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 						snake_case			(       unittest.TestCase	):
  			'''simple docstring'''
  
  			def __init__(	self     :     Dict							,							lowerCAmelCase_     :     List[Any]							,							lowerCAmelCase_     :     str=13							,							lowerCAmelCase_     :     int=7							,							lowerCAmelCase_     :     Dict=True							,							lowerCAmelCase_     :     Dict=True							,							lowerCAmelCase_     :     Union[str, Any]=True							,							lowerCAmelCase_     :     str=True							,							lowerCAmelCase_     :     List[str]=99							,							lowerCAmelCase_     :     Optional[int]=24							,							lowerCAmelCase_     :     Any=32							,							lowerCAmelCase_     :     List[str]=5							,							lowerCAmelCase_     :     Dict=4							,							lowerCAmelCase_     :     int=37							,							lowerCAmelCase_     :     str="gelu"							,							lowerCAmelCase_     :     Tuple=0.1							,							lowerCAmelCase_     :     Optional[int]=0.1							,							lowerCAmelCase_     :     int=512							,							lowerCAmelCase_     :     Dict=16							,							lowerCAmelCase_     :     int=2							,							lowerCAmelCase_     :     str=0.02							,							lowerCAmelCase_     :     List[Any]=4							,							)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       parent
  								SCREAMING_SNAKE_CASE_	      =       batch_size
  								SCREAMING_SNAKE_CASE_	      =       seq_length
  								SCREAMING_SNAKE_CASE_	      =       is_training
  								SCREAMING_SNAKE_CASE_	      =       use_attention_mask
  								SCREAMING_SNAKE_CASE_	      =       use_token_type_ids
  								SCREAMING_SNAKE_CASE_	      =       use_labels
  								SCREAMING_SNAKE_CASE_	      =       vocab_size
  								SCREAMING_SNAKE_CASE_	      =       hidden_size
  								SCREAMING_SNAKE_CASE_	      =       embedding_size
  								SCREAMING_SNAKE_CASE_	      =       num_hidden_layers
  								SCREAMING_SNAKE_CASE_	      =       num_attention_heads
  								SCREAMING_SNAKE_CASE_	      =       intermediate_size
  								SCREAMING_SNAKE_CASE_	      =       hidden_act
  								SCREAMING_SNAKE_CASE_	      =       hidden_dropout_prob
  								SCREAMING_SNAKE_CASE_	      =       attention_probs_dropout_prob
  								SCREAMING_SNAKE_CASE_	      =       max_position_embeddings
  								SCREAMING_SNAKE_CASE_	      =       type_vocab_size
  								SCREAMING_SNAKE_CASE_	      =       type_sequence_label_size
  								SCREAMING_SNAKE_CASE_	      =       initializer_range
  								SCREAMING_SNAKE_CASE_	      =       num_choices
  
  			def _lowercase (	self     :     str					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size					)
  
  								SCREAMING_SNAKE_CASE_	      =       None
  								if self.use_attention_mask:
  													SCREAMING_SNAKE_CASE_	      =       random_attention_mask([self.batch_size, self.seq_length]					)
  
  								SCREAMING_SNAKE_CASE_	      =       None
  								if self.use_token_type_ids:
  													SCREAMING_SNAKE_CASE_	      =       ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size					)
  
  								SCREAMING_SNAKE_CASE_	      =       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 _lowercase (	self     :     Any					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.prepare_config_and_inputs()
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       config_and_inputs
  								SCREAMING_SNAKE_CASE_	      =       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
  								return config, inputs_dict
  
  @require_flax
  class 						snake_case			(       lowerCAmelCase__	, unittest.TestCase	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       int       = True
  
  			UpperCAmelCase       :       str       = (
  			    (
  			        FlaxElectraModel,
  			        FlaxElectraForCausalLM,
  			        FlaxElectraForMaskedLM,
  			        FlaxElectraForPreTraining,
  			        FlaxElectraForTokenClassification,
  			        FlaxElectraForQuestionAnswering,
  			        FlaxElectraForMultipleChoice,
  			        FlaxElectraForSequenceClassification,
  			    )
  			    if is_flax_available()
  			    else ()
  			)
  
  			def _lowercase (	self     :     Optional[int]					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       FlaxElectraModelTester(self					)
  
  
  
  
  			@slow
  			def _lowercase (	self     :     Dict					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								for model_class_name in self.all_model_classes:
  													if model_class_name == FlaxElectraForMaskedLM:
  																		SCREAMING_SNAKE_CASE_	      =       model_class_name.from_pretrained('''google/electra-small-generator'''					)
  													else:
  																		SCREAMING_SNAKE_CASE_	      =       model_class_name.from_pretrained('''google/electra-small-discriminator'''					)
  													SCREAMING_SNAKE_CASE_	      =       model(np.ones((1, 1)					)					)
  													self.assertIsNotNone(lowerCAmelCase_					)
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			def __init__(	self     :     int							,							lowerCAmelCase_     :     int					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       value
  								SCREAMING_SNAKE_CASE_	      =       None
  								SCREAMING_SNAKE_CASE_	      =       None
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			UpperCAmelCase       :       int
  
  			def __init__(	self     :     Union[str, Any]					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       0
  
  			def _lowercase (	self     :     Optional[int]							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if node is None:
  													return
  
  								if path_sum == self.target:
  													self.paths += 1
  
  								if node.left:
  													self.depth_first_search(node.left							,							path_sum + node.left.value					)
  								if node.right:
  													self.depth_first_search(node.right							,							path_sum + node.right.value					)
  
  
  
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int | None = None					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if node is None:
  													return 0
  								if target is not None:
  													SCREAMING_SNAKE_CASE_	      =       target
  
  								self.depth_first_search(lowerCAmelCase_							,							node.value					)
  								self.path_sum(node.left					)
  								self.path_sum(node.right					)
  
  								return self.paths
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  