

'''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   UpperCamelCase__   (       unittest.TestCase       ):


		"""simple docstring"""

		@slow
		@unittest.skip("Model is not available."      )
		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[Any]        =       "nvidia/megatron-gpt2-345m/"
							if "MYDIR" in os.environ:
												UpperCAmelCase		:							Optional[Any]        =       os.path.join(os.environ["MYDIR"]   ,      snake_case      )
							UpperCAmelCase		:							Optional[int]        =       GPTaLMHeadModel.from_pretrained(snake_case      )
							model.to(snake_case      )
							model.half()

							UpperCAmelCase		:							Optional[int]        =       torch.tensor(
							    [[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]   ,      device=snake_case   ,      dtype=torch.long   ,      )

							with torch.no_grad():
												UpperCAmelCase		:							Optional[Any]        =       model(snake_case      ).logits

							UpperCAmelCase		:							Any        =       torch.Size((1, 9, 5_0_2_5_7)      )
							self.assertEqual(output.shape   ,      snake_case      )

							UpperCAmelCase		:							Union[str, Any]        =       torch.tensor(
							    [
							        4.9414,
							        -0.2920,
							        -1.2148,
							        -4.0273,
							        -0.5161,
							        -5.2109,
							        -1.2412,
							        -1.8301,
							        -1.7734,
							        -4.7148,
							        -0.2317,
							        -1.0811,
							        -2.1777,
							        0.4141,
							        -3.7969,
							        -4.0586,
							        -2.5332,
							        -3.3809,
							        4.3867,
							    ]   ,      device=snake_case   ,      dtype=torch.half   ,      )

							for i in range(1_9      ):
												UpperCAmelCase		:							int        =       8 * i // 1_7, 2_7_9_2 * i  # along the diagonal
												UpperCAmelCase		:							str        =       output[0, r, c], expected_diag[i]
												UpperCAmelCase		:							Any        =       f"row={r} col={c} computed={computed} expected={expected}"
												self.assertAlmostEqual(snake_case   ,      snake_case   ,      delta=1e-4   ,      msg=snake_case      )








'''simple docstring'''

a       :      str  	=			[[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class   UpperCamelCase__   :


		"""simple docstring"""

		def __init__(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]        =       name
							UpperCAmelCase		:							Optional[Any]        =       []
							UpperCAmelCase		:							Tuple        =       []







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							self.inbound.append(snake_case      )







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							self.outbound.append(snake_case      )







		def __repr__(			self      ):


							'''simple docstring'''

							return f"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"





def 				lowercase (					__magic_name__			,   __magic_name__=3			,   __magic_name__=0.8_5							):



					'''simple docstring'''







					UpperCAmelCase		:							str        =       {}
					for node in nodes:
										UpperCAmelCase		:							Tuple        =       1

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

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





def 				lowercase (					):



					'''simple docstring'''







					UpperCAmelCase		:							Dict        =       list(input("Enter Names of the Nodes: "							).split()							)

					UpperCAmelCase		:							Any        =       [Node(__magic_name__							) for name in names]

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

					print("======= Nodes ======="							)
					for node in nodes:
										print(__magic_name__							)

					page_rank(__magic_name__							)


if __name__ == "__main__":
				main()
