



"""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  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	['''image_processor''']
       UpperCamelCase		=	'''SamImageProcessor'''



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


             '''simple docstring'''




             super().__init__(_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.image_processor
             UpperCAmelCase_     					=				-10
             UpperCAmelCase_     					=				self.image_processor.size["longest_edge"]



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


             '''simple docstring'''




             UpperCAmelCase_     					=				self.image_processor(
                 _UpperCAmelCase						,							return_tensors=_UpperCAmelCase						,							**_UpperCAmelCase						,							)

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

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

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

             UpperCAmelCase_     					=				self._normalize_and_convert(
                 _UpperCAmelCase						,							_UpperCAmelCase						,							input_points=_UpperCAmelCase						,							input_labels=_UpperCAmelCase						,							input_boxes=_UpperCAmelCase						,							return_tensors=_UpperCAmelCase						,							)

             return encoding_image_processor



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


             '''simple docstring'''




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

                   UpperCAmelCase_     					=				np.array(_UpperCAmelCase       )

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

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

             if input_boxes is not None:
                   if return_tensors == "pt":
                         UpperCAmelCase_     					=				torch.from_numpy(_UpperCAmelCase       )
                         # 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(_UpperCAmelCase       )
                         # boxes batch size of 1 by default
                         UpperCAmelCase_     					=				tf.expand_dims(_UpperCAmelCase						,							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(_UpperCAmelCase       )
                         # 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(_UpperCAmelCase       )
                         # point batch size of 1 by default
                         UpperCAmelCase_     					=				tf.expand_dims(_UpperCAmelCase						,							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(_UpperCAmelCase       )
                         # 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(_UpperCAmelCase       )
                         # point batch size of 1 by default
                         UpperCAmelCase_     					=				tf.expand_dims(_UpperCAmelCase						,							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     :    Dict						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    Optional[Any]       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				max([point.shape[0] for point in input_points]       )
             UpperCAmelCase_     					=				[]
             for i, point in enumerate(_UpperCAmelCase       ):
                   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(_UpperCAmelCase       )
             UpperCAmelCase_     					=				processed_input_points
             return input_points, input_labels



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


             '''simple docstring'''




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

             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 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    Dict=None						,							_UpperCAmelCase     :    Optional[Any]=None						,							_UpperCAmelCase     :    Dict=None						,							)					->		List[str]:


             '''simple docstring'''




             if input_points is not None:
                   if hasattr(_UpperCAmelCase						,							"numpy"       ):  # Checks for TF or Torch tensor
                         UpperCAmelCase_     					=				input_points.numpy().tolist()

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

             if input_labels is not None:
                   if hasattr(_UpperCAmelCase						,							"numpy"       ):
                         UpperCAmelCase_     					=				input_labels.numpy().tolist()

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

             if input_boxes is not None:
                   if hasattr(_UpperCAmelCase						,							"numpy"       ):
                         UpperCAmelCase_     					=				input_boxes.numpy().tolist()

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

             return input_points, input_labels, input_boxes



       @property
       def 	lowercase__							(				self     :    Union[str, Any]       )					->		Any:


             '''simple docstring'''




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







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


             '''simple docstring'''




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









def        __a			(   __lowerCAmelCase   )     ->     List[str]:
					stooge(__lowerCAmelCase      ,	0      ,	len(__lowerCAmelCase   ) - 1   )
					return arr

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     int:
					if i >= h:
										return

					# If first element is smaller than the last then swap them
					if arr[i] > arr[h]:
										SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				arr[h], arr[i]

					# If there are more than 2 elements in the array
					if h - i + 1 > 2:
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				(int)((h - i + 1) / 3   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )

										# Recursively sort last 2/3 elements
										stooge(__lowerCAmelCase      ,	i + t      ,	(__lowerCAmelCase)   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )


if __name__ == "__main__":
			_lowerCamelCase    :		List[str]		      =    input("""Enter numbers separated by a comma:\n""").strip()
			_lowerCamelCase    :		List[Any]		      =    [int(item) for item in user_input.split(""",""")]
			print(stooge_sort(unsorted))