
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 	lowerCamelCase						(    unittest.TestCase    ):





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

         snake_case						=   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=_A							,       dtype=torch.long							,       )

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

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

         snake_case						=   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=_A							,       dtype=torch.half							,       )

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



import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


_A       =					logging.get_logger(__name__)







class 	lowerCamelCase						(    A_    ):





     def __init__(self :       Any							,       *_A :       Optional[Any]							,       **_A :       List[Any]  )		->		None:
         warnings.warn(
             "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use LevitImageProcessor instead."							,       _A							,       )
         super().__init__(*_A							,       **_A  )

