






"""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   UpperCamelCase_ (       __a  ):
  _A       :					Dict				=	['''image_processor''']
  _A       :					List[Any]				=	'''SamImageProcessor'''



  def __init__(      self ,		snake_case__				)			->			Any:



         """simple docstring"""





         super().__init__(snake_case__				)
         UpperCAmelCase	    =			self.image_processor
         UpperCAmelCase	    =			-10
         UpperCAmelCase	    =			self.image_processor.size["""longest_edge"""]



  def __call__(      self ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		snake_case__ = None ,		**snake_case__ ,		)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processor(
             snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__ ,		)

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

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

         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			self._check_and_preprocess_points(
             input_points=snake_case__ ,		input_labels=snake_case__ ,		input_boxes=snake_case__ ,		)

         UpperCAmelCase	    =			self._normalize_and_convert(
             snake_case__ ,		snake_case__ ,		input_points=snake_case__ ,		input_labels=snake_case__ ,		input_boxes=snake_case__ ,		return_tensors=snake_case__ ,		)

         return encoding_image_processor



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		snake_case__="pt" ,		)			->			Optional[int]:



         """simple docstring"""





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

                UpperCAmelCase	    =			np.array(snake_case__				)

         if input_labels is not None:
                UpperCAmelCase	    =			np.array(snake_case__				)

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

         if input_boxes is not None:
                if return_tensors == "pt":
                       UpperCAmelCase	    =			torch.from_numpy(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(snake_case__				)
                       # boxes batch size of 1 by default
                       UpperCAmelCase	    =			tf.expand_dims(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(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(snake_case__				)
                       # point batch size of 1 by default
                       UpperCAmelCase	    =			tf.expand_dims(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(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(snake_case__				)
                       # point batch size of 1 by default
                       UpperCAmelCase	    =			tf.expand_dims(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 ,		snake_case__ ,		snake_case__				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			max([point.shape[0] for point in input_points]				)
         UpperCAmelCase	    =			[]
         for i, point in enumerate(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(snake_case__				)
         UpperCAmelCase	    =			processed_input_points
         return input_points, input_labels



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=False				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			original_size
         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor._get_preprocess_shape(snake_case__ ,		longest_edge=snake_case__				)
         UpperCAmelCase	    =			deepcopy(snake_case__				).astype(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 ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		)			->			Tuple:



         """simple docstring"""





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

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

         if input_labels is not None:
                if hasattr(snake_case__ ,		"""numpy"""				):
                       UpperCAmelCase	    =			input_labels.numpy().tolist()

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

         if input_boxes is not None:
                if hasattr(snake_case__ ,		"""numpy"""				):
                       UpperCAmelCase	    =			input_boxes.numpy().tolist()

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

         return input_points, input_labels, input_boxes



  @property
  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processor.model_input_names
         return list(dict.fromkeys(snake_case__				)				)



  def 		UpperCamelCase_				(      self ,		*snake_case__ ,		**snake_case__				)			->			Optional[Any]:



         """simple docstring"""





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








"""simple docstring"""







import random
from typing import Any






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       for _ in range(len(lowerCAmelCase		)		):
              UpperCAmelCase	    =			random.randint(0	,      len(lowerCAmelCase		) - 1		)
              UpperCAmelCase	    =			random.randint(0	,      len(lowerCAmelCase		) - 1		)
              UpperCAmelCase    ,       UpperCAmelCase	    =			data[b], data[a]
       return data


if __name__ == "__main__":
 lowerCAmelCase_	:    Tuple            =      [0, 1, 2, 3, 4, 5, 6, 7]
 lowerCAmelCase_	:    List[str]            =      ['''python''', '''says''', '''hello''', '''!''']
 print('''Fisher-Yates Shuffle:''')
 print('''List''', integers, strings)
 print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
