




from functools import reduce

lowerCamelCase__					:  Any      							=				(
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)

def    UpperCAmelCase_				( __UpperCAmelCase		:      str = N				)   ->					int:
      return max(
          # mypy cannot properly interpret reduce
          int(reduce(lambda __UpperCAmelCase     ,     __UpperCAmelCase				: str(int(__UpperCAmelCase				) * int(__UpperCAmelCase				)				)     ,     n[i : i + 13]				)				)
          for i in range(len(__UpperCAmelCase				) - 12				)				)


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





'''simple docstring'''



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roformer.modeling_flax_roformer import (
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerModel,
     )





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=13     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=99     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=5     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=37     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=512     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=4     ,	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	seq_length
            __snake_case            =	is_training
            __snake_case            =	use_attention_mask
            __snake_case            =	use_token_type_ids
            __snake_case            =	use_labels
            __snake_case            =	vocab_size
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	max_position_embeddings
            __snake_case            =	type_vocab_size
            __snake_case            =	type_sequence_label_size
            __snake_case            =	initializer_range
            __snake_case            =	num_choices


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size	)

            __snake_case            =	None
            if self.use_attention_mask:
                 __snake_case            =	random_attention_mask([self.batch_size, self.seq_length]	)

            __snake_case            =	None
            if self.use_token_type_ids:
                 __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size	)

            __snake_case            =	RoFormerConfig(
                vocab_size=self.vocab_size     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	is_decoder=__SCREAMING_SNAKE_CASE     ,	initializer_range=self.initializer_range     ,	)

            return config, input_ids, token_type_ids, attention_mask




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.prepare_config_and_inputs()
            __snake_case       ,     __snake_case       ,     __snake_case       ,     __snake_case            =	config_and_inputs
            __snake_case            =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
            return config, inputs_dict





@require_flax
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Tuple										=      True

       __lowercase	:   Optional[int]										=      (
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerModelTester(self	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case            =	model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''     ,	from_pt=__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(np.ones((1, 1)	)	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)





@require_flax
class      lowerCAmelCase (			unittest.TestCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''	)
            __snake_case            =	jnp.array([[0, 1, 2, 3, 4, 5]]	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)[0]

            __snake_case            =	5_0000

            __snake_case            =	(1, 6, vocab_size)
            self.assertEqual(output.shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	jnp.array(
                [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]	)

            self.assertTrue(jnp.allclose(output[:, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)






