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        __snake_case  (_a		):
				lowerCAmelCase__				=     ["image_processor"]
				lowerCAmelCase__				=     "SamImageProcessor"



				def __init__(      self      :		Any		,	_UpperCAmelCase      :		Optional[int]      )  ->    int:





								'''simple docstring'''




								super().__init__(__A      )
								_lowerCAmelCase    :    Union[str, Any]										=      self.image_processor
								_lowerCAmelCase    :    Optional[int]										=      -10
								_lowerCAmelCase    :    List[str]										=      self.image_processor.size["longest_edge"]



				def __call__(      self      :		Any		,	_UpperCAmelCase      :		Tuple=None		,	_UpperCAmelCase      :		Any=None		,	_UpperCAmelCase      :		Optional[Any]=None		,	_UpperCAmelCase      :		Dict=None		,	_UpperCAmelCase      :		Optional[Union[str, TensorType]] = None		,	**_UpperCAmelCase      :		str		,	)  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.image_processor(
								    __A		,	return_tensors=__A		,	**__A		,	)

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

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

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

								_lowerCAmelCase    :    Union[str, Any]										=      self._normalize_and_convert(
								    __A		,	__A		,	input_points=__A		,	input_labels=__A		,	input_boxes=__A		,	return_tensors=__A		,	)

								return encoding_image_processor



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		List[str]=None		,	_UpperCAmelCase      :		int=None		,	_UpperCAmelCase      :		Any=None		,	_UpperCAmelCase      :		int="pt"		,	)  ->    Any:





								'''simple docstring'''




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

												_lowerCAmelCase    :    Optional[int]										=      np.array(__A      )

								if input_labels is not None:
												_lowerCAmelCase    :    List[Any]										=      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    :    List[str]										=      [
																    self._normalize_coordinates(self.target_size		,	__A		,	__A		,	is_bounding_box=__A      )
																    for box, original_size in zip(__A		,	__A      )
																]
												_lowerCAmelCase    :    Dict										=      np.array(__A      )

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





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Optional[int]=False      )  ->    str:





								'''simple docstring'''




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

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

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

								if is_bounding_box:
												_lowerCAmelCase    :    List[Any]										=      coords.reshape(-1		,	4      )

								return coords



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		str=None		,	_UpperCAmelCase      :		Tuple=None		,	_UpperCAmelCase      :		List[Any]=None		,	)  ->    Union[str, Any]:





								'''simple docstring'''




								if input_points is not None:
												if hasattr(__A		,	"""numpy"""      ):  # Checks for TF or Torch tensor
																_lowerCAmelCase    :    Optional[int]										=      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    :    str										=      [np.array(__A      ) for input_point in input_points]
								else:
												_lowerCAmelCase    :    List[str]										=      None

								if input_labels is not None:
												if hasattr(__A		,	"""numpy"""      ):
																_lowerCAmelCase    :    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    :    str										=      [np.array(__A      ) for label in input_labels]
								else:
												_lowerCAmelCase    :    int										=      None

								if input_boxes is not None:
												if hasattr(__A		,	"""numpy"""      ):
																_lowerCAmelCase    :    str										=      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    :    str										=      [np.array(__A      ).astype(np.floataa      ) for box in input_boxes]
								else:
												_lowerCAmelCase    :    str										=      None

								return input_points, input_labels, input_boxes



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      self.image_processor.model_input_names
								return list(dict.fromkeys(__A      )      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	*_UpperCAmelCase      :		int		,	**_UpperCAmelCase      :		Union[str, Any]      )  ->    Dict:





								'''simple docstring'''




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







from __future__ import annotations

from collections.abc import Iterator
from typing import Any
class        __snake_case  :



				def __init__(      self      :		List[Any]		,	_UpperCAmelCase      :		Any      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      data
								_lowerCAmelCase    :    Node | None										=      None






class        __snake_case  :



				def __init__(      self      :		Tuple      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      None
								_lowerCAmelCase    :    List[Any]										=      None



				def __iter__(      self      :		str      )  ->    Iterator[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.head
								while self.head:
												yield node.data
												_lowerCAmelCase    :    List[str]										=      node.next
												if node == self.head:
																break



				def __len__(      self      :		Union[str, Any]      )  ->    int:





								'''simple docstring'''




								return sum(1 for _ in self      )



				def __repr__(      self      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								return "->".join(str(_UpperCAmelCase      ) for item in iter(self      )      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Any      )  ->    None:





								'''simple docstring'''




								self.insert_nth(len(self      )		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		Any      )  ->    None:





								'''simple docstring'''




								self.insert_nth(0		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Any      )  ->    None:





								'''simple docstring'''




								if index < 0 or index > len(self      ):
												raise IndexError("""list index out of range."""      )
								_lowerCAmelCase    :    Optional[int]										=      Node(_UpperCAmelCase      )
								if self.head is None:
												_lowerCAmelCase    :    Tuple										=      new_node  # first node points itself
												_lowerCAmelCase    :    int										=      new_node
								elif index == 0:  # insert at head
												_lowerCAmelCase    :    Tuple										=      self.head
												_lowerCAmelCase    :    int										=      new_node
								else:
												_lowerCAmelCase    :    Union[str, Any]										=      self.head
												for _ in range(index - 1      ):
																_lowerCAmelCase    :    Optional[Any]										=      temp.next
												_lowerCAmelCase    :    Optional[int]										=      temp.next
												_lowerCAmelCase    :    Optional[int]										=      new_node
												if index == len(self      ) - 1:  # insert at tail
																_lowerCAmelCase    :    Optional[Any]										=      new_node



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								return self.delete_nth(0      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Any:





								'''simple docstring'''




								return self.delete_nth(len(self      ) - 1      )



				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		int = 0      )  ->    Any:





								'''simple docstring'''




								if not 0 <= index < len(self      ):
												raise IndexError("""list index out of range."""      )
								_lowerCAmelCase    :    Optional[Any]										=      self.head
								if self.head == self.tail:  # just one node
												_lowerCAmelCase    :    Optional[Any]										=      None
								elif index == 0:  # delete head node
												_lowerCAmelCase    :    str										=      self.tail.next.next
												_lowerCAmelCase    :    List[str]										=      self.head.next
								else:
												_lowerCAmelCase    :    List[str]										=      self.head
												for _ in range(index - 1      ):
																_lowerCAmelCase    :    List[Any]										=      temp.next
												_lowerCAmelCase    :    List[str]										=      temp.next
												_lowerCAmelCase    :    Dict										=      temp.next.next
												if index == len(self      ) - 1:  # delete at tail
																_lowerCAmelCase    :    Optional[int]										=      temp
								return delete_node.data



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    bool:





								'''simple docstring'''




								return len(self      ) == 0






def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Dict										=      CircularLinkedList()
				assert len(UpperCamelCase_       ) == 0
				assert circular_linked_list.is_empty() is True
				assert str(UpperCamelCase_       ) == ""

				try:
								circular_linked_list.delete_front()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_tail()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_nth(-1       )
								raise AssertionError
				except IndexError:
								assert True

				try:
								circular_linked_list.delete_nth(0       )
								raise AssertionError
				except IndexError:
								assert True

				assert circular_linked_list.is_empty() is True
				for i in range(5       ):
								assert len(UpperCamelCase_       ) == i
								circular_linked_list.insert_nth(UpperCamelCase_      ,    i + 1       )
				assert str(UpperCamelCase_       ) == "->".join(str(UpperCamelCase_       ) for i in range(1      ,    6       )       )

				circular_linked_list.insert_tail(6       )
				assert str(UpperCamelCase_       ) == "->".join(str(UpperCamelCase_       ) for i in range(1      ,    7       )       )
				circular_linked_list.insert_head(0       )
				assert str(UpperCamelCase_       ) == "->".join(str(UpperCamelCase_       ) for i in range(0      ,    7       )       )

				assert circular_linked_list.delete_front() == 0
				assert circular_linked_list.delete_tail() == 6
				assert str(UpperCamelCase_       ) == "->".join(str(UpperCamelCase_       ) for i in range(1      ,    6       )       )
				assert circular_linked_list.delete_nth(2       ) == 3

				circular_linked_list.insert_nth(2      ,    3       )
				assert str(UpperCamelCase_       ) == "->".join(str(UpperCamelCase_       ) for i in range(1      ,    6       )       )

				assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
	import doctest

	doctest.testmod()

