






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			snake_case__   (							lowercase_	,    lowercase_	,    lowercase_):

       '''simple docstring'''



       @register_to_config
       def __init__(							self			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__ = False			,						) ->      Tuple:






            '''simple docstring'''





            super().__init__()

            __snake_case			:int          =						nn.Embedding(a__			,						a__    )

            __snake_case			:Any          =						nn.Embedding(a__			,						a__    )
            __snake_case			:Union[str, Any]          =						False

            __snake_case			:Any          =						nn.Dropout(p=a__    )

            __snake_case			:List[str]          =						TaConfig(
                vocab_size=a__			,						d_model=a__			,						num_heads=a__			,						d_kv=a__			,						d_ff=a__			,						dropout_rate=a__			,						feed_forward_proj=a__			,						is_decoder=a__			,						is_encoder_decoder=a__			,						)

            __snake_case			:str          =						nn.ModuleList()
            for lyr_num in range(a__    ):
                 __snake_case			:str          =						TaBlock(a__    )
                 self.encoders.append(a__    )

            __snake_case			:Any          =						TaLayerNorm(a__    )
            __snake_case			:Union[str, Any]          =						nn.Dropout(p=a__    )



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






            '''simple docstring'''





            __snake_case			:Dict          =						self.token_embedder(a__    )

            __snake_case			:Optional[Any]          =						encoder_input_tokens.shape[1]
            __snake_case			:Optional[Any]          =						torch.arange(a__			,						device=encoder_input_tokens.device    )
            x += self.position_encoding(a__    )

            __snake_case			:Optional[int]          =						self.dropout_pre(a__    )

            # inverted the attention mask
            __snake_case			:Tuple          =						encoder_input_tokens.size()
            __snake_case			:Union[str, Any]          =						self.get_extended_attention_mask(a__			,						a__    )

            for lyr in self.encoders:
                 __snake_case			:List[str]          =						lyr(a__			,						a__    )[0]
            __snake_case			:Dict          =						self.layer_norm(a__    )

            return self.dropout_post(a__    ), encoder_inputs_mask










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




     '''simple docstring'''






     __snake_case			:list[list[str]]          =						[[] for _ in range(snake_case__  )]
     __snake_case			:Union[str, Any]          =						key - 1

     if key <= 0:
          raise ValueError("""Height of grid can't be 0 or negative"""  )
     if key == 1 or len(snake_case__  ) <= key:
          return input_string

     for position, character in enumerate(snake_case__  ):
          __snake_case			:Any          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:Optional[int]          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          temp_grid[num].append(snake_case__  )
     __snake_case			:List[Any]          =						["""""".join(snake_case__  ) for row in temp_grid]
     __snake_case			:Any          =						"""""".join(snake_case__  )

     return output_string






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




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						[]
     __snake_case			:Optional[int]          =						key - 1

     if key <= 0:
          raise ValueError("""Height of grid can't be 0 or negative"""  )
     if key == 1:
          return input_string

     __snake_case			:list[list[str]]          =						[[] for _ in range(snake_case__  )]  # generates template
     for position in range(len(snake_case__  )  ):
          __snake_case			:List[str]          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:int          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          temp_grid[num].append("""*"""  )

     __snake_case			:str          =						0
     for row in temp_grid:  # fills in the characters
          __snake_case			:str          =						input_string[counter : counter + len(snake_case__  )]
          grid.append(list(snake_case__  )  )
          counter += len(snake_case__  )

     __snake_case			:Any          =						""""""  # reads as zigzag
     for position in range(len(snake_case__  )  ):
          __snake_case			:Optional[int]          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:int          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          output_string += grid[num][0]
          grid[num].pop(0  )
     return output_string






def   UpperCamelCase  (       snake_case__     :   str  ):




     '''simple docstring'''






     __snake_case			:Optional[Any]          =						{}
     for key_guess in range(1	,len(snake_case__  )  ):  # tries every key
          __snake_case			:Dict          =						decrypt(snake_case__	,snake_case__  )
     return results


if __name__ == "__main__":
     import doctest

     doctest.testmod()


