




from __future__ import annotations




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	list[tuple[int, int]]:

    """simple docstring"""


    __UpperCamelCase		,      __UpperCamelCase  		=						position
    __UpperCamelCase  		=						[
        (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  		=						[]

    for position in positions:
        __UpperCamelCase		,      __UpperCamelCase  		=						position
        if 0 <= y_test < n and 0 <= x_test < n:
            permissible_positions.append(lowercase_      )

    return permissible_positions




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	bool:

    """simple docstring"""


    return not any(elem == 0 for row in board for elem in row      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	bool:

    """simple docstring"""


    if is_complete(lowercase_      ):
        return True

    for position in get_valid_pos(lowercase_					,     len(lowercase_      )      ):
        __UpperCamelCase		,      __UpperCamelCase  		=						position

        if board[y][x] == 0:
            __UpperCamelCase  		=						curr + 1
            if open_knight_tour_helper(lowercase_					,     lowercase_					,     curr + 1      ):
                return True
            __UpperCamelCase  		=						0

    return False




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	list[list[int]]:

    """simple docstring"""


    __UpperCamelCase  		=						[[0 for i in range(lowercase_      )] for j in range(lowercase_      )]

    for i in range(lowercase_      ):
        for j in range(lowercase_      ):
            __UpperCamelCase  		=						1
            if open_knight_tour_helper(lowercase_					,     (i, j)					,     1      ):
                return board
            __UpperCamelCase  		=						0

    __UpperCamelCase  		=						F"Open Kight Tour cannot be performed on a board of size {n}"
    raise ValueError(lowercase_      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_   		=     {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     ["LongformerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongformerForMaskedLM",
     "LongformerForMultipleChoice",
     "LongformerForQuestionAnswering",
     "LongformerForSequenceClassification",
     "LongformerForTokenClassification",
     "LongformerModel",
     "LongformerPreTrainedModel",
     "LongformerSelfAttention",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLongformerForMaskedLM",
     "TFLongformerForMultipleChoice",
     "TFLongformerForQuestionAnswering",
     "TFLongformerForSequenceClassification",
     "TFLongformerForTokenClassification",
     "TFLongformerModel",
     "TFLongformerPreTrainedModel",
     "TFLongformerSelfAttention",
 ]


if TYPE_CHECKING:
 from .configuration_longformer import (
     LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LongformerConfig,
     LongformerOnnxConfig,
 )
 from .tokenization_longformer import LongformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_longformer_fast import LongformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longformer import (
      LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongformerForMaskedLM,
      LongformerForMultipleChoice,
      LongformerForQuestionAnswering,
      LongformerForSequenceClassification,
      LongformerForTokenClassification,
      LongformerModel,
      LongformerPreTrainedModel,
      LongformerSelfAttention,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_longformer import (
      TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLongformerForMaskedLM,
      TFLongformerForMultipleChoice,
      TFLongformerForQuestionAnswering,
      TFLongformerForSequenceClassification,
      TFLongformerForTokenClassification,
      TFLongformerModel,
      TFLongformerPreTrainedModel,
      TFLongformerSelfAttention,
  )

else:
 import sys

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






