


'''simple docstring'''



import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A__		:					int												=  logging.get_logger(__name__)
A__		:					List[Any]												=  {"""vocab_file""": """spiece.model"""}

A__		:					Tuple												=  {
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

A__		:					Optional[int]												=  {
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

A__		:					Optional[int]												=  """▁"""





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					List[Any]							     =			VOCAB_FILES_NAMES
    lowerCamelCase  :					Optional[int]							     =			PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  :					Dict							     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_="[CLS]"			,     SCREAMING_SNAKE_CASE_="[SEP]"			,     SCREAMING_SNAKE_CASE_="<unk>"			,     SCREAMING_SNAKE_CASE_="[SEP]"			,     SCREAMING_SNAKE_CASE_="<pad>"			,     SCREAMING_SNAKE_CASE_="[CLS]"			,     SCREAMING_SNAKE_CASE_="[MASK]"			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     None:
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.
         __lowerCamelCase   :	Optional[Any]								=		(
             AddedToken(SCREAMING_SNAKE_CASE_			,     lstrip=SCREAMING_SNAKE_CASE_			,     rstrip=SCREAMING_SNAKE_CASE_			,     normalized=SCREAMING_SNAKE_CASE_	)
             if isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
             else mask_token
         )

         __lowerCamelCase   :	Union[str, Any]								=		{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             do_lower_case=SCREAMING_SNAKE_CASE_			,     remove_space=SCREAMING_SNAKE_CASE_			,     keep_accents=SCREAMING_SNAKE_CASE_			,     bos_token=SCREAMING_SNAKE_CASE_			,     eos_token=SCREAMING_SNAKE_CASE_			,     unk_token=SCREAMING_SNAKE_CASE_			,     sep_token=SCREAMING_SNAKE_CASE_			,     pad_token=SCREAMING_SNAKE_CASE_			,     cls_token=SCREAMING_SNAKE_CASE_			,     mask_token=SCREAMING_SNAKE_CASE_			,     sp_model_kwargs=self.sp_model_kwargs			,     **SCREAMING_SNAKE_CASE_			,     )

         __lowerCamelCase   :	Optional[Any]								=		do_lower_case
         __lowerCamelCase   :	List[str]								=		remove_space
         __lowerCamelCase   :	Optional[int]								=		keep_accents
         __lowerCamelCase   :	str								=		vocab_file

         __lowerCamelCase   :	Union[str, Any]								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(SCREAMING_SNAKE_CASE_	)







    @property
    def       lowercase_				(							self	)				->     str:
         return len(self.sp_model	)







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Union[str, Any]								=		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
         vocab.update(self.added_tokens_encoder	)
         return vocab







    def __getstate__(							self	)				->     str:
         __lowerCamelCase   :	Tuple								=		self.__dict__.copy()
         __lowerCamelCase   :	Tuple								=		None
         return state







    def __setstate__(							self			,     SCREAMING_SNAKE_CASE_	)				->     Dict:
         __lowerCamelCase   :	Union[str, Any]								=		d

         # for backward compatibility
         if not hasattr(self			,     'sp_model_kwargs'	):
              __lowerCamelCase   :	Optional[Any]								=		{}

         __lowerCamelCase   :	Optional[int]								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(self.vocab_file	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         if self.remove_space:
              __lowerCamelCase   :	Any								=		' '.join(inputs.strip().split()	)
         else:
              __lowerCamelCase   :	List[Any]								=		inputs
         __lowerCamelCase   :	List[str]								=		outputs.replace('``'			,     '"'	).replace('\'\''			,     '"'	)

         if not self.keep_accents:
              __lowerCamelCase   :	int								=		unicodedata.normalize('NFKD'			,     SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Optional[Any]								=		''.join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE_	)]	)
         if self.do_lower_case:
              __lowerCamelCase   :	Any								=		outputs.lower()

         return outputs







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         __lowerCamelCase   :	List[Any]								=		self.preprocess_text(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		self.sp_model.encode(SCREAMING_SNAKE_CASE_			,     out_type=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		[]
         for piece in pieces:
              if len(SCREAMING_SNAKE_CASE_	) > 1 and piece[-1] == str(','	) and piece[-2].isdigit():
                   __lowerCamelCase   :	List[Any]								=		self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE_			,     ''	)	)
                   if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                        if len(cur_pieces[0]	) == 1:
                             __lowerCamelCase   :	Optional[int]								=		cur_pieces[1:]
                        else:
                             __lowerCamelCase   :	Any								=		cur_pieces[0][1:]
                   cur_pieces.append(piece[-1]	)
                   new_pieces.extend(SCREAMING_SNAKE_CASE_	)
              else:
                   new_pieces.append(SCREAMING_SNAKE_CASE_	)

         return new_pieces







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         __lowerCamelCase   :	Optional[int]								=		[]
         __lowerCamelCase   :	Union[str, Any]								=		''
         __lowerCamelCase   :	Tuple								=		False
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   if not prev_is_special:
                        out_string += " "
                   out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	) + token
                   __lowerCamelCase   :	Dict								=		True
                   __lowerCamelCase   :	Optional[Any]								=		[]
              else:
                   current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
                   __lowerCamelCase   :	List[Any]								=		False
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)
         return out_string.strip()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     List[int]:
         __lowerCamelCase   :	Any								=		[self.sep_token_id]
         __lowerCamelCase   :	List[str]								=		[self.cls_token_id]
         if token_ids_a is None:
              return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = False	)				->     List[int]:

         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=SCREAMING_SNAKE_CASE_			,     token_ids_a=SCREAMING_SNAKE_CASE_			,     already_has_special_tokens=SCREAMING_SNAKE_CASE_	)

         if token_ids_a is not None:
              return [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     List[int]:
         __lowerCamelCase   :	int								=		[self.sep_token_id]
         __lowerCamelCase   :	Optional[Any]								=		[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
              return
         __lowerCamelCase   :	Optional[Any]								=		os.path.join(
             SCREAMING_SNAKE_CASE_			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	) and os.path.isfile(self.vocab_file	):
              copyfile(self.vocab_file			,     SCREAMING_SNAKE_CASE_	)
         elif not os.path.isfile(self.vocab_file	):
              with open(SCREAMING_SNAKE_CASE_			,     'wb'	) as fi:
                   __lowerCamelCase   :	Dict								=		self.sp_model.serialized_model_proto()
                   fi.write(SCREAMING_SNAKE_CASE_	)

         return (out_vocab_file,)










'''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_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Dict							     =			['image_processor']
    lowerCamelCase  :					str							     =			'SamImageProcessor'







    def __init__(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         super().__init__(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		self.image_processor
         __lowerCamelCase   :	str								=		-10
         __lowerCamelCase   :	List[str]								=		self.image_processor.size['longest_edge']







    def __call__(							self			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     BatchEncoding:
         __lowerCamelCase   :	Union[str, Any]								=		self.image_processor(
             SCREAMING_SNAKE_CASE_			,     return_tensors=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_			,     )

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

         if hasattr(SCREAMING_SNAKE_CASE_			,     'numpy'	):  # Checks if Torch or TF tensor
              __lowerCamelCase   :	Dict								=		original_sizes.numpy()

         __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	str								=		self._check_and_preprocess_points(
             input_points=SCREAMING_SNAKE_CASE_			,     input_labels=SCREAMING_SNAKE_CASE_			,     input_boxes=SCREAMING_SNAKE_CASE_			,     )

         __lowerCamelCase   :	Tuple								=		self._normalize_and_convert(
             SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     input_points=SCREAMING_SNAKE_CASE_			,     input_labels=SCREAMING_SNAKE_CASE_			,     input_boxes=SCREAMING_SNAKE_CASE_			,     return_tensors=SCREAMING_SNAKE_CASE_			,     )

         return encoding_image_processor







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_="pt"			,     )				->     Optional[int]:
         if input_points is not None:
              if len(SCREAMING_SNAKE_CASE_	) != len(SCREAMING_SNAKE_CASE_	):
                   __lowerCamelCase   :	Union[str, Any]								=		[
                       self._normalize_coordinates(self.target_size			,     SCREAMING_SNAKE_CASE_			,     original_sizes[0]	) for point in input_points
                   ]
              else:
                   __lowerCamelCase   :	List[Any]								=		[
                       self._normalize_coordinates(self.target_size			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
                       for point, original_size in zip(SCREAMING_SNAKE_CASE_			,     SCREAMING_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:
                        __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		self._pad_points_and_labels(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)

              __lowerCamelCase   :	Optional[int]								=		np.array(SCREAMING_SNAKE_CASE_	)

         if input_labels is not None:
              __lowerCamelCase   :	Optional[int]								=		np.array(SCREAMING_SNAKE_CASE_	)

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

         if input_boxes is not None:
              if return_tensors == "pt":
                   __lowerCamelCase   :	Any								=		torch.from_numpy(SCREAMING_SNAKE_CASE_	)
                   # boxes batch size of 1 by default
                   __lowerCamelCase   :	Union[str, Any]								=		input_boxes.unsqueeze(1	) if len(input_boxes.shape	) != 3 else input_boxes
              elif return_tensors == "tf":
                   __lowerCamelCase   :	List[str]								=		tf.convert_to_tensor(SCREAMING_SNAKE_CASE_	)
                   # boxes batch size of 1 by default
                   __lowerCamelCase   :	str								=		tf.expand_dims(SCREAMING_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":
                   __lowerCamelCase   :	int								=		torch.from_numpy(SCREAMING_SNAKE_CASE_	)
                   # point batch size of 1 by default
                   __lowerCamelCase   :	Dict								=		input_points.unsqueeze(1	) if len(input_points.shape	) != 4 else input_points
              elif return_tensors == "tf":
                   __lowerCamelCase   :	Dict								=		tf.convert_to_tensor(SCREAMING_SNAKE_CASE_	)
                   # point batch size of 1 by default
                   __lowerCamelCase   :	Tuple								=		tf.expand_dims(SCREAMING_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":
                   __lowerCamelCase   :	List[Any]								=		torch.from_numpy(SCREAMING_SNAKE_CASE_	)
                   # point batch size of 1 by default
                   __lowerCamelCase   :	Dict								=		input_labels.unsqueeze(1	) if len(input_labels.shape	) != 3 else input_labels
              elif return_tensors == "tf":
                   __lowerCamelCase   :	str								=		tf.convert_to_tensor(SCREAMING_SNAKE_CASE_	)
                   # point batch size of 1 by default
                   __lowerCamelCase   :	Dict								=		tf.expand_dims(SCREAMING_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       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Tuple:
         __lowerCamelCase   :	List[str]								=		max([point.shape[0] for point in input_points]	)
         __lowerCamelCase   :	Union[str, Any]								=		[]
         for i, point in enumerate(SCREAMING_SNAKE_CASE_	):
              if point.shape[0] != expected_nb_points:
                   __lowerCamelCase   :	Optional[int]								=		np.concatenate(
                       [point, np.zeros((expected_nb_points - point.shape[0], 2)	) + self.point_pad_value]			,     axis=0	)
                   __lowerCamelCase   :	List[Any]								=		np.append(input_labels[i]			,     [self.point_pad_value]	)
              processed_input_points.append(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		processed_input_points
         return input_points, input_labels







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=False	)				->     np.ndarray:
         __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		original_size
         __lowerCamelCase				,       __lowerCamelCase   :	Optional[Any]								=		self.image_processor._get_preprocess_shape(SCREAMING_SNAKE_CASE_			,     longest_edge=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		deepcopy(SCREAMING_SNAKE_CASE_	).astype(SCREAMING_SNAKE_CASE_	)

         if is_bounding_box:
              __lowerCamelCase   :	Optional[int]								=		coords.reshape(-1			,     2			,     2	)

         __lowerCamelCase   :	List[Any]								=		coords[..., 0] * (new_w / old_w)
         __lowerCamelCase   :	Dict								=		coords[..., 1] * (new_h / old_h)

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

         return coords







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     )				->     Optional[Any]:
         if input_points is not None:
              if hasattr(SCREAMING_SNAKE_CASE_			,     'numpy'	):  # Checks for TF or Torch tensor
                   __lowerCamelCase   :	List[str]								=		input_points.numpy().tolist()

              if not isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) or not isinstance(input_points[0]			,     SCREAMING_SNAKE_CASE_	):
                   raise ValueError('Input points must be a list of list of floating points.'	)
              __lowerCamelCase   :	str								=		[np.array(SCREAMING_SNAKE_CASE_	) for input_point in input_points]
         else:
              __lowerCamelCase   :	Optional[Any]								=		None

         if input_labels is not None:
              if hasattr(SCREAMING_SNAKE_CASE_			,     'numpy'	):
                   __lowerCamelCase   :	Union[str, Any]								=		input_labels.numpy().tolist()

              if not isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) or not isinstance(input_labels[0]			,     SCREAMING_SNAKE_CASE_	):
                   raise ValueError('Input labels must be a list of list integers.'	)
              __lowerCamelCase   :	Any								=		[np.array(SCREAMING_SNAKE_CASE_	) for label in input_labels]
         else:
              __lowerCamelCase   :	Dict								=		None

         if input_boxes is not None:
              if hasattr(SCREAMING_SNAKE_CASE_			,     'numpy'	):
                   __lowerCamelCase   :	int								=		input_boxes.numpy().tolist()

              if (
                  not isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
                  or not isinstance(input_boxes[0]			,     SCREAMING_SNAKE_CASE_	)
                  or not isinstance(input_boxes[0][0]			,     SCREAMING_SNAKE_CASE_	)
              ):
                   raise ValueError('Input boxes must be a list of list of list of floating points.'	)
              __lowerCamelCase   :	List[Any]								=		[np.array(SCREAMING_SNAKE_CASE_	).astype(np.floataa	) for box in input_boxes]
         else:
              __lowerCamelCase   :	Tuple								=		None

         return input_points, input_labels, input_boxes







    @property
    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	Any								=		self.image_processor.model_input_names
         return list(dict.fromkeys(SCREAMING_SNAKE_CASE_	)	)







    def       lowercase_				(							self			,     *SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)				->     Optional[Any]:
         return self.image_processor.post_process_masks(*SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)






