





'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  UpperCAmelCase     (			_UpperCAmelCase   ):
      _lowercase:			Union[str, Any]						     =   ["""image_processor""", """tokenizer"""]
      _lowercase:			Optional[int]						     =   """LayoutLMv3ImageProcessor"""
      _lowercase:			List[str]						     =   ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")




      def __init__(			self			: Tuple			,			__snake_case			: Optional[Any]=None			,			__snake_case			: int=None			,			**__snake_case			: Union[str, Any]  )				->      Dict:
            _lowerCAmelCase							     =							None
            if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead."""			,			UpperCAmelCase_			,			)
                  _lowerCAmelCase							     =							kwargs.pop("""feature_extractor"""  )

            _lowerCAmelCase							     =							image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                  raise ValueError("""You need to specify an `image_processor`."""  )
            if tokenizer is None:
                  raise ValueError("""You need to specify a `tokenizer`."""  )

            super().__init__(UpperCAmelCase_			,			UpperCAmelCase_  )




      def __call__(			self			: List[str]			,			__snake_case			: List[str]			,			__snake_case			: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,			__snake_case			: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,			__snake_case			: Union[List[List[int]], List[List[List[int]]]] = None			,			__snake_case			: Optional[Union[List[int], List[List[int]]]] = None			,			__snake_case			: bool = True			,			__snake_case			: Union[bool, str, PaddingStrategy] = False			,			__snake_case			: Union[bool, str, TruncationStrategy] = None			,			__snake_case			: Optional[int] = None			,			__snake_case			: int = 0			,			__snake_case			: Optional[int] = None			,			__snake_case			: Optional[bool] = None			,			__snake_case			: Optional[bool] = None			,			__snake_case			: bool = False			,			__snake_case			: bool = False			,			__snake_case			: bool = False			,			__snake_case			: bool = False			,			__snake_case			: bool = True			,			__snake_case			: Optional[Union[str, TensorType]] = None			,			**__snake_case			: Any			,			)				->      Optional[int]:
            # verify input
            if self.image_processor.apply_ocr and (boxes is not None):
                  raise ValueError(
                      """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""  )

            if self.image_processor.apply_ocr and (word_labels is not None):
                  raise ValueError(
                      """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""  )

            # first, apply the image processor
            _lowerCAmelCase							     =							self.image_processor(images=UpperCAmelCase_			,			return_tensors=UpperCAmelCase_  )

            # second, apply the tokenizer
            if text is not None and self.image_processor.apply_ocr and text_pair is None:
                  if isinstance(UpperCAmelCase_			,			UpperCAmelCase_  ):
                        _lowerCAmelCase							     =							[text]  # add batch dimension (as the image processor always adds a batch dimension)
                  _lowerCAmelCase							     =							features["""words"""]

            _lowerCAmelCase							     =							self.tokenizer(
                text=text if text is not None else features["""words"""]			,			text_pair=text_pair if text_pair is not None else None			,			boxes=boxes if boxes is not None else features["""boxes"""]			,			word_labels=UpperCAmelCase_			,			add_special_tokens=UpperCAmelCase_			,			padding=UpperCAmelCase_			,			truncation=UpperCAmelCase_			,			max_length=UpperCAmelCase_			,			stride=UpperCAmelCase_			,			pad_to_multiple_of=UpperCAmelCase_			,			return_token_type_ids=UpperCAmelCase_			,			return_attention_mask=UpperCAmelCase_			,			return_overflowing_tokens=UpperCAmelCase_			,			return_special_tokens_mask=UpperCAmelCase_			,			return_offsets_mapping=UpperCAmelCase_			,			return_length=UpperCAmelCase_			,			verbose=UpperCAmelCase_			,			return_tensors=UpperCAmelCase_			,			**UpperCAmelCase_			,			)

            # add pixel values
            _lowerCAmelCase							     =							features.pop("""pixel_values"""  )
            if return_overflowing_tokens is True:
                  _lowerCAmelCase							     =							self.get_overflowing_images(UpperCAmelCase_			,			encoded_inputs["""overflow_to_sample_mapping"""]  )
            _lowerCAmelCase							     =							images

            return encoded_inputs




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: Any			,			__snake_case			: Union[str, Any]  )				->      Union[str, Any]:
            # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
            _lowerCAmelCase							     =							[]
            for sample_idx in overflow_to_sample_mapping:
                  images_with_overflow.append(images[sample_idx]  )

            if len(UpperCAmelCase_  ) != len(UpperCAmelCase_  ):
                  raise ValueError(
                      """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
                      f" {len(UpperCAmelCase_  )} and {len(UpperCAmelCase_  )}"  )

            return images_with_overflow




      def 				lowercase__    (			self			: Optional[int]			,			*__snake_case			: Optional[Any]			,			**__snake_case			: Dict  )				->      List[str]:
            return self.tokenizer.batch_decode(*UpperCAmelCase_			,			**UpperCAmelCase_  )




      def 				lowercase__    (			self			: Dict			,			*__snake_case			: Optional[int]			,			**__snake_case			: Union[str, Any]  )				->      Dict:
            return self.tokenizer.decode(*UpperCAmelCase_			,			**UpperCAmelCase_  )




      @property
      def 				lowercase__    (			self			: List[Any]  )				->      Union[str, Any]:
            return ["input_ids", "bbox", "attention_mask", "pixel_values"]




      @property
      def 				lowercase__    (			self			: Optional[Any]  )				->      int:
            warnings.warn(
                """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			UpperCAmelCase_			,			)
            return self.image_processor_class




      @property
      def 				lowercase__    (			self			: List[str]  )				->      int:
            warnings.warn(
                """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			UpperCAmelCase_			,			)
            return self.image_processor








def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					def update_area_of_max_square(UpperCamelCase_	,	UpperCamelCase_		) -> int:
										# BASE CASE
										if row >= rows or col >= cols:
															return 0

										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(UpperCamelCase_	,	col + 1		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(row + 1	,	col + 1		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(row + 1	,	UpperCamelCase_		)

										if mat[row][col]:
															SCREAMING_SNAKE_CASE__													= 1 + min([right, diagonal, down]		)
															SCREAMING_SNAKE_CASE__													= max(largest_square_area[0]	,	UpperCamelCase_		)
															return sub_problem_sol
										else:
															return 0

					SCREAMING_SNAKE_CASE__													= [0]
					update_area_of_max_square(0	,	0		)
					return largest_square_area[0]






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					def update_area_of_max_square_using_dp_array(
					    UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		) -> int:
										if row >= rows or col >= cols:
															return 0
										if dp_array[row][col] != -1:
															return dp_array[row][col]

										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(UpperCamelCase_	,	col + 1	,	UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(row + 1	,	col + 1	,	UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(row + 1	,	UpperCamelCase_	,	UpperCamelCase_		)

										if mat[row][col]:
															SCREAMING_SNAKE_CASE__													= 1 + min([right, diagonal, down]		)
															SCREAMING_SNAKE_CASE__													= max(largest_square_area[0]	,	UpperCamelCase_		)
															SCREAMING_SNAKE_CASE__													= sub_problem_sol
															return sub_problem_sol
										else:
															return 0

					SCREAMING_SNAKE_CASE__													= [0]
					SCREAMING_SNAKE_CASE__													= [[-1] * cols for _ in range(UpperCamelCase_		)]
					update_area_of_max_square_using_dp_array(0	,	0	,	UpperCamelCase_		)

					return largest_square_area[0]






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= [[0] * (cols + 1) for _ in range(rows + 1		)]
					SCREAMING_SNAKE_CASE__													= 0
					for row in range(rows - 1	,	-1	,	-1		):
										for col in range(cols - 1	,	-1	,	-1		):
															SCREAMING_SNAKE_CASE__													= dp_array[row][col + 1]
															SCREAMING_SNAKE_CASE__													= dp_array[row + 1][col + 1]
															SCREAMING_SNAKE_CASE__													= dp_array[row + 1][col]

															if mat[row][col] == 1:
																				SCREAMING_SNAKE_CASE__													= 1 + min(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
																				SCREAMING_SNAKE_CASE__													= max(dp_array[row][col]	,	UpperCamelCase_		)
															else:
																				SCREAMING_SNAKE_CASE__													= 0

					return largest_square_area






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= [0] * (cols + 1)
					SCREAMING_SNAKE_CASE__													= [0] * (cols + 1)
					SCREAMING_SNAKE_CASE__													= 0
					for row in range(rows - 1	,	-1	,	-1		):
										for col in range(cols - 1	,	-1	,	-1		):
															SCREAMING_SNAKE_CASE__													= current_row[col + 1]
															SCREAMING_SNAKE_CASE__													= next_row[col + 1]
															SCREAMING_SNAKE_CASE__													= next_row[col]

															if mat[row][col] == 1:
																				SCREAMING_SNAKE_CASE__													= 1 + min(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
																				SCREAMING_SNAKE_CASE__													= max(current_row[col]	,	UpperCamelCase_		)
															else:
																				SCREAMING_SNAKE_CASE__													= 0
										SCREAMING_SNAKE_CASE__													= current_row

					return largest_square_area


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))





