




"""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  __lowercase    (     unittest.TestCase		):


				'''simple docstring'''


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

										snake_case__    :   Any							     =   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=SCREAMING_SNAKE_CASE_ ,dtype=torch.long ,)

										with torch.no_grad():
																snake_case__    :   Optional[Any]							     =   model(SCREAMING_SNAKE_CASE_ ).logits

										snake_case__    :   Dict							     =   torch.Size((1, 9, 5_0_2_5_7) )
										self.assertEqual(output.shape ,SCREAMING_SNAKE_CASE_ )

										snake_case__    :   str							     =   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=SCREAMING_SNAKE_CASE_ ,dtype=torch.half ,)

										for i in range(1_9 ):
																snake_case__     ,		snake_case__    :   str							     =   8 * i // 1_7, 2_7_9_2 * i  # along the diagonal
																snake_case__     ,		snake_case__    :   Tuple							     =   output[0, r, c], expected_diag[i]
																snake_case__    :   Dict							     =   F'''row={r} col={c} computed={computed} expected={expected}'''
																self.assertAlmostEqual(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,delta=1E-4 ,msg=SCREAMING_SNAKE_CASE_ )








"""simple docstring"""





from __future__ import annotations

import math
import random

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Tuple							     =   d * e - 1
						snake_case__    :   Optional[int]							     =   0
						snake_case__    :   Tuple							     =   0
						while p == 0:
												snake_case__    :   Optional[int]							     =   random.randint(2 ,     n - 1						)
												snake_case__    :   Tuple							     =   k
												while True:
																		if t % 2 == 0:
																								snake_case__    :   List[Any]							     =   t // 2
																								snake_case__    :   Union[str, Any]							     =   (g**t) % n
																								snake_case__    :   Dict							     =   math.gcd(x - 1 ,     lowerCamelCase						)
																								if x > 1 and y > 1:
																														snake_case__    :   Union[str, Any]							     =   y
																														snake_case__    :   str							     =   n // y
																														break  # find the correct factors
																		else:
																								break  # t is not divisible by 2, break and choose another g
						return sorted([p, q]						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()


