





"""simple docstring"""
import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.roberta.modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
  )






class  _SCREAMING_SNAKE_CASE(       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__=5_12		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=4		,)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  parent
             __SCREAMING_SNAKE_CASE       :Tuple		    =  batch_size
             __SCREAMING_SNAKE_CASE       :Any		    =  seq_length
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  is_training
             __SCREAMING_SNAKE_CASE       :List[str]		    =  use_attention_mask
             __SCREAMING_SNAKE_CASE       :int		    =  use_token_type_ids
             __SCREAMING_SNAKE_CASE       :int		    =  use_labels
             __SCREAMING_SNAKE_CASE       :Tuple		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Any		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  num_hidden_layers
             __SCREAMING_SNAKE_CASE       :int		    =  num_attention_heads
             __SCREAMING_SNAKE_CASE       :Tuple		    =  intermediate_size
             __SCREAMING_SNAKE_CASE       :Any		    =  hidden_act
             __SCREAMING_SNAKE_CASE       :str		    =  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE       :Dict		    =  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE       :Dict		    =  max_position_embeddings
             __SCREAMING_SNAKE_CASE       :str		    =  type_vocab_size
             __SCREAMING_SNAKE_CASE       :List[str]		    =  type_sequence_label_size
             __SCREAMING_SNAKE_CASE       :str		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_choices




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size							)

             __SCREAMING_SNAKE_CASE       :Any		    =  None
             if self.use_attention_mask:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  random_attention_mask([self.batch_size, self.seq_length]							)

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

             __SCREAMING_SNAKE_CASE       :List[str]		    =  RobertaConfig(
                 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    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.prepare_config_and_inputs()
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :List[Any]		    =  config_and_inputs
             __SCREAMING_SNAKE_CASE       :Any		    =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
             return config, inputs_dict




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.prepare_config_and_inputs()
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Optional[int]		    =  config_and_inputs

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  True
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
             __SCREAMING_SNAKE_CASE       :str		    =  ids_tensor([self.batch_size, self.seq_length]		,vocab_size=2							)

             return (
                 config,
                 input_ids,
                 token_type_ids,
                 encoder_hidden_states,
                 encoder_attention_mask,
             )






@require_flax
class  _SCREAMING_SNAKE_CASE(       A   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				Tuple     						=  True

       SCREAMING_SNAKE_CASE_       :				Any     						=  (
           (
               FlaxRobertaModel,
               FlaxRobertaForCausalLM,
               FlaxRobertaForMaskedLM,
               FlaxRobertaForSequenceClassification,
               FlaxRobertaForTokenClassification,
               FlaxRobertaForMultipleChoice,
               FlaxRobertaForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  FlaxRobertaModelTester(self							)




       @slow
       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             for model_class_name in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  model_class_name.from_pretrained('''roberta-base'''		,from_pt=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :int		    =  model(np.ones((1, 1)							)							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		int = 10			,       a_					:		int = 22							)      ->				int:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  range(1			,       a_							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  range(1			,       a_							)
      return sum(
          1 for power in powers for base in bases if len(str(base**power							)							) == power							)


if __name__ == "__main__":
  print(f'{solution(1_0, 2_2) = }')