






import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		@require_torch
		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
							# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
							# while running an external program

							# python one-liner segments

							# this must be loaded before socket.socket is monkey-patched
							UpperCAmelCase_    :      str            =						'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

							UpperCAmelCase_    :      Union[str, Any]            =						'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

							UpperCAmelCase_    :      Optional[int]            =						'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

							# Force fetching the files so that we can use the cache
							UpperCAmelCase_    :      List[Any]            =						'hf-internal-testing/tiny-random-bert'
							BertConfig.from_pretrained(_UpperCamelCase   )
							BertModel.from_pretrained(_UpperCamelCase   )
							BertTokenizer.from_pretrained(_UpperCamelCase   )
							pipeline(task='fill-mask'				,		model=_UpperCamelCase   )

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_    :      Dict            =						[sys.executable, '-c', '\n'.join([load, run, mock]   )]

							# should succeed
							UpperCAmelCase_    :      str            =						self.get_env()
							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCAmelCase_    :      Tuple            =						'1'
							UpperCAmelCase_    :      Any            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )



		@require_torch
		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							# python one-liner segments
							# this must be loaded before socket.socket is monkey-patched
							UpperCAmelCase_    :      Optional[int]            =						'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

							UpperCAmelCase_    :      List[Any]            =						'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

							UpperCAmelCase_    :      List[str]            =						'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

							# Force fetching the files so that we can use the cache
							UpperCAmelCase_    :      Optional[int]            =						'hf-internal-testing/tiny-random-bert'
							BertConfig.from_pretrained(_UpperCamelCase   )
							BertModel.from_pretrained(_UpperCamelCase   )
							BertTokenizer.from_pretrained(_UpperCamelCase   )
							pipeline(task='fill-mask'				,		model=_UpperCamelCase   )

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_    :      str            =						[sys.executable, '-c', '\n'.join([load, run, mock]   )]

							# should succeed
							UpperCAmelCase_    :      Tuple            =						self.get_env()
							UpperCAmelCase_    :      Union[str, Any]            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )



		@require_torch
		def 					__UpperCAmelCase			(							self   )	->							str:
							# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
							# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
							# while running an external program

							# python one-liner segments

							# this must be loaded before socket.socket is monkey-patched
							UpperCAmelCase_    :      str            =						'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

							UpperCAmelCase_    :      List[Any]            =						'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

							UpperCAmelCase_    :      str            =						'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_    :      List[Any]            =						[sys.executable, '-c', '\n'.join([load, run]   )]

							# should succeed
							UpperCAmelCase_    :      str            =						self.get_env()
							UpperCAmelCase_    :      str            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )

							# next emulate no network
							UpperCAmelCase_    :      str            =						[sys.executable, '-c', '\n'.join([load, mock, run]   )]

							# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
							# env["TRANSFORMERS_OFFLINE"] = "0"
							# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
							# self.assertEqual(result.returncode, 1, result.stderr)

							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCAmelCase_    :      List[Any]            =						'1'
							UpperCAmelCase_    :      int            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )



		@require_torch
		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      Any            =						'\nfrom transformers import pipeline\n        '
							UpperCAmelCase_    :      Optional[int]            =						'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

							UpperCAmelCase_    :      Tuple            =						'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
							UpperCAmelCase_    :      str            =						self.get_env()
							UpperCAmelCase_    :      Optional[Any]            =						'1'
							UpperCAmelCase_    :      str            =						[sys.executable, '-c', '\n'.join([load, mock, run]   )]
							UpperCAmelCase_    :      List[Any]            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		1				,		result.stderr   )
							self.assertIn(
							    'You cannot infer task automatically within `pipeline` when using offline mode'				,		result.stderr.decode().replace('\n'				,		''   )				,		)



		@require_torch
		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      Dict            =						'\nfrom transformers import AutoModel\n        '
							UpperCAmelCase_    :      str            =						'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_    :      Union[str, Any]            =						[sys.executable, '-c', '\n'.join([load, run]   )]

							# should succeed
							UpperCAmelCase_    :      Any            =						self.get_env()
							UpperCAmelCase_    :      List[Any]            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )

							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCAmelCase_    :      Tuple            =						'1'
							UpperCAmelCase_    :      Optional[Any]            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )














from __future__ import annotations




def      lowercase__      (     __snake_case    :							tuple[int, int]     ,       __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						position
					UpperCAmelCase_    :      str            =						[
					    (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_    :      Optional[Any]            =						[]

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

					return permissible_positions




def      lowercase__      (     __snake_case    :							list[list[int]]  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							list[list[int]]     ,       __snake_case    :							tuple[int, int]     ,       __snake_case    :							int  ):


					'''simple docstring'''


					if is_complete(__snake_case  ):
										return True

					for position in get_valid_pos(__snake_case     ,       len(__snake_case  )  ):
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						position

										if board[y][x] == 0:
															UpperCAmelCase_    :      Optional[Any]            =						curr + 1
															if open_knight_tour_helper(__snake_case     ,       __snake_case     ,       curr + 1  ):
																				return True
															UpperCAmelCase_    :      List[Any]            =						0

					return False




def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						[[0 for i in range(__snake_case  )] for j in range(__snake_case  )]

					for i in range(__snake_case  ):
										for j in range(__snake_case  ):
															UpperCAmelCase_    :      Optional[Any]            =						1
															if open_knight_tour_helper(__snake_case     ,       (i, j)     ,       1  ):
																				return board
															UpperCAmelCase_    :      List[Any]            =						0

					UpperCAmelCase_    :      List[str]            =						F"Open Kight Tour cannot be performed on a board of size {n}"
					raise ValueError(__snake_case  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






