





'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__						:      Optional[Any]	=    logging.get_logger(__name__)

lowercase__						:      Any	=    {
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Tuple						    =   'bridgetower_vision_model'





       def __init__(							self       :		Any	,			lowerCAmelCase__       :		Optional[Any]=768	,			lowerCAmelCase__       :		Dict=12	,			lowerCAmelCase__       :		List[str]=3	,			lowerCAmelCase__       :		Tuple=16	,			lowerCAmelCase__       :		Tuple=288	,			lowerCAmelCase__       :		Dict=1	,			lowerCAmelCase__       :		Optional[int]=1e-0_5	,			lowerCAmelCase__       :		Optional[Any]=False	,			lowerCAmelCase__       :		List[str]=True	,			lowerCAmelCase__       :		str=False	,			**lowerCAmelCase__       :		Any	,			)   ->					List[Any]:

            '''simple docstring'''




            super().__init__(**lowerCAmelCase__					)
            _UpperCamelCase       =	hidden_size
            _UpperCamelCase       =	num_hidden_layers
            _UpperCamelCase       =	num_channels
            _UpperCamelCase       =	patch_size
            _UpperCamelCase       =	image_size
            _UpperCamelCase       =	initializer_factor
            _UpperCamelCase       =	layer_norm_eps
            _UpperCamelCase       =	stop_gradient
            _UpperCamelCase       =	share_layernorm
            _UpperCamelCase       =	remove_last_layer







       @classmethod
       def snake_case__						(							cls       :		Union[str, Any]	,			lowerCAmelCase__       :		Union[str, os.PathLike]	,			**lowerCAmelCase__       :		List[Any]					)   ->					"PretrainedConfig":

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	cls.get_config_dict(lowerCAmelCase__	,			**lowerCAmelCase__					)

            if config_dict.get('''model_type'''					) == "bridgetower":
                 _UpperCamelCase       =	config_dict['''text_config''']

            if "model_type" in config_dict and hasattr(cls	,			'''model_type'''					) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
                     f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""					)

            return cls.from_dict(lowerCAmelCase__	,			**lowerCAmelCase__					)

class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					int						    =   'bridgetower_text_model'





       def __init__(							self       :		Optional[int]	,			lowerCAmelCase__       :		str=50265	,			lowerCAmelCase__       :		List[str]=768	,			lowerCAmelCase__       :		Optional[Any]=12	,			lowerCAmelCase__       :		Optional[Any]=12	,			lowerCAmelCase__       :		str=1	,			lowerCAmelCase__       :		List[str]=3072	,			lowerCAmelCase__       :		Any="gelu"	,			lowerCAmelCase__       :		Any=0.1	,			lowerCAmelCase__       :		List[Any]=0.1	,			lowerCAmelCase__       :		int=514	,			lowerCAmelCase__       :		int=1	,			lowerCAmelCase__       :		int=1e-0_5	,			lowerCAmelCase__       :		Any=1	,			lowerCAmelCase__       :		Union[str, Any]=0	,			lowerCAmelCase__       :		Tuple=2	,			lowerCAmelCase__       :		str="absolute"	,			lowerCAmelCase__       :		Optional[Any]=True	,			**lowerCAmelCase__       :		Optional[int]	,			)   ->					List[str]:

            '''simple docstring'''




            super().__init__(**lowerCAmelCase__					)

            _UpperCamelCase       =	vocab_size
            _UpperCamelCase       =	hidden_size
            _UpperCamelCase       =	num_hidden_layers
            _UpperCamelCase       =	num_attention_heads
            _UpperCamelCase       =	hidden_act
            _UpperCamelCase       =	initializer_factor
            _UpperCamelCase       =	intermediate_size
            _UpperCamelCase       =	hidden_dropout_prob
            _UpperCamelCase       =	attention_probs_dropout_prob
            _UpperCamelCase       =	max_position_embeddings
            _UpperCamelCase       =	type_vocab_size
            _UpperCamelCase       =	layer_norm_eps
            _UpperCamelCase       =	position_embedding_type
            _UpperCamelCase       =	use_cache
            _UpperCamelCase       =	pad_token_id
            _UpperCamelCase       =	bos_token_id
            _UpperCamelCase       =	eos_token_id







       @classmethod
       def snake_case__						(							cls       :		int	,			lowerCAmelCase__       :		Union[str, os.PathLike]	,			**lowerCAmelCase__       :		Dict					)   ->					"PretrainedConfig":

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	cls.get_config_dict(lowerCAmelCase__	,			**lowerCAmelCase__					)

            if config_dict.get('''model_type'''					) == "bridgetower":
                 _UpperCamelCase       =	config_dict['''text_config''']

            if "model_type" in config_dict and hasattr(cls	,			'''model_type'''					) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
                     f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""					)

            return cls.from_dict(lowerCAmelCase__	,			**lowerCAmelCase__					)

class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					List[Any]						    =   'bridgetower'





       def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		List[Any]=True	,			lowerCAmelCase__       :		int="gelu"	,			lowerCAmelCase__       :		str=768	,			lowerCAmelCase__       :		Dict=1	,			lowerCAmelCase__       :		Union[str, Any]=1e-0_5	,			lowerCAmelCase__       :		str=False	,			lowerCAmelCase__       :		Any="add"	,			lowerCAmelCase__       :		List[str]=12	,			lowerCAmelCase__       :		Optional[Any]=6	,			lowerCAmelCase__       :		Union[str, Any]=False	,			lowerCAmelCase__       :		Any=False	,			lowerCAmelCase__       :		Optional[int]=None	,			lowerCAmelCase__       :		List[str]=None	,			**lowerCAmelCase__       :		Any	,			)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	kwargs.pop('''text_config_dict'''	,			lowerCAmelCase__					)
            _UpperCamelCase       =	kwargs.pop('''vision_config_dict'''	,			lowerCAmelCase__					)

            super().__init__(**lowerCAmelCase__					)
            _UpperCamelCase       =	share_cross_modal_transformer_layers
            _UpperCamelCase       =	hidden_act
            _UpperCamelCase       =	hidden_size
            _UpperCamelCase       =	initializer_factor
            _UpperCamelCase       =	layer_norm_eps
            _UpperCamelCase       =	share_link_tower_layers
            _UpperCamelCase       =	link_tower_type
            _UpperCamelCase       =	num_attention_heads
            _UpperCamelCase       =	num_hidden_layers
            _UpperCamelCase       =	tie_word_embeddings
            _UpperCamelCase       =	init_layernorm_from_vision_encoder

            if text_config is None:
                 _UpperCamelCase       =	{}
                 logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'''					)

            if vision_config is None:
                 _UpperCamelCase       =	{}
                 logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'''					)

            _UpperCamelCase       =	BridgeTowerTextConfig(**lowerCAmelCase__					)
            _UpperCamelCase       =	BridgeTowerVisionConfig(**lowerCAmelCase__					)





       @classmethod
       def snake_case__						(							cls       :		Any	,			lowerCAmelCase__       :		BridgeTowerTextConfig	,			lowerCAmelCase__       :		BridgeTowerVisionConfig	,			**lowerCAmelCase__       :		Dict					)   ->					str:

            '''simple docstring'''




            return cls(text_config=text_config.to_dict()	,			vision_config=vision_config.to_dict()	,			**lowerCAmelCase__					)







       def snake_case__						(							self       :		Union[str, Any]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	copy.deepcopy(self.__dict__					)
            _UpperCamelCase       =	self.text_config.to_dict()
            _UpperCamelCase       =	self.vision_config.to_dict()
            _UpperCamelCase       =	self.__class__.model_type
            return output










'''simple docstring'''

def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

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

          _UpperCamelCase       =	update_area_of_max_square(lowercase, col + 1				)
          _UpperCamelCase       =	update_area_of_max_square(row + 1, col + 1				)
          _UpperCamelCase       =	update_area_of_max_square(row + 1, lowercase				)

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

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







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

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

          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(lowercase, col + 1, lowercase				)
          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(row + 1, col + 1, lowercase				)
          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(row + 1, lowercase, lowercase				)

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

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

     return largest_square_area[0]







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

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

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

     return largest_square_area







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

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

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



