

'''simple docstring'''

from __future__ import annotations




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase ,			UpperCAmelCase	:     Tuple						 =			position
 UpperCAmelCase	:     List[Any]						 =			[
     (y + 1, x + 2),
     (y - 1, x + 2),
     (y + 1, x - 2),
     (y - 1, x - 2),
     (y + 2, x + 1),
     (y + 2, x - 1),
     (y - 2, x + 1),
     (y - 2, x - 1),
 ]
 UpperCAmelCase	:     List[Any]						 =			[]

 for position in positions:
  UpperCAmelCase ,			UpperCAmelCase	:     Optional[Any]						 =			position
  if 0 <= y_test < n and 0 <= x_test < n:
   permissible_positions.append(UpperCAmelCase_     )

 return permissible_positions




def 				UpperCamelCase(					UpperCAmelCase_     ):
 return not any(elem == 0 for row in board for elem in row     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):

 if is_complete(UpperCAmelCase_     ):
  return True

 for position in get_valid_pos(UpperCAmelCase_  ,	len(UpperCAmelCase_     )     ):
  UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			position

  if board[y][x] == 0:
   UpperCAmelCase	:     Optional[Any]						 =			curr + 1
   if open_knight_tour_helper(UpperCAmelCase_  ,	UpperCAmelCase_  ,	curr + 1     ):
    return True
   UpperCAmelCase	:     Tuple						 =			0

 return False




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     str						 =			[[0 for i in range(UpperCAmelCase_     )] for j in range(UpperCAmelCase_     )]

 for i in range(UpperCAmelCase_     ):
  for j in range(UpperCAmelCase_     ):
   UpperCAmelCase	:     Optional[int]						 =			1
   if open_knight_tour_helper(UpperCAmelCase_  ,	(i, j)  ,	1     ):
    return board
   UpperCAmelCase	:     int						 =			0

 UpperCAmelCase	:     Optional[int]						 =			F"""Open Kight Tour cannot be performed on a board of size {n}"""
 raise ValueError(UpperCAmelCase_     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__         =      {
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLMForMultipleChoice",
     "XLMForQuestionAnswering",
     "XLMForQuestionAnsweringSimple",
     "XLMForSequenceClassification",
     "XLMForTokenClassification",
     "XLMModel",
     "XLMPreTrainedModel",
     "XLMWithLMHeadModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFXLMForMultipleChoice",
     "TFXLMForQuestionAnsweringSimple",
     "TFXLMForSequenceClassification",
     "TFXLMForTokenClassification",
     "TFXLMMainLayer",
     "TFXLMModel",
     "TFXLMPreTrainedModel",
     "TFXLMWithLMHeadModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
 from .tokenization_xlm import XLMTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm import (
      XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMForMultipleChoice,
      XLMForQuestionAnswering,
      XLMForQuestionAnsweringSimple,
      XLMForSequenceClassification,
      XLMForTokenClassification,
      XLMModel,
      XLMPreTrainedModel,
      XLMWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm import (
      TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMForMultipleChoice,
      TFXLMForQuestionAnsweringSimple,
      TFXLMForSequenceClassification,
      TFXLMForTokenClassification,
      TFXLMMainLayer,
      TFXLMModel,
      TFXLMPreTrainedModel,
      TFXLMWithLMHeadModel,
  )

else:
 import sys

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




