



def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int  ,      _snake_case  :Optional[Any]  ,      _snake_case  :Any  ,      _snake_case  :List[str]							)     ->    int:
			return len_board * len_board_column * row + column

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :str  ,      _snake_case  :List[Any]  ,      _snake_case  :Tuple  ,      _snake_case  :Optional[int]  ,      _snake_case  :List[Any]  ,      _snake_case  :Optional[Any]  ,      )     ->    bool:







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

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

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

						_A						     =					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   SCREAMING_SNAKE_CASE_       (  _snake_case  :str  ,      _snake_case  :Optional[int]							)     ->    bool:
			_A						     =					(
			    '''The board should be a non empty matrix of single chars strings.'''
			)

			_A						     =					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.'''							)

			_A						     =					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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase_		   =				{
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_		   =				["""XLMProphetNetTokenizer"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_		   =				[
       """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XLMProphetNetDecoder""",
       """XLMProphetNetEncoder""",
       """XLMProphetNetForCausalLM""",
       """XLMProphetNetForConditionalGeneration""",
       """XLMProphetNetModel""",
       """XLMProphetNetPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm_prophetnet import (
          XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMProphetNetDecoder,
          XLMProphetNetEncoder,
          XLMProphetNetForCausalLM,
          XLMProphetNetForConditionalGeneration,
          XLMProphetNetModel,
          XLMProphetNetPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase_		   =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



