
'''simple docstring'''





import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

UpperCAmelCase            = [(1,)]

def   A			(							):
						snake_case							:  Union[str, Any]			=     bool(os.getenv('''RUN_SLOW'''     ,				0							)							)
						if run_slow:
												assert True
						else:
												assert False, "should have been skipped"

def   A			(							):
						snake_case							:  int			=     bool(os.getenv('''RUN_SLOW'''     ,				0							)							)
						if run_slow and torch_device == "cuda":
												assert True
						else:
												assert False, "should have been skipped"

@require_torch
class     a (					unittest.TestCase     ):




				@slow
				@require_torch_gpu
				def 						__snake_case				(				self						:					Tuple   ):
										check_slow_torch_cuda()




				@require_torch_gpu
				@slow
				def 						__snake_case				(				self						:					List[str]   ):
										check_slow_torch_cuda()




				@parameterized.expand(SCREAMING_SNAKE_CASE_   )
				@slow
				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None   ):
										check_slow()




@slow
@require_torch_gpu
def   A			(							):
						check_slow_torch_cuda()

@require_torch_gpu
@slow
def   A			(							):
						check_slow_torch_cuda()

@slow
@pytest.mark.parametrize('''param'''     ,				[1]							)
def   A			(							A_				:       List[Any]							):
						check_slow()





@pytest.mark.parametrize('''param'''     ,				[1]							)
@slow
def   A			(							A_				:       Tuple							):
						check_slow()









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

     UpperCAmelCase__ 						=				torch.tensor(
         [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]   ,			device=__lowercase   ,			dtype=torch.long   ,			)

     with torch.no_grad():
      UpperCAmelCase__ 						=				model(__lowercase			).logits

     UpperCAmelCase__ 						=				torch.Size((1, 9, 50257)			)
     self.assertEqual(output.shape   ,			__lowercase			)

     UpperCAmelCase__ 						=				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=__lowercase   ,			dtype=torch.half   ,			)

     for i in range(19			):
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				8 * i // 17, 2792 * i  # along the diagonal
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				output[0, r, c], expected_diag[i]
      UpperCAmelCase__ 						=				F'''row={r} col={c} computed={computed} expected={expected}'''
      self.assertAlmostEqual(__lowercase   ,			__lowercase   ,			delta=1e-4   ,			msg=__lowercase			)



