






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_ ):
  lowercase_						:			List[str]						=					['''image_processor''']
  lowercase_						:			Optional[Any]						=					'''SamImageProcessor'''

  def __init__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	Optional[Any]			):
     """simple docstring"""

     super().__init__(lowerCamelCase__			)
     __UpperCamelCase	:			int					=					self.image_processor
     __UpperCamelCase	:			Optional[Any]					=					-10
     __UpperCamelCase	:			List[Any]					=					self.image_processor.size["""longest_edge"""]

  def __call__(    self						:	int   ,				lowerCamelCase__						:	Dict=None   ,				lowerCamelCase__						:	List[Any]=None   ,				lowerCamelCase__						:	Optional[int]=None   ,				lowerCamelCase__						:	List[Any]=None   ,				lowerCamelCase__						:	Optional[Union[str, TensorType]] = None   ,				**lowerCamelCase__						:	Union[str, Any]   ,				):
     """simple docstring"""

     __UpperCamelCase	:			int					=					self.image_processor(
         lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)

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

     if hasattr(lowerCamelCase__   ,				"""numpy"""			):  # Checks if Torch or TF tensor
        __UpperCamelCase	:			int					=					original_sizes.numpy()

     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			List[str]					=					self._check_and_preprocess_points(
         input_points=lowerCamelCase__   ,				input_labels=lowerCamelCase__   ,				input_boxes=lowerCamelCase__   ,				)

     __UpperCamelCase	:			int					=					self._normalize_and_convert(
         lowerCamelCase__   ,				lowerCamelCase__   ,				input_points=lowerCamelCase__   ,				input_labels=lowerCamelCase__   ,				input_boxes=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				)

     return encoding_image_processor

  def    a					(    self						:	Dict   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	Any   ,				lowerCamelCase__						:	str=None   ,				lowerCamelCase__						:	List[str]=None   ,				lowerCamelCase__						:	Any=None   ,				lowerCamelCase__						:	str="pt"   ,				):
     """simple docstring"""

     if input_points is not None:
        if len(lowerCamelCase__			) != len(lowerCamelCase__			):
           __UpperCamelCase	:			Optional[Any]					=					[
               self._normalize_coordinates(self.target_size   ,				lowerCamelCase__   ,				original_sizes[0]			) for point in input_points
           ]
        else:
           __UpperCamelCase	:			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:
              __UpperCamelCase	,     __UpperCamelCase	:			Dict					=					self._pad_points_and_labels(lowerCamelCase__   ,				lowerCamelCase__			)

        __UpperCamelCase	:			Union[str, Any]					=					np.array(lowerCamelCase__			)

     if input_labels is not None:
        __UpperCamelCase	:			str					=					np.array(lowerCamelCase__			)

     if input_boxes is not None:
        if len(lowerCamelCase__			) != len(lowerCamelCase__			):
           __UpperCamelCase	:			Optional[int]					=					[
               self._normalize_coordinates(self.target_size   ,				lowerCamelCase__   ,				original_sizes[0]   ,				is_bounding_box=lowerCamelCase__			)
               for box in input_boxes
           ]
        else:
           __UpperCamelCase	:			Tuple					=					[
               self._normalize_coordinates(self.target_size   ,				lowerCamelCase__   ,				lowerCamelCase__   ,				is_bounding_box=lowerCamelCase__			)
               for box, original_size in zip(lowerCamelCase__   ,				lowerCamelCase__			)
           ]
        __UpperCamelCase	:			Any					=					np.array(lowerCamelCase__			)

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

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

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	np.ndarray   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	Tuple=False			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			List[Any]					=					original_size
     __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					self.image_processor._get_preprocess_shape(lowerCamelCase__   ,				longest_edge=lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					deepcopy(lowerCamelCase__			).astype(lowerCamelCase__			)

     if is_bounding_box:
        __UpperCamelCase	:			Optional[int]					=					coords.reshape(-1   ,				2   ,				2			)

     __UpperCamelCase	:			Dict					=					coords[..., 0] * (new_w / old_w)
     __UpperCamelCase	:			List[Any]					=					coords[..., 1] * (new_h / old_h)

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

     return coords

  def    a					(    self						:	int   ,				lowerCamelCase__						:	List[str]=None   ,				lowerCamelCase__						:	Union[str, Any]=None   ,				lowerCamelCase__						:	List[Any]=None   ,				):
     """simple docstring"""

     if input_points is not None:
        if hasattr(lowerCamelCase__   ,				"""numpy"""			):  # Checks for TF or Torch tensor
           __UpperCamelCase	:			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."""			)
        __UpperCamelCase	:			Optional[Any]					=					[np.array(lowerCamelCase__			) for input_point in input_points]
     else:
        __UpperCamelCase	:			Union[str, Any]					=					None

     if input_labels is not None:
        if hasattr(lowerCamelCase__   ,				"""numpy"""			):
           __UpperCamelCase	:			Optional[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."""			)
        __UpperCamelCase	:			Any					=					[np.array(lowerCamelCase__			) for label in input_labels]
     else:
        __UpperCamelCase	:			List[Any]					=					None

     if input_boxes is not None:
        if hasattr(lowerCamelCase__   ,				"""numpy"""			):
           __UpperCamelCase	:			Union[str, 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."""			)
        __UpperCamelCase	:			Optional[int]					=					[np.array(lowerCamelCase__			).astype(np.floataa			) for box in input_boxes]
     else:
        __UpperCamelCase	:			Optional[Any]					=					None

     return input_points, input_labels, input_boxes

  @property
  def    a					(    self						:	List[str]			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					self.image_processor.model_input_names
     return list(dict.fromkeys(lowerCamelCase__			)			)

  def    a					(    self						:	Union[str, Any]   ,				*lowerCamelCase__						:	str   ,				**lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

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











from collections.abc import Iterable
from typing import Generic, TypeVar

UpperCamelCase    							=      TypeVar('_T')







class 		_A						( Generic[_T] ):

  def __init__(    self						:	int   ,				lowerCamelCase__						:	Iterable[_T] | None = None			):
     """simple docstring"""

     __UpperCamelCase	:			list[_T]					=					list(iterable or []			)
     __UpperCamelCase	:			list[_T]					=					[]

  def __len__(    self						:	str			):
     """simple docstring"""

     return len(self._stacka			) + len(self._stacka			)

  def __repr__(    self						:	Dict			):
     """simple docstring"""

     return f'Queue({tuple(self._stacka[::-1] + self._stacka			)})'

  def    a					(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	_T			):
     """simple docstring"""

     self._stacka.append(lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					self._stacka.pop
     __UpperCamelCase	:			int					=					self._stacka.append

     if not self._stacka:
        while self._stacka:
           stacka_append(stacka_pop()			)

     if not self._stacka:
        raise IndexError("""Queue is empty"""			)
     return self._stacka.pop()


if __name__ == "__main__":
       from doctest import testmod

       testmod()



