




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       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      Tuple ,   __snake_case       :      Dict ,   __snake_case       :      Optional[int]=13 ,   __snake_case       :      int=7 ,   __snake_case       :      Optional[int]=True ,   __snake_case       :      int=True ,   __snake_case       :      List[str]=True ,   __snake_case       :      List[str]=True ,   __snake_case       :      Tuple=99 ,   __snake_case       :      Dict=32 ,   __snake_case       :      Union[str, Any]=5 ,   __snake_case       :      int=4 ,   __snake_case       :      List[Any]=37 ,   __snake_case       :      Any="gelu" ,   __snake_case       :      Dict=0.1 ,   __snake_case       :      str=0.1 ,   __snake_case       :      Optional[Any]=5_12 ,   __snake_case       :      List[str]=16 ,   __snake_case       :      Optional[int]=2 ,   __snake_case       :      List[str]=0.02 ,   __snake_case       :      Tuple=4 ,   ):
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					seq_length
         UpperCAmelCase_										=					is_training
         UpperCAmelCase_										=					use_attention_mask
         UpperCAmelCase_										=					use_token_type_ids
         UpperCAmelCase_										=					use_labels
         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					intermediate_size
         UpperCAmelCase_										=					hidden_act
         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					type_vocab_size
         UpperCAmelCase_										=					type_sequence_label_size
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					num_choices
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size    )

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

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

         UpperCAmelCase_										=					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=__snake_case ,   initializer_range=self.initializer_range ,   )

         return config, input_ids, token_type_ids, attention_mask




     def   lowerCamelCase_       (     self       :      List[Any]    ):
         UpperCAmelCase_										=					self.prepare_config_and_inputs()
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					config_and_inputs
         UpperCAmelCase_										=					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict



@require_flax
class       a					(       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  True

     lowerCAmelCase    :	Any       		=  (
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					FlaxRoFormerModelTester(self    )




     @slow
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         for model_class_name in self.all_model_classes:
             UpperCAmelCase_										=					model_class_name.from_pretrained('''junnyu/roformer_chinese_small''' ,   from_pt=__snake_case    )
             UpperCAmelCase_										=					model(np.ones((1, 1)    )    )
             self.assertIsNotNone(__snake_case    )



@require_flax
class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     @slow
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''    )
         UpperCAmelCase_										=					jnp.array([[0, 1, 2, 3, 4, 5]]    )
         UpperCAmelCase_										=					model(__snake_case    )[0]

         UpperCAmelCase_										=					5_00_00

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

         UpperCAmelCase_										=					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] ,   __snake_case ,   atol=1E-4    )    )











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict=2_8123				)  ->							str:
    UpperCAmelCase_										=					[1] * (limit + 1)

    for i in range(2	,					int(limit**0.5				) + 1				):
        sum_divs[i * i] += i
        for k in range(i + 1	,					limit // i + 1				):
            sum_divs[k * i] += k + i

    UpperCAmelCase_										=					set()
    UpperCAmelCase_										=					0

    for n in range(1	,					limit + 1				):
        if sum_divs[n] > n:
            abundants.add(__UpperCamelCase				)

        if not any((n - a in abundants) for a in abundants				):
            res += n

    return res


if __name__ == "__main__":
 print(solution())





