


'''simple docstring'''






from __future__ import annotations






def 		_A			(lowerCAmelCase__					:list[list[int]]						)				->					bool:




     '''simple docstring'''



     _a      =							len(lowerCAmelCase__						)
     # We need to create solution object to save path.
     _a      =							[[0 for _ in range(lowerCAmelCase__						)] for _ in range(lowerCAmelCase__						)]
     _a      =							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 		_A			(lowerCAmelCase__					:list[list[int]]			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:list[list[int]]						)				->					bool:




     '''simple docstring'''



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

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

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

               _a      =							0
               return False
     return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXForCausalLM",
     "GPTNeoXForQuestionAnswering",
     "GPTNeoXForSequenceClassification",
     "GPTNeoXForTokenClassification",
     "GPTNeoXLayer",
     "GPTNeoXModel",
     "GPTNeoXPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox import (
      GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXLayer,
      GPTNeoXModel,
      GPTNeoXPreTrainedModel,
  )


else:
 import sys

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

