
def        lowerCAmelCase       (				snake_case__   :      float )->			str:
    if num < 0:
        return -1
    return 1 if num else 0





def        lowerCAmelCase       (				)->			int:
    assert signum(5 ) == 1
    assert signum(-5 ) == -1
    assert signum(0 ) == 0


if __name__ == "__main__":
     print(signum(1_2))
     print(signum(-1_2))
     print(signum(0))








'''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       lowerCAmelCase			(					unittest.TestCase      ):




		@slow
		@unittest.skip("Model is not available."   )
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			"nvidia/megatron-gpt2-345m/"
								if "MYDIR" in os.environ:
														_SCREAMING_SNAKE_CASE					=			os.path.join(os.environ["MYDIR"]       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			GPTaLMHeadModel.from_pretrained(UpperCamelCase   )
								model.to(UpperCamelCase   )
								model.half()

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

								with torch.no_grad():
														_SCREAMING_SNAKE_CASE					=			model(UpperCamelCase   ).logits

								_SCREAMING_SNAKE_CASE					=			torch.Size((1, 9, 50_257)   )
								self.assertEqual(output.shape       ,    UpperCamelCase   )

								_SCREAMING_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=UpperCamelCase       ,    dtype=torch.half       ,    )

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