
"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """AdaptiveEmbedding""",
		    """TransfoXLForSequenceClassification""",
		    """TransfoXLLMHeadModel""",
		    """TransfoXLModel""",
		    """TransfoXLPreTrainedModel""",
		    """load_tf_weights_in_transfo_xl""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFAdaptiveEmbedding""",
		    """TFTransfoXLForSequenceClassification""",
		    """TFTransfoXLLMHeadModel""",
		    """TFTransfoXLMainLayer""",
		    """TFTransfoXLModel""",
		    """TFTransfoXLPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
		from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_transfo_xl import (
				    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AdaptiveEmbedding,
				    TransfoXLForSequenceClassification,
				    TransfoXLLMHeadModel,
				    TransfoXLModel,
				    TransfoXLPreTrainedModel,
				    load_tf_weights_in_transfo_xl,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_transfo_xl import (
				    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAdaptiveEmbedding,
				    TFTransfoXLForSequenceClassification,
				    TFTransfoXLLMHeadModel,
				    TFTransfoXLMainLayer,
				    TFTransfoXLModel,
				    TFTransfoXLPreTrainedModel,
				)

else:
		import sys

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


"""simple docstring"""







from heapq import heappop, heappush

import numpy as np
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    )  ->     tuple[float | int, list[tuple[int, int]]]:







							"""simple docstring"""


							__snake_case			,   __snake_case     			=       grid.shape
							__snake_case     			=       [-1, 1, 0, 0]
							__snake_case     			=       [0, 0, -1, 1]
							if allow_diagonal:
														dx += [-1, -1, 1, 1]
														dy += [-1, 1, -1, 1]

							__snake_case			,   __snake_case     			=       [(0, source)], set()
							__snake_case     			=       np.full((rows, cols)							,    np.inf )
							__snake_case     			=       0
							__snake_case     			=       np.empty((rows, cols)							,    dtype=SCREAMING_SNAKE_CASE )
							__snake_case     			=       None

							while queue:
														((__snake_case)			,   (__snake_case))     			=       heappop(SCREAMING_SNAKE_CASE )
														if (x, y) in visited:
																					continue
														visited.add((x, y) )

														if (x, y) == destination:
																					__snake_case     			=       []
																					while (x, y) != source:
																												path.append((x, y) )
																												__snake_case			,   __snake_case     			=       predecessors[x, y]
																					path.append(SCREAMING_SNAKE_CASE )  # add the source manually
																					path.reverse()
																					return matrix[destination], path

														for i in range(len(SCREAMING_SNAKE_CASE ) ):
																					__snake_case			,   __snake_case     			=       x + dx[i], y + dy[i]
																					if 0 <= nx < rows and 0 <= ny < cols:
																												__snake_case     			=       grid[nx][ny]
																												if next_node == 1 and matrix[nx, ny] > dist + 1:
																																			heappush(SCREAMING_SNAKE_CASE							,    (dist + 1, (nx, ny)) )
																																			__snake_case     			=       dist + 1
																																			__snake_case     			=       (x, y)

							return np.inf, []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
