





'''simple docstring'''






from heapq import heappop, heappush

import numpy as np





def      UpperCAmelCase      (		lowerCamelCase_  :np.ndarray				,      lowerCamelCase_  :tuple[int, int]				,      lowerCamelCase_  :tuple[int, int]				,      lowerCamelCase_  :bool				,      ):







		'''simple docstring'''
		snake_case_		:							str			       =						grid.shape
		snake_case_		:							Optional[Any]			       =						[-1, 1, 0, 0]
		snake_case_		:							Optional[int]			       =						[0, 0, -1, 1]
		if allow_diagonal:
				dx += [-1, -1, 1, 1]
				dy += [-1, 1, -1, 1]

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

		while queue:
				(snake_case_)		:							Optional[Any]			       =						heappop(lowerCamelCase_ )
				if (x, y) in visited:
						continue
				visited.add((x, y) )

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

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

		return np.inf, []


if __name__ == "__main__":
							import doctest

							doctest.testmod()



'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch
@require_torch
@require_optimum
@slow
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoTokenizer.from_pretrained(_UpperCamelCase			)
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						tokenizer("""This is me"""  ,return_tensors="""pt"""			)

									snake_case_		:							List[str]			       =						model.to_bettertransformer()

									self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									snake_case_		:							List[str]			       =						model.generate(**_UpperCamelCase			)

									snake_case_		:							Dict			       =						model.reverse_bettertransformer()

									self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

											self.assertFalse(
											    any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

											snake_case_		:							Optional[Any]			       =						model_reloaded.generate(**_UpperCamelCase			)
											self.assertTrue(torch.allclose(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						model.to_bettertransformer()

									with tempfile.TemporaryDirectory() as tmpdirname:
											with self.assertRaises(_UpperCamelCase			):
													model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							Any			       =						model.reverse_bettertransformer()
											model.save_pretrained(_UpperCamelCase			)