
"""simple docstring"""
from __future__ import annotations

from pathlib import Path


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					return pointa[0] * pointa[1] - pointa[1] * pointa[0]


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			bool:


					'''simple docstring'''
					__a:						tuple[int, int]		    =					(xa, ya)
					__a:						tuple[int, int]		    =					(xa - xa, ya - ya)
					__a:						tuple[int, int]		    =					(xa - xa, ya - ya)
					__a:						float		    =					-vector_product(lowerCamelCase_	,				lowerCamelCase_ ) / vector_product(
					    lowerCamelCase_	,				lowerCamelCase_ )
					__a:						float		    =					+vector_product(lowerCamelCase_	,				lowerCamelCase_ ) / vector_product(
					    lowerCamelCase_	,				lowerCamelCase_ )

					return a > 0 and b > 0 and a + b < 1


def     lowerCamelCase_    (	lowerCamelCase_ = "p102_triangles.txt" )     ->			int:


					'''simple docstring'''
					__a:						str		    =					Path(lowerCamelCase_ ).parent.joinpath(lowerCamelCase_ ).read_text(encoding="""utf-8""" )

					__a:						list[list[int]]		    =					[]
					for line in data.strip().split("""\n""" ):
										triangles.append([int(lowerCamelCase_ ) for number in line.split(""",""" )] )

					__a:						int		    =					0
					__a:						list[int]

					for triangle in triangles:
										ret += contains_origin(*lowerCamelCase_ )

					return ret


if __name__ == "__main__":
			print(F"{solution() = }")


"""simple docstring"""
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 CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    _snake_case  (						unittest.TestCase       ):






							@slow
							def 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												__a:						Any		    =					CamembertModel.from_pretrained("""camembert-base"""    )
												model.to(snake_case__    )

												__a:						str		    =					torch.tensor(
												    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]						, device=snake_case__						, dtype=torch.long						, )  # J'aime le camembert !
												with torch.no_grad():
																	__a:						str		    =					model(snake_case__    )["""last_hidden_state"""]
												__a:						Optional[Any]		    =					torch.Size((1, 1_0, 7_6_8)    )
												self.assertEqual(output.shape						, snake_case__    )
												# compare the actual values for a slice.
												__a:						Union[str, Any]		    =					torch.tensor(
												    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]						, device=snake_case__						, dtype=torch.float						, )
												# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
												# camembert.eval()
												# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

												self.assertTrue(torch.allclose(output[:, :3, :3]						, snake_case__						, atol=1e-4    )    )
