


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_					)






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()


