
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 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''  )

           UpperCamelCase__    :   Optional[int]					=				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__    :   Tuple					=				model(lowercase__  )["last_hidden_state"]
           UpperCamelCase__    :   Any					=				tf.TensorShape((1, 1_0, 7_6_8)  )
           self.assertEqual(output.shape						,						lowercase__  )
           # compare the actual values for a slice.
           UpperCamelCase__    :   Dict					=				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  )  )








from __future__ import annotations

from collections.abc import Callable


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = 1_0_0 ,		):
      UpperCamelCase__    :   Union[str, Any]					=				x_start
      UpperCamelCase__    :   List[Any]					=				fnc(UpperCamelCase__		)
      UpperCamelCase__    :   Any					=				0.0
      for _ in range(UpperCamelCase__		):
            # Approximates small segments of curve as linear and solve
            # for trapezoidal area
            UpperCamelCase__    :   str					=				(x_end - x_start) / steps + xa
            UpperCamelCase__    :   Dict					=				fnc(UpperCamelCase__		)
            area += abs(fxa + fxa		) * (xa - xa) / 2
            # Increment step
            UpperCamelCase__    :   Tuple					=				xa
            UpperCamelCase__    :   Union[str, Any]					=				fxa
      return area


if __name__ == "__main__":


    def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
          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


