SCREAMING_SNAKE_CASE						)
																							# point batch size of 1 by default
																							_UpperCAmelCase					=      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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      max([point.shape[0] for point in input_points]						)
									_UpperCAmelCase					=      []
									for i, point in enumerate(_SCREAMING_SNAKE_CASE						):
																if point.shape[0] != expected_nb_points:
																							_UpperCAmelCase					=      np.concatenate(
																							    [point, np.zeros((expected_nb_points - point.shape[0], 2)						) + self.point_pad_value]      ,   axis=0						)
																							_UpperCAmelCase					=      np.append(input_labels[i]      ,   [self.point_pad_value]						)
																processed_input_points.append(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      processed_input_points
									return input_points, input_labels
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=False						)			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      original_size
									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor._get_preprocess_shape(_SCREAMING_SNAKE_CASE      ,   longest_edge=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      deepcopy(_SCREAMING_SNAKE_CASE						).astype(_SCREAMING_SNAKE_CASE						)

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

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

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

									return coords
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   )			->	Dict:




									"""simple docstring"""





									if input_points is not None:
																if hasattr(_SCREAMING_SNAKE_CASE      ,   'numpy'						):  # Checks for TF or Torch tensor
																							_UpperCAmelCase					=      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.'						)
																_UpperCAmelCase					=      [np.array(_SCREAMING_SNAKE_CASE						) for input_point in input_points]
									else:
																_UpperCAmelCase					=      None

									if input_labels is not None:
																if hasattr(_SCREAMING_SNAKE_CASE      ,   'numpy'						):
																							_UpperCAmelCase					=      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.'						)
																_UpperCAmelCase					=      [np.array(_SCREAMING_SNAKE_CASE						) for label in input_labels]
									else:
																_UpperCAmelCase					=      None

									if input_boxes is not None:
																if hasattr(_SCREAMING_SNAKE_CASE      ,   'numpy'						):
																							_UpperCAmelCase					=      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.'						)
																_UpperCAmelCase					=      [np.array(_SCREAMING_SNAKE_CASE						).astype(np.floataa						) for box in input_boxes]
									else:
																_UpperCAmelCase					=      None

									return input_points, input_labels, input_boxes
		@property
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processor.model_input_names
									return list(dict.fromkeys(_SCREAMING_SNAKE_CASE						)						)






		def      UpperCAmelCase__    (      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





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




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]      ,  a__:		NDArray[floataa]      ,  a__:		list[int]      ,  a__:		int      ,  )  ->		list[float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      coefficient_matrix.shape
							_UpperCAmelCase		,	_UpperCAmelCase					=      constant_matrix.shape

							if rowsa != colsa:
														_UpperCAmelCase					=      F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if colsa != 1:
														_UpperCAmelCase					=      F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if rowsa != rowsa:
														_UpperCAmelCase					=      (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(a__	)

							if len(a__	) != rowsa:
														_UpperCAmelCase					=      (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(a__	)} and {rowsa}'''
														)
														raise ValueError(a__	)

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'	)

							_UpperCAmelCase					=      np.concatenate(
							    (coefficient_matrix, constant_matrix)      ,  axis=1	)

							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							strictly_diagonally_dominant(a__	)

							# Iterates the whole matrix for given number of times
							for _ in range(a__	):
														_UpperCAmelCase					=      []
														for row in range(a__	):
																					_UpperCAmelCase					=      0
																					for col in range(a__	):
																												if col == row:
																																			_UpperCAmelCase					=      table[row][col]
																												elif col == cols - 1:
																																			_UpperCAmelCase					=      table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					_UpperCAmelCase					=      (temp + val) / denom
																					new_val.append(a__	)
														_UpperCAmelCase					=      new_val

							return [float(a__	) for i in new_val]

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]	)  ->		bool:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							_UpperCAmelCase					=      True

							for i in range(0      ,  a__	):
														_UpperCAmelCase					=      0
														for j in range(0      ,  cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()
