



"""simple docstring"""






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					def 						snake_case			(			self				:    List[str]	)->   Dict:
								lowerCamelCase__						:						Union[str, Any]						      =inspect.getfile(accelerate.test_utils	)
								lowerCamelCase__						:						List[str]						      =os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['''scripts''', '''test_sync.py''']	)



					@require_cpu
					def 						snake_case			(			self				:    Optional[int]	)->   int:
								debug_launcher(test_sync.main,    num_processes=1	)



					@require_cpu
					def 						snake_case			(			self				:    Optional[int]	)->   Any:
								debug_launcher(test_sync.main	)



					@require_single_gpu
					def 						snake_case			(			self				:    str	)->   str:
								test_sync.main()




					@require_multi_gpu
					def 						snake_case			(			self				:    Union[str, Any]	)->   Tuple:
								print(F'''Found {torch.cuda.device_count()} devices.'''	)
								lowerCamelCase__						:						Any						      =['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
								with patch_environment(omp_num_threads=1	):
											execute_subprocess_async(lowerCamelCase,    env=os.environ.copy()	)






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 	__snake_case					(      self		):
					A__	:						int								=				'''nvidia/megatron-gpt2-345m/'''
					if "MYDIR" in os.environ:
									A__	:						Union[str, Any]								=				os.path.join(os.environ['''MYDIR''']					,							UpperCamelCase__		)
					A__	:						Union[str, Any]								=				GPTaLMHeadModel.from_pretrained(UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.half()

					A__	:						Dict								=				torch.tensor(
					    [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]					,							device=UpperCamelCase__					,							dtype=torch.long					,							)

					with torch.no_grad():
									A__	:						str								=				model(UpperCamelCase__		).logits

					A__	:						int								=				torch.Size((1, 9, 5_0257)		)
					self.assertEqual(output.shape					,							UpperCamelCase__		)

					A__	:						Dict								=				torch.tensor(
					    [
					        4.9_4_1_4,
					        -0.2_9_2_0,
					        -1.2_1_4_8,
					        -4.0_2_7_3,
					        -0.5_1_6_1,
					        -5.2_1_0_9,
					        -1.2_4_1_2,
					        -1.8_3_0_1,
					        -1.7_7_3_4,
					        -4.7_1_4_8,
					        -0.2_3_1_7,
					        -1.0_8_1_1,
					        -2.1_7_7_7,
					        0.4_1_4_1,
					        -3.7_9_6_9,
					        -4.0_5_8_6,
					        -2.5_3_3_2,
					        -3.3_8_0_9,
					        4.3_8_6_7,
					    ]					,							device=UpperCamelCase__					,							dtype=torch.half					,							)

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