




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    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



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

							a   =      torch.tensor(
							    [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]] ,				device=__lowerCamelCase ,				dtype=torch.long ,				)

							with torch.no_grad():
										a   =      model(__lowerCamelCase							).logits

							a   =      torch.Size((1, 9, 5_02_57)							)
							self.assertEqual(output.shape ,				__lowerCamelCase							)

							a   =      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=__lowerCamelCase ,				dtype=torch.half ,				)

							for i in range(19							):
										a   ,		a   =      8 * i // 17, 27_92 * i  # along the diagonal
										a   ,		a   =      output[0, r, c], expected_diag[i]
										a   =      f"""row={r} col={c} computed={computed} expected={expected}"""
										self.assertAlmostEqual(__lowerCamelCase ,				__lowerCamelCase ,				delta=1e-4 ,				msg=__lowerCamelCase							)












def       __magic_name__    (			A		: float       ):






			'''simple docstring'''







			return -num if num < 0 else num




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_min() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs_val(A       ) < abs_val(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs(A       ) > abs(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max_sort() arg is an empty sequence"       )
			return sorted(A,  key=A       )[-1]




def       __magic_name__    (			):






			'''simple docstring'''







			assert abs_val(0       ) == 0
			assert abs_val(34       ) == 34
			assert abs_val(-100000000000       ) == 100000000000

			a   =      [-3, -1, 2, -11]
			assert abs_max(A       ) == -11
			assert abs_max_sort(A       ) == -11
			assert abs_min(A       ) == -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		test_abs_val()
		print(abs_val(-34))  # --> 34






