


'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable


def        a__     (					UpperCamelCase_						:      Callable[[int | float], int | float],   UpperCamelCase_						:      int | float,   UpperCamelCase_						:      int | float,   UpperCamelCase_						:      int = 100,   ):
   UpperCAmelCase__	:str      =      x_start
   UpperCAmelCase__	:int      =      fnc(UpperCamelCase_						)
   UpperCAmelCase__	:Any      =      0.0
   for _ in range(UpperCamelCase_						):
      # Approximates small segments of curve as linear and solve
      # for trapezoidal area
      UpperCAmelCase__	:Dict      =      (x_end - x_start) / steps + xa
      UpperCAmelCase__	:Tuple      =      fnc(UpperCamelCase_						)
      area += abs(fxa + fxa						) * (xa - xa) / 2
      # Increment step
      UpperCAmelCase__	:Dict      =      xa
      UpperCAmelCase__	:Union[str, Any]      =      fxa
   return area


if __name__ == "__main__":


    def        a__     (					UpperCamelCase_						:      Optional[Any]						):
       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												=						10
    while i <= 10_0000:
        print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
        i *= 10







'''simple docstring'''





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 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[str]      =      TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''' )

    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_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]]			,       dtype=tf.intaa			,       )  # J'aime le camembert !"

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase )['''last_hidden_state''']
    UpperCAmelCase__	:Dict      =      tf.TensorShape((1, 1_0, 7_6_8) )
    self.assertEqual(output.shape			,       __lowerCamelCase )
    # compare the actual values for a slice.
    UpperCAmelCase__	:Any      =      tf.convert_to_tensor(
        [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]			,       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 ) )



