def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: int ,	lowerCAmelCase_: int ,	lowerCAmelCase_: int     ):
      return len_board * len_board_column * row + column






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: list[list[str]] ,	lowerCAmelCase_: str ,	lowerCAmelCase_: int ,	lowerCAmelCase_: int ,	lowerCAmelCase_: int ,	lowerCAmelCase_: set[int] ,	):

      if board[row][column] != word[word_index]:
            return False

      if word_index == len(lowerCAmelCase_     ) - 1:
            return True

      snake_case_     :			Tuple       =       [(0, 1), (0, -1), (-1, 0), (1, 0)]
      snake_case_     :			List[str]       =       len(lowerCAmelCase_     )
      snake_case_     :			Any       =       len(board[0]     )
      for direction in traverts_directions:
            snake_case_     :			List[Any]       =       row + direction[0]
            snake_case_     :			List[str]       =       column + direction[1]
            if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
                  continue

            snake_case_     :			Dict       =       get_point_key(lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_     )
            if key in visited_points_set:
                  continue

            visited_points_set.add(lowerCAmelCase_     )
            if exits_word(lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	word_index + 1 ,	lowerCAmelCase_     ):
                  return True

            visited_points_set.remove(lowerCAmelCase_     )

      return False






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: list[list[str]] ,	lowerCAmelCase_: str     ):
      snake_case_     :			Union[str, Any]       =       (
          "The board should be a non empty matrix of single chars strings."
      )

      snake_case_     :			Optional[int]       =       len(lowerCAmelCase_     )
      if not isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     ) or len(lowerCAmelCase_     ) == 0:
            raise ValueError(lowerCAmelCase_     )

      for row in board:
            if not isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     ) or len(lowerCAmelCase_     ) == 0:
                  raise ValueError(lowerCAmelCase_     )

            for item in row:
                  if not isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     ) or len(lowerCAmelCase_     ) != 1:
                        raise ValueError(lowerCAmelCase_     )

    # Validate word
      if not isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     ) or len(lowerCAmelCase_     ) == 0:
            raise ValueError(
                "The word parameter should be a string of length greater than 0."     )

      snake_case_     :			Tuple       =       len(board[0]     )
      for i in range(lowerCAmelCase_     ):
            for j in range(lowerCAmelCase_     ):
                  if exits_word(
                      lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	0 ,	{get_point_key(lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_     )}     ):
                        return True

      return False


if __name__ == "__main__":
     import doctest

     doctest.testmod()




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}



class 					snake_case__       (				_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: List[str]						    =      "plbart"
   _SCREAMING_SNAKE_CASE	: Union[str, Any]						    =      ["past_key_values"]
   _SCREAMING_SNAKE_CASE	: Dict						    =      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



   def __init__(				self   :   Dict			,	A__   :   Any=5_00_05			,	A__   :   Optional[int]=10_24			,	A__   :   Tuple=6			,	A__   :   Optional[Any]=30_72			,	A__   :   Dict=12			,	A__   :   Union[str, Any]=6			,	A__   :   Union[str, Any]=30_72			,	A__   :   Tuple=12			,	A__   :   List[str]=0.0			,	A__   :   Dict=0.0			,	A__   :   int=True			,	A__   :   Tuple=True			,	A__   :   List[str]="gelu"			,	A__   :   List[Any]=7_68			,	A__   :   Optional[Any]=0.1			,	A__   :   List[Any]=0.1			,	A__   :   Any=0.0			,	A__   :   int=0.02			,	A__   :   Optional[Any]=0.0			,	A__   :   List[str]=True			,	A__   :   Any=1			,	A__   :   Tuple=0			,	A__   :   Any=2			,	A__   :   int=2			,	**A__   :   Dict			,	)							->     int:




         '''simple docstring'''




         snake_case_     :			int       =       vocab_size
         snake_case_     :			List[Any]       =       max_position_embeddings
         snake_case_     :			str       =       d_model
         snake_case_     :			Tuple       =       encoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       encoder_layers
         snake_case_     :			Dict       =       encoder_attention_heads
         snake_case_     :			Union[str, Any]       =       decoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       decoder_layers
         snake_case_     :			List[str]       =       decoder_attention_heads
         snake_case_     :			List[Any]       =       dropout
         snake_case_     :			Union[str, Any]       =       attention_dropout
         snake_case_     :			Optional[int]       =       activation_dropout
         snake_case_     :			List[str]       =       activation_function
         snake_case_     :			Tuple       =       init_std
         snake_case_     :			str       =       encoder_layerdrop
         snake_case_     :			int       =       decoder_layerdrop
         snake_case_     :			Optional[int]       =       classifier_dropout
         snake_case_     :			List[str]       =       use_cache
         snake_case_     :			List[Any]       =       encoder_layers
         snake_case_     :			Tuple       =       scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=A__			,	bos_token_id=A__			,	eos_token_id=A__			,	is_encoder_decoder=A__			,	forced_eos_token_id=A__			,	**A__			,	)



class 					snake_case__       (				_UpperCamelCase							):



   @property
   def 					UpperCAmelCase__       (				self   :   str   )							->     Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




         return OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "sequence"}),
                 ("attention_mask", {0: "batch", 1: "sequence"}),
             ]   )



   @property
   def 					UpperCAmelCase__       (				self   :   List[str]   )							->     Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




         if self.use_past:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("past_keys", {0: "batch", 2: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )
         else:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )



