


def    lowercase_	(	_A    :					int					,      _A    :					list		):

      """simple docstring"""




      _enforce_args(_A					,      _A		)
      if n == 0:
            return 0
      lowerCamelCase__	:       Any					=						float("-inf"		)
      for i in range(1					,      n + 1		):
            lowerCamelCase__	:       List[str]					=						max(
                _A					,      prices[i - 1] + naive_cut_rod_recursive(n - i					,      _A		)		)

      return max_revue

def    lowercase_	(	_A    :					int					,      _A    :					list		):

      """simple docstring"""




      _enforce_args(_A					,      _A		)
      lowerCamelCase__	:       int					=						[float("-inf"		) for _ in range(n + 1		)]
      return _top_down_cut_rod_recursive(_A					,      _A					,      _A		)

def    lowercase_	(	_A    :					int					,      _A    :					list					,      _A    :					list		):

      """simple docstring"""




      if max_rev[n] >= 0:
            return max_rev[n]
      elif n == 0:
            return 0
      else:
            lowerCamelCase__	:       Optional[Any]					=						float("-inf"		)
            for i in range(1					,      n + 1		):
                  lowerCamelCase__	:       int					=						max(
                      _A					,      prices[i - 1] + _top_down_cut_rod_recursive(n - i					,      _A					,      _A		)					,      )

            lowerCamelCase__	:       List[Any]					=						max_revenue

      return max_rev[n]

def    lowercase_	(	_A    :					int					,      _A    :					list		):

      """simple docstring"""




      _enforce_args(_A					,      _A		)

      # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
      # length 0.
      lowerCamelCase__	:       int					=						[float("-inf"		) for _ in range(n + 1		)]
      lowerCamelCase__	:       List[Any]					=						0

      for i in range(1					,      n + 1		):
            lowerCamelCase__	:       Tuple					=						max_rev[i]
            for j in range(1					,      i + 1		):
                  lowerCamelCase__	:       List[str]					=						max(_A					,      prices[j - 1] + max_rev[i - j]		)

            lowerCamelCase__	:       Any					=						max_revenue_i

      return max_rev[n]

def    lowercase_	(	_A    :					int					,      _A    :					list		):

      """simple docstring"""




      if n < 0:
            lowerCamelCase__	:       str					=						F"n must be greater than or equal to 0. Got n = {n}"
            raise ValueError(_A		)

      if n > len(_A		):
            lowerCamelCase__	:       List[Any]					=						(
                "Each integral piece of rod must have a corresponding price. "
                F"Got n = {n} but length of prices = {len(_A		)}"
            )
            raise ValueError(_A		)

def    lowercase_	(	):

      """simple docstring"""




      lowerCamelCase__	:       Optional[Any]					=						[6, 10, 12, 15, 20, 23]
      lowerCamelCase__	:       Tuple					=						len(_A		)

      # the best revenue comes from cutting the rod into 6 pieces, each
      # of length 1 resulting in a revenue of 6 * 6 = 36.
      lowerCamelCase__	:       Any					=						36

      lowerCamelCase__	:       Optional[Any]					=						top_down_cut_rod(_A					,      _A		)
      lowerCamelCase__	:       int					=						bottom_up_cut_rod(_A					,      _A		)
      lowerCamelCase__	:       Union[str, Any]					=						naive_cut_rod_recursive(_A					,      _A		)

      assert expected_max_revenue == max_rev_top_down
      assert max_rev_top_down == max_rev_bottom_up
      assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
    main()






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     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )

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

             lowerCamelCase__	:       Any					=						model(__lowerCamelCase )["last_hidden_state"]
             lowerCamelCase__	:       List[str]					=						tf.TensorShape((1, 10, 768) )
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             # compare the actual values for a slice.
             lowerCamelCase__	:       str					=						tf.convert_to_tensor(
                 [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]      ,   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 ) )


