


import functools

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

      """simple docstring"""




      if not isinstance(_A					,      _A		) or not all(isinstance(_A					,      _A		) for day in days		):
            raise ValueError("The parameter days should be a list of integers"		)

      if len(_A		) != 3 or not all(isinstance(_A					,      _A		) for cost in costs		):
            raise ValueError("The parameter costs should be a list of three integers"		)

      if len(_A		) == 0:
            return 0

      if min(_A		) <= 0:
            raise ValueError("All days elements should be greater than 0"		)

      if max(_A		) >= 366:
            raise ValueError("All days elements should be less than 366"		)

      lowerCamelCase__	:       str					=						set(_A		)

      @functools.cache
      def dynamic_programming(_A    :					int		) -> int:
            if index > 365:
                  return 0

            if index not in days_set:
                  return dynamic_programming(index + 1		)

            return min(
                costs[0] + dynamic_programming(index + 1		)					,      costs[1] + dynamic_programming(index + 7		)					,      costs[2] + dynamic_programming(index + 30		)					,      )

      return dynamic_programming(1		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






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 ) )


