"""simple docstring"""





from heapq import heappop, heappush

import numpy as np





def        a_  (   _lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				tuple[int, int]     ,				_lowerCAmelCase						:				tuple[int, int]     ,				_lowerCAmelCase						:				bool     ,				):





       '''simple docstring'''

       lowercase__					,   lowercase__    :	Optional[int]     					=			grid.shape
       lowercase__    :	List[Any]     					=			[-1, 1, 0, 0]
       lowercase__    :	str     					=			[0, 0, -1, 1]
       if allow_diagonal:
              dx += [-1, -1, 1, 1]
              dy += [-1, 1, -1, 1]

       lowercase__					,   lowercase__    :	List[str]     					=			[(0, source)], set()
       lowercase__    :	Optional[int]     					=			np.full((rows, cols)     ,				np.inf  )
       lowercase__    :	Union[str, Any]     					=			0
       lowercase__    :	List[str]     					=			np.empty((rows, cols)     ,				dtype=_lowerCAmelCase  )
       lowercase__    :	Any     					=			None

       while queue:
              ((lowercase__)					,   (lowercase__))    :	Union[str, Any]     					=			heappop(_lowerCAmelCase  )
              if (x, y) in visited:
                     continue
              visited.add((x, y)  )

              if (x, y) == destination:
                     lowercase__    :	str     					=			[]
                     while (x, y) != source:
                            path.append((x, y)  )
                            lowercase__					,   lowercase__    :	Union[str, Any]     					=			predecessors[x, y]
                     path.append(_lowerCAmelCase  )  # add the source manually
                     path.reverse()
                     return matrix[destination], path

              for i in range(len(_lowerCAmelCase  )  ):
                     lowercase__					,   lowercase__    :	Any     					=			x + dx[i], y + dy[i]
                     if 0 <= nx < rows and 0 <= ny < cols:
                            lowercase__    :	Any     					=			grid[nx][ny]
                            if next_node == 1 and matrix[nx, ny] > dist + 1:
                                   heappush(_lowerCAmelCase     ,				(dist + 1, (nx, ny))  )
                                   lowercase__    :	List[str]     					=			dist + 1
                                   lowercase__    :	Optional[int]     					=			(x, y)

       return np.inf, []


if __name__ == "__main__":
       import doctest

       doctest.testmod()


"""simple docstring"""





from typing import TYPE_CHECKING

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


_UpperCamelCase     :		str											=					{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Tuple											=					[
           "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTNeoForCausalLM",
           "GPTNeoForQuestionAnswering",
           "GPTNeoForSequenceClassification",
           "GPTNeoForTokenClassification",
           "GPTNeoModel",
           "GPTNeoPreTrainedModel",
           "load_tf_weights_in_gpt_neo",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Dict											=					[
           "FlaxGPTNeoForCausalLM",
           "FlaxGPTNeoModel",
           "FlaxGPTNeoPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neo import (
                  GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoForCausalLM,
                  GPTNeoForQuestionAnswering,
                  GPTNeoForSequenceClassification,
                  GPTNeoForTokenClassification,
                  GPTNeoModel,
                  GPTNeoPreTrainedModel,
                  load_tf_weights_in_gpt_neo,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
       import sys

       _UpperCamelCase     :		List[str]											=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

