
'''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):






 @slow
 @unittest.skip('Model is not available.'      )
 def 						lowercase_    (      self	:List[Any]      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      'nvidia/megatron-gpt2-345m/'
       if "MYDIR" in os.environ:
             __A													=      os.path.join(os.environ['MYDIR']						,					_A      )
       __A													=      GPTaLMHeadModel.from_pretrained(_A      )
       model.to(_A      )
       model.half()

       __A													=      torch.tensor(
           [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]						,					device=_A						,					dtype=torch.long						,					)

       with torch.no_grad():
             __A													=      model(_A      ).logits

       __A													=      torch.Size((1, 9, 50_257)      )
       self.assertEqual(output.shape						,					_A      )

       __A													=      torch.tensor(
           [
               4.9_414,
               -0.2_920,
               -1.2_148,
               -4.0_273,
               -0.5_161,
               -5.2_109,
               -1.2_412,
               -1.8_301,
               -1.7_734,
               -4.7_148,
               -0.2_317,
               -1.0_811,
               -2.1_777,
               0.4_141,
               -3.7_969,
               -4.0_586,
               -2.5_332,
               -3.3_809,
               4.3_867,
           ]						,					device=_A						,					dtype=torch.half						,					)

       for i in range(19      ):
             __A						,   __A													=      8 * i // 17, 2_792 * i  # along the diagonal
             __A						,   __A													=      output[0, r, c], expected_diag[i]
             __A													=      F'row={r} col={c} computed={computed} expected={expected}'
             self.assertAlmostEqual(_A						,					_A						,					delta=1E-4						,					msg=_A      )













"""simple docstring"""




import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCamelCase_	 ={"""UserAgent""": UserAgent().random}
UpperCamelCase_	 ="""https://www.mywaifulist.moe/random"""





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[Any]      =       requests.get(_lowercase			,		headers=_lowercase  )
						with open(_lowercase			,		'''wb'''  ) as file:
												file.write(image.content  )





def 			a_							(				):
						_UpperCamelCase     :   Union[str, Any]      =       BeautifulSoup(requests.get(_lowercase			,		headers=_lowercase  ).text			,		'''html.parser'''  )
						_UpperCamelCase     :   List[str]      =       soup.find('''meta'''			,		attrs={'''property''': '''og:title'''}  ).attrs['''content''']
						_UpperCamelCase     :   Optional[int]      =       soup.find('''meta'''			,		attrs={'''property''': '''og:image'''}  ).attrs['''content''']
						_UpperCamelCase     :   Any      =       soup.find('''p'''			,		id='''description'''  ).get_text()
						_UpperCamelCase  ,				_UpperCamelCase     :   Optional[int]      =       os.path.splitext(os.path.basename(_lowercase  )  )
						_UpperCamelCase     :   Dict      =       title.strip().replace(''' '''			,		'''_'''  )
						_UpperCamelCase     :   str      =       F"""{image_title}{image_extension}"""
						save_image(_lowercase			,		_lowercase  )
						return (title, description, image_title)


if __name__ == "__main__":
						UpperCamelCase_ ,      UpperCamelCase_ ,      UpperCamelCase_	 =random_anime_character()
						print(F"{title}\n\n{desc}\n\nImage saved : {image_title}")



