





'''simple docstring'''


import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
       import jax.numpy as jnp

       from transformers import FlaxXLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_flax
class    lowerCAmelCase_		(		unittest.TestCase		):




 @slow
 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"		)
     _lowerCAmelCase				=      AutoTokenizer.from_pretrained("xlm-roberta-base"		)
     _lowerCAmelCase				=      "The dog is cute and lives in the garden house"
     _lowerCAmelCase				=      jnp.array([tokenizer.encode(_lowerCAmelCase		)]		)

     _lowerCAmelCase				=      (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
     _lowerCAmelCase				=      jnp.array(
         [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]		)

     _lowerCAmelCase				=      model(_lowerCAmelCase		)["last_hidden_state"]
     self.assertEqual(output.shape			,     _lowerCAmelCase		)
     # compare the actual values for a slice of last dim
     self.assertTrue(jnp.allclose(output[:, :, -1]			,     _lowerCAmelCase			,     atol=1E-3		)		)









'''simple docstring'''


import math


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 100    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(i * i for i in range(1		,       n + 1    )    )
    _lowerCAmelCase				=      int(math.pow(sum(range(1		,       n + 1    )    )		,       2    )    )
    return square_of_sum - sum_of_squares


if __name__ == "__main__":
       print(f'''{solution() = }''')


