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 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      ["image_processor"]
    __a  =      "SamImageProcessor"
    def __init__(  self , lowerCAmelCase   )       ->					Optional[int]:
     super().__init__(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      self.image_processor
     SCREAMING_SNAKE_CASE__:   Optional[int]=      -10
     SCREAMING_SNAKE_CASE__:   Tuple=      self.image_processor.size['''longest_edge''']
    def __call__(  self , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase = None , **lowerCAmelCase , )       ->					BatchEncoding:
     SCREAMING_SNAKE_CASE__:   Tuple=      self.image_processor(
         lowerCAmelCase , return_tensors=lowerCAmelCase , **lowerCAmelCase , )

     # pop arguments that are not used in the foward but used nevertheless
     SCREAMING_SNAKE_CASE__:   List[str]=      encoding_image_processor['''original_sizes''']

     if hasattr(lowerCAmelCase , '''numpy'''   ):  # Checks if Torch or TF tensor
      SCREAMING_SNAKE_CASE__:   str=      original_sizes.numpy()

     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   int=      self._check_and_preprocess_points(
         input_points=lowerCAmelCase , input_labels=lowerCAmelCase , input_boxes=lowerCAmelCase , )

     SCREAMING_SNAKE_CASE__:   str=      self._normalize_and_convert(
         lowerCAmelCase , lowerCAmelCase , input_points=lowerCAmelCase , input_labels=lowerCAmelCase , input_boxes=lowerCAmelCase , return_tensors=lowerCAmelCase , )

     return encoding_image_processor
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase="pt" , )       ->					List[str]:
     if input_points is not None:
      if len(lowerCAmelCase   ) != len(lowerCAmelCase   ):
       SCREAMING_SNAKE_CASE__:   List[Any]=      [
           self._normalize_coordinates(self.target_size , lowerCAmelCase , original_sizes[0]   ) for point in input_points
       ]
      else:
       SCREAMING_SNAKE_CASE__:   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:
        SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self._pad_points_and_labels(lowerCAmelCase , lowerCAmelCase   )

      SCREAMING_SNAKE_CASE__:   str=      np.array(lowerCAmelCase   )

     if input_labels is not None:
      SCREAMING_SNAKE_CASE__:   List[Any]=      np.array(lowerCAmelCase   )

     if input_boxes is not None:
      if len(lowerCAmelCase   ) != len(lowerCAmelCase   ):
       SCREAMING_SNAKE_CASE__:   str=      [
           self._normalize_coordinates(self.target_size , lowerCAmelCase , original_sizes[0] , is_bounding_box=lowerCAmelCase   )
           for box in input_boxes
       ]
      else:
       SCREAMING_SNAKE_CASE__:   Union[str, Any]=      [
           self._normalize_coordinates(self.target_size , lowerCAmelCase , lowerCAmelCase , is_bounding_box=lowerCAmelCase   )
           for box, original_size in zip(lowerCAmelCase , lowerCAmelCase   )
       ]
      SCREAMING_SNAKE_CASE__:   Dict=      np.array(lowerCAmelCase   )

     if input_boxes is not None:
      if return_tensors == "pt":
       SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.from_numpy(lowerCAmelCase   )
       # boxes batch size of 1 by default
       SCREAMING_SNAKE_CASE__:   List[Any]=      input_boxes.unsqueeze(1   ) if len(input_boxes.shape   ) != 3 else input_boxes
      elif return_tensors == "tf":
       SCREAMING_SNAKE_CASE__:   int=      tf.convert_to_tensor(lowerCAmelCase   )
       # boxes batch size of 1 by default
       SCREAMING_SNAKE_CASE__:   Union[str, 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":
       SCREAMING_SNAKE_CASE__:   int=      torch.from_numpy(lowerCAmelCase   )
       # point batch size of 1 by default
       SCREAMING_SNAKE_CASE__:   Tuple=      input_points.unsqueeze(1   ) if len(input_points.shape   ) != 4 else input_points
      elif return_tensors == "tf":
       SCREAMING_SNAKE_CASE__:   Any=      tf.convert_to_tensor(lowerCAmelCase   )
       # point batch size of 1 by default
       SCREAMING_SNAKE_CASE__:   Optional[Any]=      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":
       SCREAMING_SNAKE_CASE__:   str=      torch.from_numpy(lowerCAmelCase   )
       # point batch size of 1 by default
       SCREAMING_SNAKE_CASE__:   Tuple=      input_labels.unsqueeze(1   ) if len(input_labels.shape   ) != 3 else input_labels
      elif return_tensors == "tf":
       SCREAMING_SNAKE_CASE__:   Optional[Any]=      tf.convert_to_tensor(lowerCAmelCase   )
       # point batch size of 1 by default
       SCREAMING_SNAKE_CASE__:   List[Any]=      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 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      max([point.shape[0] for point in input_points]   )
     SCREAMING_SNAKE_CASE__:   List[str]=      []
     for i, point in enumerate(lowerCAmelCase   ):
      if point.shape[0] != expected_nb_points:
       SCREAMING_SNAKE_CASE__:   str=      np.concatenate(
           [point, np.zeros((expected_nb_points - point.shape[0], 2)   ) + self.point_pad_value] , axis=0   )
       SCREAMING_SNAKE_CASE__:   Optional[int]=      np.append(input_labels[i] , [self.point_pad_value]   )
      processed_input_points.append(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      processed_input_points
     return input_points, input_labels
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=False   )       ->					np.ndarray:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   str=      original_size
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Dict=      self.image_processor._get_preprocess_shape(lowerCAmelCase , longest_edge=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      deepcopy(lowerCAmelCase   ).astype(lowerCAmelCase   )

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

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

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

     return coords
    def 						UpperCamelCase_				(  self , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None , )       ->					Optional[int]:
     if input_points is not None:
      if hasattr(lowerCAmelCase , '''numpy'''   ):  # Checks for TF or Torch tensor
       SCREAMING_SNAKE_CASE__:   Union[str, Any]=      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.'''   )
      SCREAMING_SNAKE_CASE__:   Any=      [np.array(lowerCAmelCase   ) for input_point in input_points]
     else:
      SCREAMING_SNAKE_CASE__:   int=      None

     if input_labels is not None:
      if hasattr(lowerCAmelCase , '''numpy'''   ):
       SCREAMING_SNAKE_CASE__:   Optional[Any]=      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.'''   )
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      [np.array(lowerCAmelCase   ) for label in input_labels]
     else:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      None

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

     return input_points, input_labels, input_boxes
    @property
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.image_processor.model_input_names
     return list(dict.fromkeys(lowerCAmelCase   )   )



    def 						UpperCamelCase_				(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					Tuple:
     return self.image_processor.post_process_masks(*lowerCAmelCase , **lowerCAmelCase   )


from ..utils import DummyObject, requires_backends






class 		_lowerCamelCase		(   metaclass=UpperCamelCase_    ):
    __a  =      ["torch", "scipy"]
    def __init__(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					Any:
     requires_backends(self , ['''torch''', '''scipy''']   )
    @classmethod
    def 						UpperCamelCase_				(  cls , *lowerCAmelCase , **lowerCAmelCase   )       ->					List[str]:
     requires_backends(cls , ['''torch''', '''scipy''']   )



    @classmethod
    def 						UpperCamelCase_				(  cls , *lowerCAmelCase , **lowerCAmelCase   )       ->					Tuple:
     requires_backends(cls , ['''torch''', '''scipy''']   )

