code: import unittest
  
  import numpy as np
  
  from transformers import ElectraConfig, 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():
   from transformers.models.electra.modeling_flax_electra import (
       FlaxElectraForCausalLM,
       FlaxElectraForMaskedLM,
       FlaxElectraForMultipleChoice,
       FlaxElectraForPreTraining,
       FlaxElectraForQuestionAnswering,
       FlaxElectraForSequenceClassification,
       FlaxElectraForTokenClassification,
       FlaxElectraModel,
   )
  
  class 				_lowercase						(    unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=13					,	lowerCamelCase__=7					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=99					,	lowerCamelCase__=24					,	lowerCamelCase__=32					,	lowerCamelCase__=5					,	lowerCamelCase__=4					,	lowerCamelCase__=37					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.1					,	lowerCamelCase__=0.1					,	lowerCamelCase__=512					,	lowerCamelCase__=16					,	lowerCamelCase__=2					,	lowerCamelCase__=0.0_2					,	lowerCamelCase__=4					,	):
         lowerCAmelCase_:       List[str]         =  parent
         lowerCAmelCase_:       Union[str, Any]         =  batch_size
         lowerCAmelCase_:       str         =  seq_length
         lowerCAmelCase_:       Any         =  is_training
         lowerCAmelCase_:       str         =  use_attention_mask
         lowerCAmelCase_:       List[Any]         =  use_token_type_ids
         lowerCAmelCase_:       List[Any]         =  use_labels
         lowerCAmelCase_:       Any         =  vocab_size
         lowerCAmelCase_:       Any         =  hidden_size
         lowerCAmelCase_:       Union[str, Any]         =  embedding_size
         lowerCAmelCase_:       Dict         =  num_hidden_layers
         lowerCAmelCase_:       List[Any]         =  num_attention_heads
         lowerCAmelCase_:       Optional[int]         =  intermediate_size
         lowerCAmelCase_:       Union[str, Any]         =  hidden_act
         lowerCAmelCase_:       Dict         =  hidden_dropout_prob
         lowerCAmelCase_:       Dict         =  attention_probs_dropout_prob
         lowerCAmelCase_:       Dict         =  max_position_embeddings
         lowerCAmelCase_:       Optional[Any]         =  type_vocab_size
         lowerCAmelCase_:       Optional[int]         =  type_sequence_label_size
         lowerCAmelCase_:       str         =  initializer_range
         lowerCAmelCase_:       Dict         =  num_choices
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       List[str]         =  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size   )
  
         lowerCAmelCase_:       List[Any]         =  None
         if self.use_attention_mask:
            lowerCAmelCase_:       Dict         =  random_attention_mask([self.batch_size, self.seq_length]   )
  
         lowerCAmelCase_:       Tuple         =  None
         if self.use_token_type_ids:
            lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	self.type_vocab_size   )
  
         lowerCAmelCase_:       Any         =  ElectraConfig(
             vocab_size=self.vocab_size					,	hidden_size=self.hidden_size					,	embedding_size=self.embedding_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					,	initializer_range=self.initializer_range					,	)
  
         return config, input_ids, token_type_ids, attention_mask
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       List[Any]         =  self.prepare_config_and_inputs()
         lowerCAmelCase_:       Any         =  config_and_inputs
         lowerCAmelCase_:       Optional[int]         =  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
         return config, inputs_dict
  
  
  
  
  
  @require_flax
  class 				_lowercase						(    UpperCAmelCase__   ,     unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Optional[int]        =						True
  
      SCREAMING_SNAKE_CASE:							Any        =						(
          (
              FlaxElectraModel,
              FlaxElectraForCausalLM,
              FlaxElectraForMaskedLM,
              FlaxElectraForPreTraining,
              FlaxElectraForTokenClassification,
              FlaxElectraForQuestionAnswering,
              FlaxElectraForMultipleChoice,
              FlaxElectraForSequenceClassification,
          )
          if is_flax_available()
          else ()
      )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       List[Any]         =  FlaxElectraModelTester(self   )
  
  
  
  
      @slow
      def     _a						(						self   ):
         for model_class_name in self.all_model_classes:
            if model_class_name == FlaxElectraForMaskedLM:
               lowerCAmelCase_:       Tuple         =  model_class_name.from_pretrained("google/electra-small-generator"   )
            else:
               lowerCAmelCase_:       Optional[int]         =  model_class_name.from_pretrained("google/electra-small-discriminator"   )
            lowerCAmelCase_:       List[Any]         =  model(np.ones((1, 1)   )   )
            self.assertIsNotNone(lowerCamelCase__   )
  
  
  
  
style_context: import itertools
  def 		snake_case__  (		lowercase				):
     return (
         int("".join(combination[0:2]				)				) * int("".join(combination[2:5]				)				)
         == int("".join(combination[5:9]				)				)
     ) or (
         int("".join(combination[0]				)				) * int("".join(combination[1:5]				)				)
         == int("".join(combination[5:9]				)				)
     )
  def 		snake_case__  (		):
     return sum(
         {
             int("".join(pandigital[5:9]				)				)
             for pandigital in itertools.permutations("123456789"				)
             if is_combination_valid(lowercase				)
         }				)
  
  
  if __name__ == "__main__":
   print(solution())