

'''simple docstring'''

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 lowerCamelCase   (    unittest.TestCase				):

				def __init__(       self,						lowercase_,						lowercase_=13,						lowercase_=7,						lowercase_=True,						lowercase_=True,						lowercase_=True,						lowercase_=True,						lowercase_=99,						lowercase_=24,						lowercase_=32,						lowercase_=5,						lowercase_=4,						lowercase_=37,						lowercase_="gelu",						lowercase_=0.1,						lowercase_=0.1,						lowercase_=512,						lowercase_=16,						lowercase_=2,						lowercase_=0.02,						lowercase_=4,						)    ->							Union[str, Any]:
					snake_case							=					parent
					snake_case							=					batch_size
					snake_case							=					seq_length
					snake_case							=					is_training
					snake_case							=					use_attention_mask
					snake_case							=					use_token_type_ids
					snake_case							=					use_labels
					snake_case							=					vocab_size
					snake_case							=					hidden_size
					snake_case							=					embedding_size
					snake_case							=					num_hidden_layers
					snake_case							=					num_attention_heads
					snake_case							=					intermediate_size
					snake_case							=					hidden_act
					snake_case							=					hidden_dropout_prob
					snake_case							=					attention_probs_dropout_prob
					snake_case							=					max_position_embeddings
					snake_case							=					type_vocab_size
					snake_case							=					type_sequence_label_size
					snake_case							=					initializer_range
					snake_case							=					num_choices

				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					ids_tensor([self.batch_size, self.seq_length],						self.vocab_size )

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

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

					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 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					self.prepare_config_and_inputs()
					snake_case			,			snake_case			,			snake_case			,			snake_case							=					config_and_inputs
					snake_case							=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
					return config, inputs_dict







@require_flax
class lowerCamelCase   (    __lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = True

				snake_case_							     = (
				    (
				        FlaxElectraModel,
				        FlaxElectraForCausalLM,
				        FlaxElectraForMaskedLM,
				        FlaxElectraForPreTraining,
				        FlaxElectraForTokenClassification,
				        FlaxElectraForQuestionAnswering,
				        FlaxElectraForMultipleChoice,
				        FlaxElectraForSequenceClassification,
				    )
				    if is_flax_available()
				    else ()
				)

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					FlaxElectraModelTester(self )






				@slow
				def 					_lowerCamelCase    (       self )    ->							Any:
					for model_class_name in self.all_model_classes:
						if model_class_name == FlaxElectraForMaskedLM:
							snake_case							=					model_class_name.from_pretrained('google/electra-small-generator' )
						else:
							snake_case							=					model_class_name.from_pretrained('google/electra-small-discriminator' )
						snake_case							=					model(np.ones((1, 1) ) )
						self.assertIsNotNone(lowercase_ )








'''simple docstring'''

from __future__ import annotations







class lowerCamelCase   :

				def __init__(       self,						lowercase_ )    ->							None:
					snake_case							=					value
					snake_case							=					None
					snake_case							=					None







class lowerCamelCase   :
				snake_case_							     = 42

				def __init__(       self )    ->							None:
					snake_case							=					0

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							None:
					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 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = None )    ->							int:
					if node is None:
						return 0
					if target is not None:
						snake_case							=					target

					self.depth_first_search(lowercase_,						node.value )
					self.path_sum(node.left )
					self.path_sum(node.right )

					return self.paths


if __name__ == "__main__":
						import doctest

						doctest.testmod()





