






import copy
import os
from typing import Union

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


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """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 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				List[str] 				= "bridgetower_vision_model"



       def __init__(							self			,						a__=7_68			,						a__=12			,						a__=3			,						a__=16			,						a__=2_88			,						a__=1			,						a__=1e-05			,						a__=False			,						a__=True			,						a__=False			,						**a__			,						) ->      Dict:






            '''simple docstring'''





            super().__init__(**a__    )
            __snake_case			:Union[str, Any]          =						hidden_size
            __snake_case			:Optional[int]          =						num_hidden_layers
            __snake_case			:Dict          =						num_channels
            __snake_case			:str          =						patch_size
            __snake_case			:Tuple          =						image_size
            __snake_case			:Optional[int]          =						initializer_factor
            __snake_case			:Optional[int]          =						layer_norm_eps
            __snake_case			:List[Any]          =						stop_gradient
            __snake_case			:Tuple          =						share_layernorm
            __snake_case			:str          =						remove_last_layer



       @classmethod
       def 					__lowercase						(							cls			,						a__			,						**a__    ) ->      "PretrainedConfig":






            '''simple docstring'''





            __snake_case       , __snake_case			:str          =						cls.get_config_dict(a__			,						**a__    )

            if config_dict.get("""model_type"""    ) == "bridgetower":
                 __snake_case			:Optional[Any]          =						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(a__			,						**a__    )




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				List[str] 				= "bridgetower_text_model"



       def __init__(							self			,						a__=5_02_65			,						a__=7_68			,						a__=12			,						a__=12			,						a__=1			,						a__=30_72			,						a__="gelu"			,						a__=0.1			,						a__=0.1			,						a__=5_14			,						a__=1			,						a__=1e-05			,						a__=1			,						a__=0			,						a__=2			,						a__="absolute"			,						a__=True			,						**a__			,						) ->      Dict:






            '''simple docstring'''





            super().__init__(**a__    )

            __snake_case			:Union[str, Any]          =						vocab_size
            __snake_case			:Any          =						hidden_size
            __snake_case			:Any          =						num_hidden_layers
            __snake_case			:Dict          =						num_attention_heads
            __snake_case			:Any          =						hidden_act
            __snake_case			:Union[str, Any]          =						initializer_factor
            __snake_case			:int          =						intermediate_size
            __snake_case			:List[Any]          =						hidden_dropout_prob
            __snake_case			:Optional[int]          =						attention_probs_dropout_prob
            __snake_case			:Union[str, Any]          =						max_position_embeddings
            __snake_case			:Dict          =						type_vocab_size
            __snake_case			:Optional[Any]          =						layer_norm_eps
            __snake_case			:Any          =						position_embedding_type
            __snake_case			:Any          =						use_cache
            __snake_case			:Optional[Any]          =						pad_token_id
            __snake_case			:Any          =						bos_token_id
            __snake_case			:Optional[Any]          =						eos_token_id



       @classmethod
       def 					__lowercase						(							cls			,						a__			,						**a__    ) ->      "PretrainedConfig":






            '''simple docstring'''





            __snake_case       , __snake_case			:Tuple          =						cls.get_config_dict(a__			,						**a__    )

            if config_dict.get("""model_type"""    ) == "bridgetower":
                 __snake_case			:str          =						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(a__			,						**a__    )




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Any 				= "bridgetower"



       def __init__(							self			,						a__=True			,						a__="gelu"			,						a__=7_68			,						a__=1			,						a__=1e-05			,						a__=False			,						a__="add"			,						a__=12			,						a__=6			,						a__=False			,						a__=False			,						a__=None			,						a__=None			,						**a__			,						) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						kwargs.pop("""text_config_dict"""			,						a__    )
            __snake_case			:int          =						kwargs.pop("""vision_config_dict"""			,						a__    )

            super().__init__(**a__    )
            __snake_case			:Union[str, Any]          =						share_cross_modal_transformer_layers
            __snake_case			:Dict          =						hidden_act
            __snake_case			:int          =						hidden_size
            __snake_case			:Tuple          =						initializer_factor
            __snake_case			:Any          =						layer_norm_eps
            __snake_case			:List[Any]          =						share_link_tower_layers
            __snake_case			:Optional[Any]          =						link_tower_type
            __snake_case			:Tuple          =						num_attention_heads
            __snake_case			:Optional[Any]          =						num_hidden_layers
            __snake_case			:List[str]          =						tie_word_embeddings
            __snake_case			:Optional[Any]          =						init_layernorm_from_vision_encoder

            if text_config is None:
                 __snake_case			:List[str]          =						{}
                 logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""    )

            if vision_config is None:
                 __snake_case			:List[Any]          =						{}
                 logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""    )

            __snake_case			:Any          =						BridgeTowerTextConfig(**a__    )
            __snake_case			:Any          =						BridgeTowerVisionConfig(**a__    )



       @classmethod
       def 					__lowercase						(							cls			,						a__			,						a__			,						**a__    ) ->      Optional[Any]:






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:str          =						copy.deepcopy(self.__dict__    )
            __snake_case			:Union[str, Any]          =						self.text_config.to_dict()
            __snake_case			:List[str]          =						self.vision_config.to_dict()
            __snake_case			:List[str]          =						self.__class__.model_type
            return output










from __future__ import annotations

lowerCamelCase__							=     list[list[int]]

# assigning initial values to the grid
lowerCamelCase__							=     [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
lowerCamelCase__							=     [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]






def   UpperCamelCase  (       snake_case__     :   Matrix	,snake_case__     :   int	,snake_case__     :   int	,snake_case__     :   int  ):




     '''simple docstring'''






     for i in range(9  ):
          if grid[row][i] == n or grid[i][column] == n:
               return False

     for i in range(3  ):
          for j in range(3  ):
               if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
                    return False

     return True






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for i in range(9  ):
          for j in range(9  ):
               if grid[i][j] == 0:
                    return i, j
     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     if location := find_empty_location(snake_case__  ):
          __snake_case       , __snake_case			:Optional[int]          =						location
     else:
          # If the location is ``None``, then the grid is solved.
          return grid

     for digit in range(1	,10  ):
          if is_safe(snake_case__	,snake_case__	,snake_case__	,snake_case__  ):
               __snake_case			:Union[str, Any]          =						digit

               if sudoku(snake_case__  ) is not None:
                    return grid

               __snake_case			:Tuple          =						0

     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for row in grid:
          for cell in row:
               print(snake_case__	,end=""" """  )
          print()


if __name__ == "__main__":
     # make a copy of grid so that you can compare with the unmodified grid
     for example_grid in (initial_grid, no_solution):
          print("""\nExample grid:\n""" + """=""" * 20)
          print_solution(example_grid)
          print("""\nExample grid solution:""")
          lowerCamelCase__							=     sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("""Cannot find a solution.""")


