
'''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 ):
    lowerCAmelCase_				    =		['image_processor']
    lowerCAmelCase_				    =		'SamImageProcessor'



    def __init__(	self      :       Any,__A      :       Optional[int]     ):
          super().__init__(__A     )
          _lowerCamelCase				:   Union[str, Any]								=   self.image_processor
          _lowerCamelCase				:   Optional[int]								=   -1_0
          _lowerCamelCase				:   List[str]								=   self.image_processor.size["longest_edge"]



    def __call__(	self      :       Any,__A      :       Tuple=None,__A      :       Any=None,__A      :       Optional[Any]=None,__A      :       Dict=None,__A      :       Optional[Union[str, TensorType]] = None,**__A      :       str,):
          _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						,		_lowerCamelCase						,		_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   lowerCamelCase_  (	self      :       List[str],__A      :       List[str],__A      :       Union[str, Any],__A      :       List[str]=None,__A      :       int=None,__A      :       Any=None,__A      :       int="pt",):
          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						,		_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   lowerCamelCase_  (	self      :       List[str],__A      :       Dict,__A      :       str     ):
          _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   lowerCamelCase_  (	self      :       int,__A      :       int,__A      :       np.ndarray,__A      :       Union[str, Any],__A      :       Optional[int]=False     ):
          _lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   original_size
          _lowerCamelCase						,		_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   lowerCamelCase_  (	self      :       List[str],__A      :       str=None,__A      :       Tuple=None,__A      :       List[Any]=None,):
          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   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[Any]								=   self.image_processor.model_input_names
          return list(dict.fromkeys(__A     )     )



    def   lowerCamelCase_  (	self      :       List[str],*__A      :       int,**__A      :       Union[str, Any]     ):
          return self.image_processor.post_process_masks(*__A,**__A     )

'''simple docstring'''






import random
from typing import Any







def 		A_       (	_lowerCAmelCase   :    list							):



      """simple docstring"""




      for _ in range(len(_lowerCAmelCase							)							):
            _lowerCamelCase				:   Any								=   random.randint(0				,	len(_lowerCAmelCase							) - 1							)
            _lowerCamelCase				:   List[str]								=   random.randint(0				,	len(_lowerCAmelCase							) - 1							)
            _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   data[b], data[a]
      return data


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