


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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Optional[int]				=   CamembertModel.from_pretrained('camembert-base'   )
										model.to(_UpperCAmelCase   )

										__magic_name__					:				int				=   torch.tensor(
										    [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,   device=_UpperCAmelCase  ,   dtype=torch.long  ,   )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__					:				Tuple				=   model(_UpperCAmelCase   )['last_hidden_state']
										__magic_name__					:				Tuple				=   torch.Size((1, 10, 768)   )
										self.assertEqual(output.shape  ,   _UpperCAmelCase   )
										# compare the actual values for a slice.
										__magic_name__					:				Tuple				=   torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]  ,   device=_UpperCAmelCase  ,   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]  ,   _UpperCAmelCase  ,   atol=1E-4   )   )






from __future__ import annotations

from collections.abc import Callable
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Callable[[int | float], int | float]   ,    lowercase__	:int | float   ,    lowercase__	:int | float   ,    lowercase__	:int = 1_0_0   ,    )							->	float:
						__magic_name__					:				str				=   x_start
						__magic_name__					:				str				=   fnc(lowercase__					)
						__magic_name__					:				Union[str, Any]				=   0.0

						for _ in range(lowercase__					):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												__magic_name__					:				Optional[int]				=   (x_end - x_start) / steps + xa
												__magic_name__					:				List[Any]				=   fnc(lowercase__					)
												area += abs(fxa + fxa					) * (xa - xa) / 2

												# Increment step
												__magic_name__					:				Optional[Any]				=   xa
												__magic_name__					:				Union[str, Any]				=   fxa
						return area


if __name__ == "__main__":
			def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Dict					)							->	Optional[int]:
									return x**3

			print('f(x) = x^3')
			print('The area between the curve, x = -10, x = 10 and the x axis is:')
			snake_case__  				=			10
			while i <= 10_0000:
						snake_case__  				=			trapezoidal_area(f, -5, 5, i)
						print(F"""with {i} steps: {area}""")
						i *= 10


