





'''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 		lowerCamelCase  (      _A       ):
	snake_case_      =   ["image_processor"]
	snake_case_      =   "SamImageProcessor"



	def __init__(   self		,      a_	):
			super().__init__(a_	)
			lowerCAmelCase     :    Optional[Any]						=   self.image_processor
			lowerCAmelCase     :    int						=   -10
			lowerCAmelCase     :    Dict						=   self.image_processor.size["longest_edge"]



	def __call__(   self		,      a_=None		,      a_=None		,      a_=None		,      a_=None		,      a_ = None		,      **a_		,      ):
			lowerCAmelCase     :    List[str]						=   self.image_processor(
			    a_		,      return_tensors=a_		,      **a_		,      )

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

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

			lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   self._check_and_preprocess_points(
			    input_points=a_		,      input_labels=a_		,      input_boxes=a_		,      )

			lowerCAmelCase     :    str						=   self._normalize_and_convert(
			    a_		,      a_		,      input_points=a_		,      input_labels=a_		,      input_boxes=a_		,      return_tensors=a_		,      )

			return encoding_image_processor



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_=None		,      a_=None		,      a_=None		,      a_="pt"		,      ):
			if input_points is not None:
					if len(a_	) != len(a_	):
							lowerCAmelCase     :    Optional[int]						=   [
							    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	,		lowerCAmelCase     :    Tuple						=   self._pad_points_and_labels(a_		,      a_	)

					lowerCAmelCase     :    int						=   np.array(a_	)

			if input_labels is not None:
					lowerCAmelCase     :    str						=   np.array(a_	)

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

			if input_boxes is not None:
					if return_tensors == "pt":
							lowerCAmelCase     :    Tuple						=   torch.from_numpy(a_	)
							# boxes batch size of 1 by default
							lowerCAmelCase     :    Tuple						=   input_boxes.unsqueeze(1	) if len(input_boxes.shape	) != 3 else input_boxes
					elif return_tensors == "tf":
							lowerCAmelCase     :    Any						=   tf.convert_to_tensor(a_	)
							# boxes batch size of 1 by default
							lowerCAmelCase     :    int						=   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     :    Any						=   input_points.unsqueeze(1	) if len(input_points.shape	) != 4 else input_points
					elif return_tensors == "tf":
							lowerCAmelCase     :    Optional[Any]						=   tf.convert_to_tensor(a_	)
							# point batch size of 1 by default
							lowerCAmelCase     :    int						=   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     :    Dict						=   torch.from_numpy(a_	)
							# point batch size of 1 by default
							lowerCAmelCase     :    Any						=   input_labels.unsqueeze(1	) if len(input_labels.shape	) != 3 else input_labels
					elif return_tensors == "tf":
							lowerCAmelCase     :    List[str]						=   tf.convert_to_tensor(a_	)
							# point batch size of 1 by default
							lowerCAmelCase     :    Union[str, Any]						=   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   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Tuple						=   max([point.shape[0] for point in input_points]	)
			lowerCAmelCase     :    Union[str, Any]						=   []
			for i, point in enumerate(a_	):
					if point.shape[0] != expected_nb_points:
							lowerCAmelCase     :    Optional[Any]						=   np.concatenate(
							    [point, np.zeros((expected_nb_points - point.shape[0], 2)	) + self.point_pad_value]		,      axis=0	)
							lowerCAmelCase     :    Any						=   np.append(input_labels[i]		,      [self.point_pad_value]	)
					processed_input_points.append(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   processed_input_points
			return input_points, input_labels



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_=False	):
			lowerCAmelCase	,		lowerCAmelCase     :    Any						=   original_size
			lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   self.image_processor._get_preprocess_shape(a_		,      longest_edge=a_	)
			lowerCAmelCase     :    Optional[int]						=   deepcopy(a_	).astype(a_	)

			if is_bounding_box:
					lowerCAmelCase     :    Union[str, Any]						=   coords.reshape(-1		,      2		,      2	)

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

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

			return coords



	def   _lowerCamelCase    (   self		,      a_=None		,      a_=None		,      a_=None		,      ):
			if input_points is not None:
					if hasattr(a_		,      "numpy"	):  # Checks for TF or Torch tensor
							lowerCAmelCase     :    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     :    List[Any]						=   [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     :    Optional[int]						=   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     :    Dict						=   [np.array(a_	) for label in input_labels]
			else:
					lowerCAmelCase     :    Any						=   None

			if input_boxes is not None:
					if hasattr(a_		,      "numpy"	):
							lowerCAmelCase     :    Any						=   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     :    Union[str, Any]						=   [np.array(a_	).astype(np.floataa	) for box in input_boxes]
			else:
					lowerCAmelCase     :    Dict						=   None

			return input_points, input_labels, input_boxes



	@property
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   self.image_processor.model_input_names
			return list(dict.fromkeys(a_	)	)





	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.image_processor.post_process_masks(*a_		,      **a_	)










'''simple docstring'''






import numpy as np






def      __A						(							a_						:  np.array	):
		return (2 / (1 + np.exp(-2 * vector	))) - 1


if __name__ == "__main__":
					import doctest

					doctest.testmod()



