






"""simple docstring"""
from __future__ import annotations

import math
import random






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			str     				=						d * e - 1
 lowerCamelCase__     :			List[str]     				=						0
 lowerCamelCase__     :			Any     				=						0
 while p == 0:
  lowerCamelCase__     :			List[Any]     				=						random.randint(2			,   n - 1				)
  lowerCamelCase__     :			Union[str, Any]     				=						k
  while True:
   if t % 2 == 0:
    lowerCamelCase__     :			Optional[Any]     				=						t // 2
    lowerCamelCase__     :			str     				=						(g**t) % n
    lowerCamelCase__     :			str     				=						math.gcd(x - 1			,   _lowerCamelCase				)
    if x > 1 and y > 1:
     lowerCamelCase__     :			Tuple     				=						y
     lowerCamelCase__     :			List[Any]     				=						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()





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 CamembertModel


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




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.tensor(
           [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,      device=a						,      dtype=torch.float						,      )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(torch.allclose(output[:, :3, :3]						,      a						,      atol=1e-4				)				)

