




from __future__ import annotations


def    A						(__A   :    list[list[int]]       )   ->      bool:







     """simple docstring"""


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


def    A						(__A   :    list[list[int]]   ,				__A   :    int   ,				__A   :    int   ,				__A   :    list[list[int]]       )   ->      bool:







     """simple docstring"""


     UpperCAmelCase_          =							len(__A       )
     # Final check point.
     if i == j == (size - 1):
          UpperCAmelCase_          =							1
          return True

     UpperCAmelCase_          =							(not i < 0) and (not j < 0)  # Check lower bounds
     UpperCAmelCase_          =							(i < size) and (j < size)  # Check upper bounds

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

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

               UpperCAmelCase_          =							0
               return False
     return False


if __name__ == "__main__":
    import doctest

    doctest.testmod()












from typing import TYPE_CHECKING

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


snake_case_ : Dict    		=       {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Tuple    		=       ["MBartTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       ["MBartTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[Any]    		=       [
        "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MBartForCausalLM",
        "MBartForConditionalGeneration",
        "MBartForQuestionAnswering",
        "MBartForSequenceClassification",
        "MBartModel",
        "MBartPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TFMBartForConditionalGeneration",
        "TFMBartModel",
        "TFMBartPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "FlaxMBartForConditionalGeneration",
        "FlaxMBartForQuestionAnswering",
        "FlaxMBartForSequenceClassification",
        "FlaxMBartModel",
        "FlaxMBartPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart import MBartTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart_fast import MBartTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mbart import (
            MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
            MBartForCausalLM,
            MBartForConditionalGeneration,
            MBartForQuestionAnswering,
            MBartForSequenceClassification,
            MBartModel,
            MBartPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mbart import (
            FlaxMBartForConditionalGeneration,
            FlaxMBartForQuestionAnswering,
            FlaxMBartForSequenceClassification,
            FlaxMBartModel,
            FlaxMBartPreTrainedModel,
        )

else:
    import sys

    snake_case_ : Any    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






