"""simple docstring"""





from typing import TYPE_CHECKING

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


__A  =				{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A  =				[
        """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """SwinForImageClassification""",
        """SwinForMaskedImageModeling""",
        """SwinModel""",
        """SwinPreTrainedModel""",
        """SwinBackbone""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A  =				[
        """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFSwinForImageClassification""",
        """TFSwinForMaskedImageModeling""",
        """TFSwinModel""",
        """TFSwinPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_swin import (
            SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
            SwinModel,
            SwinPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_swin import (
            TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFSwinForImageClassification,
            TFSwinForMaskedImageModeling,
            TFSwinModel,
            TFSwinPreTrainedModel,
        )

else:
    import sys

    __A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
"""simple docstring"""





from __future__ import annotations


def    lowercase_   (   _lowerCamelCase:	list[list[int]]      ) ->      bool:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     len(_lowerCamelCase      )
							# We need to create solution object to save path.
							__lowerCamelCase					:		List[Any]		   =     [[0 for _ in range(_lowerCamelCase      )] for _ in range(_lowerCamelCase      )]
							__lowerCamelCase					:		Dict		   =     run_maze(_lowerCamelCase						,   0						,   0						,   _lowerCamelCase      )
							if solved:
														print("\n".join(str(_lowerCamelCase      ) for row in solutions      )      )
							else:
														print("No solution exists!"      )
							return solved


def    lowercase_   (   _lowerCamelCase:	list[list[int]]						,   _lowerCamelCase:	int						,   _lowerCamelCase:	int						,   _lowerCamelCase:	list[list[int]]      ) ->      bool:



							'''simple docstring'''

							__lowerCamelCase					:		Dict		   =     len(_lowerCamelCase      )
							# Final check point.
							if i == j == (size - 1):
														__lowerCamelCase					:		Optional[Any]		   =     1
														return True

							__lowerCamelCase					:		int		   =     (not i < 0) and (not j < 0)  # Check lower bounds
							__lowerCamelCase					:		Union[str, Any]		   =     (i < size) and (j < size)  # Check upper bounds

							if lower_flag and upper_flag:
														# check for already visited and block points.
														__lowerCamelCase					:		List[Any]		   =     (not solutions[i][j]) and (not maze[i][j])
														if block_flag:
																					# check visited
																					__lowerCamelCase					:		Dict		   =     1

																					# check for directions
																					if (
																					    run_maze(_lowerCamelCase						,   i + 1						,   _lowerCamelCase						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   _lowerCamelCase						,   j + 1						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   i - 1						,   _lowerCamelCase						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   _lowerCamelCase						,   j - 1						,   _lowerCamelCase      )
																					):
																												return True

																					__lowerCamelCase					:		List[str]		   =     0
																					return False
							return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()