





"""simple docstring"""




import subprocess
import sys

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


class        __UpperCamelCase				(						a__						):






							@require_torch
							def     __a   (   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
								a      :		Union[str, Any]	=				"\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

								a      :		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        "

								a      :		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
								a      :		Optional[int]	=				"hf-internal-testing/tiny-random-bert"
								BertConfig.from_pretrained(lowerCAmelCase__      )
								BertModel.from_pretrained(lowerCAmelCase__      )
								BertTokenizer.from_pretrained(lowerCAmelCase__      )
								pipeline(task="fill-mask"      ,					model=lowerCAmelCase__      )

								# baseline - just load from_pretrained with normal network
								a      :		Optional[Any]	=				[sys.executable, "-c", "\n".join([load, run, mock]      )]

								# should succeed
								a      :		Dict	=				self.get_env()
								# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
								a      :		Any	=				"1"
								a      :		List[str]	=				subprocess.run(lowerCAmelCase__      ,					env=lowerCAmelCase__      ,					check=lowerCAmelCase__      ,					capture_output=lowerCAmelCase__      )
								self.assertEqual(result.returncode      ,					0      ,					result.stderr      )
								self.assertIn("success"      ,					result.stdout.decode()      )






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

								a      :		List[str]	=				"\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        "

								a      :		Optional[int]	=				"\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
								a      :		List[Any]	=				"hf-internal-testing/tiny-random-bert"
								BertConfig.from_pretrained(lowerCAmelCase__      )
								BertModel.from_pretrained(lowerCAmelCase__      )
								BertTokenizer.from_pretrained(lowerCAmelCase__      )
								pipeline(task="fill-mask"      ,					model=lowerCAmelCase__      )

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

								# should succeed
								a      :		str	=				self.get_env()
								a      :		int	=				subprocess.run(lowerCAmelCase__      ,					env=lowerCAmelCase__      ,					check=lowerCAmelCase__      ,					capture_output=lowerCAmelCase__      )
								self.assertEqual(result.returncode      ,					0      ,					result.stderr      )
								self.assertIn("success"      ,					result.stdout.decode()      )






							@require_torch
							def     __a   (   self      )	-> Tuple:
								# 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
								a      :		Optional[int]	=				"\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        "

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

								a      :		List[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
								a      :		int	=				[sys.executable, "-c", "\n".join([load, run]      )]

								# should succeed
								a      :		Dict	=				self.get_env()
								a      :		Optional[Any]	=				subprocess.run(lowerCAmelCase__      ,					env=lowerCAmelCase__      ,					check=lowerCAmelCase__      ,					capture_output=lowerCAmelCase__      )
								self.assertEqual(result.returncode      ,					0      ,					result.stderr      )
								self.assertIn("success"      ,					result.stdout.decode()      )

								# next emulate no network
								a      :		Tuple	=				[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
								a      :		List[Any]	=				"1"
								a      :		Optional[int]	=				subprocess.run(lowerCAmelCase__      ,					env=lowerCAmelCase__      ,					check=lowerCAmelCase__      ,					capture_output=lowerCAmelCase__      )
								self.assertEqual(result.returncode      ,					0      ,					result.stderr      )
								self.assertIn("success"      ,					result.stdout.decode()      )






							@require_torch
							def     __a   (   self      )	-> Optional[Any]:
								a      :		Union[str, Any]	=				"\nfrom transformers import pipeline\n        "
								a      :		Any	=				"\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n        "

								a      :		List[Any]	=				"\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "
								a      :		List[Any]	=				self.get_env()
								a      :		Optional[int]	=				"1"
								a      :		Tuple	=				[sys.executable, "-c", "\n".join([load, mock, run]      )]
								a      :		List[Any]	=				subprocess.run(lowerCAmelCase__      ,					env=lowerCAmelCase__      ,					check=lowerCAmelCase__      ,					capture_output=lowerCAmelCase__      )
								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     __a   (   self      )	-> Tuple:
								a      :		Optional[int]	=				"\nfrom transformers import AutoModel\n        "
								a      :		int	=				"\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
								a      :		Optional[Any]	=				[sys.executable, "-c", "\n".join([load, run]      )]

								# should succeed
								a      :		List[str]	=				self.get_env()
								a      :		Optional[int]	=				subprocess.run(lowerCAmelCase__      ,					env=lowerCAmelCase__      ,					check=lowerCAmelCase__      ,					capture_output=lowerCAmelCase__      )
								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
								a      :		Union[str, Any]	=				"1"
								a      :		Dict	=				subprocess.run(lowerCAmelCase__      ,					env=lowerCAmelCase__      ,					check=lowerCAmelCase__      ,					capture_output=lowerCAmelCase__      )
								self.assertEqual(result.returncode      ,					0      ,					result.stderr      )
								self.assertIn("success"      ,					result.stdout.decode()      )









"""simple docstring"""




from __future__ import annotations






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list[int]    ,       _lowercase						:    int					) ->int:






	'''simple docstring'''






	if len(_lowercase					) < k or k < 0:
		raise ValueError("Invalid Input"					)
	a      :		Optional[Any]	=				sum(array[:k]					)
	for i in range(len(_lowercase					) - k					):
		a      :		Optional[Any]	=				current_sum - array[i] + array[i + k]
		a      :		Union[str, Any]	=				max(_lowercase    ,       _lowercase					)
	return max_sum


if __name__ == "__main__":
		from doctest import testmod
		from random import randint

		testmod()
		a						: Any     	=			[randint(-1000, 1000) for i in range(100)]
		a						: List[str]     	=			randint(0, 110)
		print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')


