

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 (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =["image_processor"]
    __UpperCamelCase         ="SamImageProcessor"

    def __init__(			self :      Any							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     super().__init__(__A    )
     SCREAMING_SNAKE_CASE								=			self.image_processor
     SCREAMING_SNAKE_CASE								=			-1_0
     SCREAMING_SNAKE_CASE								=			self.image_processor.size["longest_edge"]

    def __call__(			self :      Any							,    snake_case__ :      Tuple=None							,    snake_case__ :      Any=None							,    snake_case__ :      Optional[Any]=None							,    snake_case__ :      Dict=None							,    snake_case__ :      Optional[Union[str, TensorType]] = None							,    **snake_case__ :      str							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.image_processor(
         __A							,    return_tensors=__A							,    **__A							,    )

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

     if hasattr(__A							,    'numpy'    ):  # Checks if Torch or TF tensor
      SCREAMING_SNAKE_CASE								=			original_sizes.numpy()

     SCREAMING_SNAKE_CASE								=			self._check_and_preprocess_points(
         input_points=__A							,    input_labels=__A							,    input_boxes=__A							,    )

     SCREAMING_SNAKE_CASE								=			self._normalize_and_convert(
         __A							,    __A							,    input_points=__A							,    input_labels=__A							,    input_boxes=__A							,    return_tensors=__A							,    )

     return encoding_image_processor

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      List[str]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]=None							,    snake_case__ :      int=None							,    snake_case__ :      Any=None							,    snake_case__ :      int="pt"							,    ):

     """simple docstring"""






     if input_points is not None:
      if len(__A    ) != len(__A    ):
       SCREAMING_SNAKE_CASE								=			[
           self._normalize_coordinates(self.target_size							,    __A							,    original_sizes[0]    ) for point in input_points
       ]
      else:
       SCREAMING_SNAKE_CASE								=			[
           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:
        SCREAMING_SNAKE_CASE								=			self._pad_points_and_labels(__A							,    __A    )

      SCREAMING_SNAKE_CASE								=			np.array(__A    )

     if input_labels is not None:
      SCREAMING_SNAKE_CASE								=			np.array(__A    )

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

     if input_boxes is not None:
      if return_tensors == "pt":
       SCREAMING_SNAKE_CASE								=			torch.from_numpy(__A    )
       # boxes batch size of 1 by default
       SCREAMING_SNAKE_CASE								=			input_boxes.unsqueeze(1    ) if len(input_boxes.shape    ) != 3 else input_boxes
      elif return_tensors == "tf":
       SCREAMING_SNAKE_CASE								=			tf.convert_to_tensor(__A    )
       # boxes batch size of 1 by default
       SCREAMING_SNAKE_CASE								=			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":
       SCREAMING_SNAKE_CASE								=			torch.from_numpy(__A    )
       # point batch size of 1 by default
       SCREAMING_SNAKE_CASE								=			input_points.unsqueeze(1    ) if len(input_points.shape    ) != 4 else input_points
      elif return_tensors == "tf":
       SCREAMING_SNAKE_CASE								=			tf.convert_to_tensor(__A    )
       # point batch size of 1 by default
       SCREAMING_SNAKE_CASE								=			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":
       SCREAMING_SNAKE_CASE								=			torch.from_numpy(__A    )
       # point batch size of 1 by default
       SCREAMING_SNAKE_CASE								=			input_labels.unsqueeze(1    ) if len(input_labels.shape    ) != 3 else input_labels
      elif return_tensors == "tf":
       SCREAMING_SNAKE_CASE								=			tf.convert_to_tensor(__A    )
       # point batch size of 1 by default
       SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Dict							,    snake_case__ :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			max([point.shape[0] for point in input_points]    )
     SCREAMING_SNAKE_CASE								=			[]
     for i, point in enumerate(__A    ):
      if point.shape[0] != expected_nb_points:
       SCREAMING_SNAKE_CASE								=			np.concatenate(
           [point, np.zeros((expected_nb_points - point.shape[0], 2)    ) + self.point_pad_value]							,    axis=0    )
       SCREAMING_SNAKE_CASE								=			np.append(input_labels[i]							,    [self.point_pad_value]    )
      processed_input_points.append(__A    )
     SCREAMING_SNAKE_CASE								=			processed_input_points
     return input_points, input_labels

    def UpperCamelCase			(			self :      int							,    snake_case__ :      int							,    snake_case__ :      np.ndarray							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Optional[int]=False    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			original_size
     SCREAMING_SNAKE_CASE								=			self.image_processor._get_preprocess_shape(__A							,    longest_edge=__A    )
     SCREAMING_SNAKE_CASE								=			deepcopy(__A    ).astype(__A    )

     if is_bounding_box:
      SCREAMING_SNAKE_CASE								=			coords.reshape(-1							,    2							,    2    )

     SCREAMING_SNAKE_CASE								=			coords[..., 0] * (new_w / old_w)
     SCREAMING_SNAKE_CASE								=			coords[..., 1] * (new_h / old_h)

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

     return coords

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      str=None							,    snake_case__ :      Tuple=None							,    snake_case__ :      List[Any]=None							,    ):

     """simple docstring"""






     if input_points is not None:
      if hasattr(__A							,    'numpy'    ):  # Checks for TF or Torch tensor
       SCREAMING_SNAKE_CASE								=			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.'    )
      SCREAMING_SNAKE_CASE								=			[np.array(__A    ) for input_point in input_points]
     else:
      SCREAMING_SNAKE_CASE								=			None

     if input_labels is not None:
      if hasattr(__A							,    'numpy'    ):
       SCREAMING_SNAKE_CASE								=			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.'    )
      SCREAMING_SNAKE_CASE								=			[np.array(__A    ) for label in input_labels]
     else:
      SCREAMING_SNAKE_CASE								=			None

     if input_boxes is not None:
      if hasattr(__A							,    'numpy'    ):
       SCREAMING_SNAKE_CASE								=			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.'    )
      SCREAMING_SNAKE_CASE								=			[np.array(__A    ).astype(np.floataa    ) for box in input_boxes]
     else:
      SCREAMING_SNAKE_CASE								=			None

     return input_points, input_labels, input_boxes

    @property
    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.image_processor.model_input_names
     return list(dict.fromkeys(__A    )    )




    def UpperCamelCase			(			self :      List[str]							,    *snake_case__ :      int							,    **snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






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









def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  int	)		->							list[str]:
 '''simple docstring'''


 return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase	) - ngram_size + 1	)]


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

      testmod()

