

'''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 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       ['image_processor']
 UpperCamelCase__     =       'SamImageProcessor'







 def __init__(       self  , snake_case_   ):
   super().__init__(snake_case_   )
   lowercase									=self.image_processor
   lowercase									=-10
   lowercase									=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_  , ):
   lowercase									=self.image_processor(
       snake_case_  , return_tensors=snake_case_  , **snake_case_  , )

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

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

   lowercase							, lowercase							, lowercase									=self._check_and_preprocess_points(
       input_points=snake_case_  , input_labels=snake_case_  , input_boxes=snake_case_  , )

   lowercase									=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 						_A(       self  , snake_case_  , snake_case_  , snake_case_=None  , snake_case_=None  , snake_case_=None  , snake_case_="pt"  , ):
   if input_points is not None:
     if len(snake_case_   ) != len(snake_case_   ):
       lowercase									=[
           self._normalize_coordinates(self.target_size  , snake_case_  , original_sizes[0]   ) for point in input_points
       ]
     else:
       lowercase									=[
           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:
         lowercase							, lowercase									=self._pad_points_and_labels(snake_case_  , snake_case_   )

     lowercase									=np.array(snake_case_   )

   if input_labels is not None:
     lowercase									=np.array(snake_case_   )

   if input_boxes is not None:
     if len(snake_case_   ) != len(snake_case_   ):
       lowercase									=[
           self._normalize_coordinates(self.target_size  , snake_case_  , original_sizes[0]  , is_bounding_box=snake_case_   )
           for box in input_boxes
       ]
     else:
       lowercase									=[
           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_   )
       ]
     lowercase									=np.array(snake_case_   )

   if input_boxes is not None:
     if return_tensors == "pt":
       lowercase									=torch.from_numpy(snake_case_   )
       # boxes batch size of 1 by default
       lowercase									=input_boxes.unsqueeze(1   ) if len(input_boxes.shape   ) != 3 else input_boxes
     elif return_tensors == "tf":
       lowercase									=tf.convert_to_tensor(snake_case_   )
       # boxes batch size of 1 by default
       lowercase									=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":
       lowercase									=torch.from_numpy(snake_case_   )
       # point batch size of 1 by default
       lowercase									=input_points.unsqueeze(1   ) if len(input_points.shape   ) != 4 else input_points
     elif return_tensors == "tf":
       lowercase									=tf.convert_to_tensor(snake_case_   )
       # point batch size of 1 by default
       lowercase									=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":
       lowercase									=torch.from_numpy(snake_case_   )
       # point batch size of 1 by default
       lowercase									=input_labels.unsqueeze(1   ) if len(input_labels.shape   ) != 3 else input_labels
     elif return_tensors == "tf":
       lowercase									=tf.convert_to_tensor(snake_case_   )
       # point batch size of 1 by default
       lowercase									=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 						_A(       self  , snake_case_  , snake_case_   ):
   lowercase									=max([point.shape[0] for point in input_points]   )
   lowercase									=[]
   for i, point in enumerate(snake_case_   ):
     if point.shape[0] != expected_nb_points:
       lowercase									=np.concatenate(
           [point, np.zeros((expected_nb_points - point.shape[0], 2)   ) + self.point_pad_value]  , axis=0   )
       lowercase									=np.append(input_labels[i]  , [self.point_pad_value]   )
     processed_input_points.append(snake_case_   )
   lowercase									=processed_input_points
   return input_points, input_labels







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_=False   ):
   lowercase							, lowercase									=original_size
   lowercase							, lowercase									=self.image_processor._get_preprocess_shape(snake_case_  , longest_edge=snake_case_   )
   lowercase									=deepcopy(snake_case_   ).astype(snake_case_   )

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

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

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

   return coords







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

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

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

   return input_points, input_labels, input_boxes







 @property
 def 						_A(       self   ):
   lowercase									=self.image_processor.model_input_names
   return list(dict.fromkeys(snake_case_   )   )







 def 						_A(       self  , *snake_case_  , **snake_case_   ):
   return self.image_processor.post_process_masks(*snake_case_  , **snake_case_   )







import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__A							:			List[Any]         =			"python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
 pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
 pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=None				)							-> Union[str, Any]:




    """simple docstring"""




    require_version(deps[pkg]							,     _SCREAMING_SNAKE_CASE				)





