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      __lowercase  (       unittest.TestCase    ):




				'''simple docstring'''


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

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

								with torch.no_grad():
												UpperCamelCase__       						=							model(snake_case_							).logits

								UpperCamelCase__       						=							torch.Size((1, 9, 50_257)							)
								self.assertEqual(output.shape	,				snake_case_							)

								UpperCamelCase__       						=							torch.tensor(
								    [
								        4.9414,
								        -0.2920,
								        -1.2148,
								        -4.0273,
								        -0.5161,
								        -5.2109,
								        -1.2412,
								        -1.8301,
								        -1.7734,
								        -4.7148,
								        -0.2317,
								        -1.0811,
								        -2.1777,
								        0.4141,
								        -3.7969,
								        -4.0586,
								        -2.5332,
								        -3.3809,
								        4.3867,
								    ]	,				device=snake_case_	,				dtype=torch.half	,				)

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





from math import cos, sin







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_:							int		=  float(lowerCamelCase  )
				return [[scaling_factor * int(x == y  ) for x in range(2  )] for y in range(2  )]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Union[str, Any]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				return [[c, -s], [s, c]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Any		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  c * s
				return [[c * c, cs], [cs, s * s]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							List[str]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							int		=  c * s
				return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"""    {scaling(5) = }""")
print(F"""  {rotation(45) = }""")
print(F"""{projection(45) = }""")





print(F"""{reflection(45) = }""")






