





'''simple docstring'''

def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int   )						->int:
    return len_board * len_board_column * row + column





def lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   set[int] ,	)						->bool:

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

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

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

        _SCREAMING_SNAKE_CASE     =      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 lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str   )						->bool:
    _SCREAMING_SNAKE_CASE     =      (
        """The board should be a non empty matrix of single chars strings."""
    )

    _SCREAMING_SNAKE_CASE     =      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."""   )

    _SCREAMING_SNAKE_CASE     =      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()










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowercase_         =			logging.get_logger(__name__)

lowercase_         =			{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''rembert'''


    def __init__(		self			,		A=25_0300			,		A=1152			,		A=32			,		A=18			,		A=256			,		A=1664			,		A=4608			,		A="gelu"			,		A=0.0			,		A=0.0			,		A=0.1			,		A=512			,		A=2			,		A=0.02			,		A=1e-12			,		A=True			,		A=0			,		A=312			,		A=313			,		**A			,		)		->       Dict:
        super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A	)

        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      input_embedding_size
        _SCREAMING_SNAKE_CASE     =      output_embedding_size
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      hidden_act
        _SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
        _SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE     =      classifier_dropout_prob
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      type_vocab_size
        _SCREAMING_SNAKE_CASE     =      layer_norm_eps
        _SCREAMING_SNAKE_CASE     =      use_cache
        _SCREAMING_SNAKE_CASE     =      False




class  a_					(    snake_case_	):

    '''simple docstring'''


    @property
    def       snake_case_(		self	)		->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]	)





    @property
    def       snake_case_(		self	)		->       float:
        return 1e-4



