



'''simple docstring'''






import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						import torch

						from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







	"""simple docstring"""






	@slow
	@unittest.skip('''Model is not available.'''		)
	def 					A     (						self      :					List[str]		)      ->		List[Any]:
								UpperCAmelCase			:	str										=		'''nvidia/megatron-gpt2-345m/'''
								if "MYDIR" in os.environ:
															UpperCAmelCase			:	List[Any]										=		os.path.join(os.environ['''MYDIR''']		,      _UpperCamelCase		)
								UpperCAmelCase			:	Optional[int]										=		GPTaLMHeadModel.from_pretrained(_UpperCamelCase		)
								model.to(_UpperCamelCase		)
								model.half()

								UpperCAmelCase			:	str										=		torch.tensor(
								    [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]		,      device=_UpperCamelCase		,      dtype=torch.long		,      )

								with torch.no_grad():
															UpperCAmelCase			:	List[Any]										=		model(_UpperCamelCase		).logits

								UpperCAmelCase			:	str										=		torch.Size((1, 9, 50257)		)
								self.assertEqual(output.shape		,      _UpperCamelCase		)

								UpperCAmelCase			:	int										=		torch.tensor(
								    [
								        4.94_14,
								        -0.29_20,
								        -1.21_48,
								        -4.02_73,
								        -0.51_61,
								        -5.21_09,
								        -1.24_12,
								        -1.83_01,
								        -1.77_34,
								        -4.71_48,
								        -0.23_17,
								        -1.08_11,
								        -2.17_77,
								        0.41_41,
								        -3.79_69,
								        -4.05_86,
								        -2.53_32,
								        -3.38_09,
								        4.38_67,
								    ]		,      device=_UpperCamelCase		,      dtype=torch.half		,      )

								for i in range(19		):
															UpperCAmelCase							,		UpperCAmelCase			:	Union[str, Any]										=		8 * i // 17, 2792 * i  # along the diagonal
															UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		output[0, r, c], expected_diag[i]
															UpperCAmelCase			:	Union[str, Any]										=		F"""row={r} col={c} computed={computed} expected={expected}"""
															self.assertAlmostEqual(_UpperCamelCase		,      _UpperCamelCase		,      delta=1E-4		,      msg=_UpperCamelCase		)










'''simple docstring'''






UpperCamelCase__:       Any							     =      [[0, 1, 1], [0, 0, 1], [1, 0, 0]]

class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self      :					List[str]		,      __snake_case      :					int		)      ->		int:
								UpperCAmelCase			:	Any										=		name
								UpperCAmelCase			:	Optional[Any]										=		[]
								UpperCAmelCase			:	Any										=		[]







	def 					A     (						self      :					Dict		,      __snake_case      :					str		)      ->		Tuple:
								self.inbound.append(__snake_case		)







	def 					A     (						self      :					Union[str, Any]		,      __snake_case      :					str		)      ->		Optional[int]:
								self.outbound.append(__snake_case		)







	def __repr__(						self      :					Optional[int]		)      ->		Optional[int]:
								return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 			snake_case_					( _lowerCAmelCase  :						Tuple       ,				_lowerCAmelCase  :						Dict=3       ,				_lowerCAmelCase  :						Tuple=0.8_5  )				->       Optional[Any]:
							UpperCAmelCase			:	Tuple										=		{}
							for node in nodes:
														UpperCAmelCase			:	List[str]										=		1

							UpperCAmelCase			:	List[Any]										=		{}
							for node in nodes:
														UpperCAmelCase			:	str										=		len(node.outbound  )

							for i in range(_lowerCAmelCase  ):
														print(f"""======= Iteration {i + 1} ======="""  )
														for _, node in enumerate(_lowerCAmelCase  ):
																					UpperCAmelCase			:	str										=		(1 - d) + d * sum(
																					    ranks[ib] / outbounds[ib] for ib in node.inbound  )
														print(_lowerCAmelCase  )


def 			snake_case_					( )				->       List[str]:
							UpperCAmelCase			:	str										=		list(input('''Enter Names of the Nodes: '''  ).split()  )

							UpperCAmelCase			:	Dict										=		[Node(_lowerCAmelCase  ) for name in names]

							for ri, row in enumerate(_lowerCAmelCase  ):
														for ci, col in enumerate(_lowerCAmelCase  ):
																					if col == 1:
																												nodes[ci].add_inbound(names[ri]  )
																												nodes[ri].add_outbound(names[ci]  )

							print('''======= Nodes ======='''  )
							for node in nodes:
														print(_lowerCAmelCase  )

							page_rank(_lowerCAmelCase  )


if __name__ == "__main__":
						main()





