
def        __lowercase(							__snake_case				:		int    ,__snake_case				:		int    ,__snake_case				:		int    ,__snake_case				:		int					)       ->   int:
       return len_board * len_board_column * row + column
def        __lowercase(							__snake_case				:		list[list[str]]    ,__snake_case				:		str    ,__snake_case				:		int    ,__snake_case				:		int    ,__snake_case				:		int    ,__snake_case				:		set[int]    ,)       ->   bool:

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

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

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

              __snake_case						       =   get_point_key(__snake_case    ,__snake_case    ,__snake_case    ,__snake_case					)
              if key in visited_points_set:
                     continue

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

              visited_points_set.remove(__snake_case					)

       return False
def        __lowercase(							__snake_case				:		list[list[str]]    ,__snake_case				:		str					)       ->   bool:
       __snake_case						       =   (
           'The board should be a non empty matrix of single chars strings.'
       )

       __snake_case						       =   len(__snake_case					)
       if not isinstance(__snake_case    ,__snake_case					) or len(__snake_case					) == 0:
              raise ValueError(__snake_case					)

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

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

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

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

       return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""deberta"""

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=50_265							,       SCREAMING_SNAKE_CASE_=768							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=3_072							,       SCREAMING_SNAKE_CASE_="gelu"							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=0.0_2							,       SCREAMING_SNAKE_CASE_=1E-7							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=-1							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_="gelu"							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   hidden_size
            __snake_case						       =   num_hidden_layers
            __snake_case						       =   num_attention_heads
            __snake_case						       =   intermediate_size
            __snake_case						       =   hidden_act
            __snake_case						       =   hidden_dropout_prob
            __snake_case						       =   attention_probs_dropout_prob
            __snake_case						       =   max_position_embeddings
            __snake_case						       =   type_vocab_size
            __snake_case						       =   initializer_range
            __snake_case						       =   relative_attention
            __snake_case						       =   max_relative_positions
            __snake_case						       =   pad_token_id
            __snake_case						       =   position_biased_input

            # Backwards compatibility
            if type(SCREAMING_SNAKE_CASE_    ) == str:
                   __snake_case						       =   [x.strip() for x in pos_att_type.lower().split('|'    )]

            __snake_case						       =   pos_att_type
            __snake_case						       =   vocab_size
            __snake_case						       =   layer_norm_eps

            __snake_case						       =   kwargs.get('pooler_hidden_size'							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   pooler_dropout
            __snake_case						       =   pooler_hidden_act







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.task == "multiple-choice":
                   __snake_case						       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]    )
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]    )

     @property
     def 						__lowerCamelCase				(  self    ):
            return 12

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 3							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = None							,       ):
            __snake_case						       =   super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE_							,       framework=SCREAMING_SNAKE_CASE_    )
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs
