

from __future__ import annotations

from collections.abc import Callable



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Callable[[int | float], int | float]	,       SCREAMING_SNAKE_CASE_:							int | float	,       SCREAMING_SNAKE_CASE_:							int | float	,       SCREAMING_SNAKE_CASE_:							int = 1_0_0	,       )			->      float:




				'''simple docstring'''





				A__        =			x_start
				A__        =			fnc(SCREAMING_SNAKE_CASE_      )
				A__        =			0.0
				for _ in range(SCREAMING_SNAKE_CASE_      ):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								A__        =			(x_end - x_start) / steps + xa
								A__        =			fnc(SCREAMING_SNAKE_CASE_      )
								area += abs(fxa + fxa      ) * (xa - xa) / 2
								# Increment step
								A__        =			xa
								A__        =			fxa
				return area


if __name__ == "__main__":



	def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict      )			->      Any:




					'''simple docstring'''





					return x**3 + x**2

	print("""f(x) = x^3 + x^2""")
	print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
	lowerCAmelCase__	   = 1_0
	while i <= 1_0_0_0_0_0:
		print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
		i *= 1_0




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			TFCamembertModel.from_pretrained("jplu/tf-camembert-base"       )

							A__        =			tf.convert_to_tensor(
							    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,      dtype=tf.intaa      ,      )  # J'aime le camembert !"

							A__        =			model(lowercase       )["last_hidden_state"]
							A__        =			tf.TensorShape((1, 10, 768)       )
							self.assertEqual(output.shape      ,      lowercase       )
							# compare the actual values for a slice.
							A__        =			tf.convert_to_tensor(
							    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]      ,      dtype=tf.floataa      ,      )
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,      expected_slice.numpy()      ,      atol=1e-4       )       )

