




def    snake_case   (				A		,  A     )				->				List[Any]:




	'''simple docstring'''


	if len(str(__UpperCamelCase     )     ) == 1 or len(str(__UpperCamelCase     )     ) == 1:
		return a * b

	lowerCamelCase_				:						List[str]				  =				max(len(str(__UpperCamelCase     )     )		,  len(str(__UpperCamelCase     )     )     )
	lowerCamelCase_				:						Any				  =				ma // 2

	lowerCamelCase_				:						Union[str, Any]				  =				divmod(__UpperCamelCase		,  10**ma     )
	lowerCamelCase_				:						Tuple				  =				divmod(__UpperCamelCase		,  10**ma     )

	lowerCamelCase_				:						Optional[int]				  =				karatsuba(__UpperCamelCase		,  __UpperCamelCase     )
	lowerCamelCase_				:						Tuple				  =				karatsuba((aa + aa)		,  (ba + ba)     )
	lowerCamelCase_				:						Optional[int]				  =				karatsuba(__UpperCamelCase		,  __UpperCamelCase     )

	return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)






def    snake_case   (				)				->				Dict:




	'''simple docstring'''


	print(karatsuba(15463		,  23489     )     )


if __name__ == "__main__":
							main()


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__		)