





from __future__ import annotations




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_    ,							UpperCAmelCase_           =position
   UpperCAmelCase_           =[
       (y + 1, x + 2),
       (y - 1, x + 2),
       (y + 1, x - 2),
       (y - 1, x - 2),
       (y + 2, x + 1),
       (y + 2, x - 1),
       (y - 2, x + 1),
       (y - 2, x - 1),
   ]
   UpperCAmelCase_           =[]

   for position in positions:
      UpperCAmelCase_    ,							UpperCAmelCase_           =position
      if 0 <= y_test < n and 0 <= x_test < n:
         permissible_positions.append(lowercase__	)

   return permissible_positions




def      a__			(       lowercase__	):






   '''simple docstring'''






   return not any(elem == 0 for row in board for elem in row	)




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   if is_complete(lowercase__	):
      return True

   for position in get_valid_pos(lowercase__	,     len(lowercase__	)	):
      UpperCAmelCase_    ,							UpperCAmelCase_           =position

      if board[y][x] == 0:
         UpperCAmelCase_           =curr + 1
         if open_knight_tour_helper(lowercase__	,     lowercase__	,     curr + 1	):
            return True
         UpperCAmelCase_           =0

   return False




def      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =[[0 for i in range(lowercase__	)] for j in range(lowercase__	)]

   for i in range(lowercase__	):
      for j in range(lowercase__	):
         UpperCAmelCase_           =1
         if open_knight_tour_helper(lowercase__	,     (i, j)	,     1	):
            return board
         UpperCAmelCase_           =0

   UpperCAmelCase_           =F'Open Kight Tour cannot be performed on a board of size {n}'
   raise ValueError(lowercase__	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






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     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained(lowercase			)
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= tokenizer('''This is me'''   ,     return_tensors='''pt'''			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

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

				SCREAMING_SNAKE_CASE     						= model.generate(**lowercase			)

				SCREAMING_SNAKE_CASE     						= 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(lowercase			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

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

							SCREAMING_SNAKE_CASE     						= model_reloaded.generate(**lowercase			)
							self.assertTrue(torch.allclose(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

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

							SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()
							model.save_pretrained(lowercase			)