





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):







		def __init__(		self      ,  __lowercase      ,  __lowercase=1_3      ,  __lowercase=7      ,  __lowercase=True      ,  __lowercase=True      ,  __lowercase=True      ,  __lowercase=True      ,  __lowercase=9_9      ,  __lowercase=2_4      ,  __lowercase=3_2      ,  __lowercase=5      ,  __lowercase=4      ,  __lowercase=3_7      ,  __lowercase="gelu"      ,  __lowercase=0.1      ,  __lowercase=0.1      ,  __lowercase=5_1_2      ,  __lowercase=1_6      ,  __lowercase=2      ,  __lowercase=0.0_2      ,  __lowercase=4      ,  )					-> Optional[int]:
				lowerCamelCase :			Optional[int]    		=parent
				lowerCamelCase :			Union[str, Any]    		=batch_size
				lowerCamelCase :			Union[str, Any]    		=seq_length
				lowerCamelCase :			List[Any]    		=is_training
				lowerCamelCase :			int    		=use_attention_mask
				lowerCamelCase :			Tuple    		=use_token_type_ids
				lowerCamelCase :			str    		=use_labels
				lowerCamelCase :			Optional[Any]    		=vocab_size
				lowerCamelCase :			Optional[Any]    		=hidden_size
				lowerCamelCase :			int    		=embedding_size
				lowerCamelCase :			Optional[int]    		=num_hidden_layers
				lowerCamelCase :			List[str]    		=num_attention_heads
				lowerCamelCase :			Any    		=intermediate_size
				lowerCamelCase :			int    		=hidden_act
				lowerCamelCase :			List[str]    		=hidden_dropout_prob
				lowerCamelCase :			Any    		=attention_probs_dropout_prob
				lowerCamelCase :			Optional[int]    		=max_position_embeddings
				lowerCamelCase :			Dict    		=type_vocab_size
				lowerCamelCase :			Optional[Any]    		=type_sequence_label_size
				lowerCamelCase :			Optional[Any]    		=initializer_range
				lowerCamelCase :			Optional[Any]    		=num_choices







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			Optional[Any]    		=ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

				lowerCamelCase :			Tuple    		=None
				if self.use_attention_mask:
						lowerCamelCase :			Union[str, Any]    		=random_attention_mask([self.batch_size, self.seq_length] )

				lowerCamelCase :			Union[str, Any]    		=None
				if self.use_token_type_ids:
						lowerCamelCase :			List[str]    		=ids_tensor([self.batch_size, self.seq_length]      ,  self.type_vocab_size )

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

@require_flax
class 							snake_case_		(      _A    ,       unittest.TestCase):
		lowerCamelCase     :Optional[int]							=  True

		lowerCamelCase     :str							=  (
		    (
		        FlaxElectraModel,
		        FlaxElectraForCausalLM,
		        FlaxElectraForMaskedLM,
		        FlaxElectraForPreTraining,
		        FlaxElectraForTokenClassification,
		        FlaxElectraForQuestionAnswering,
		        FlaxElectraForMultipleChoice,
		        FlaxElectraForSequenceClassification,
		    )
		    if is_flax_available()
		    else ()
		)







		def       __lowercase (		self )					-> Tuple:
				lowerCamelCase :			Optional[Any]    		=FlaxElectraModelTester(self )







		@slow
		def       __lowercase (		self )					-> List[Any]:
				for model_class_name in self.all_model_classes:
						if model_class_name == FlaxElectraForMaskedLM:
								lowerCamelCase :			List[str]    		=model_class_name.from_pretrained('''google/electra-small-generator''' )
						else:
								lowerCamelCase :			Any    		=model_class_name.from_pretrained('''google/electra-small-discriminator''' )
						lowerCamelCase :			str    		=model(np.ones((1, 1) ) )
						self.assertIsNotNone(__lowercase )








def        A__						(							SCREAMING_SNAKE_CASE_ = 1_0_0_0		) ->  int:
		lowerCamelCase :			Dict    		=[]
		lowerCamelCase :			Dict    		=[]
		lowerCamelCase :			Union[str, Any]    		=3
		lowerCamelCase :			List[str]    		=5
		lowerCamelCase :			Union[str, Any]    		=1
		while True:
				lowerCamelCase :			Dict    		=z * temp
				if result < n:
						zmulti.append(SCREAMING_SNAKE_CASE_		)
						temp += 1
				else:
						lowerCamelCase :			List[Any]    		=1
						break
		while True:
				lowerCamelCase :			Optional[Any]    		=x * temp
				if result < n:
						xmulti.append(SCREAMING_SNAKE_CASE_		)
						temp += 1
				else:
						break
		lowerCamelCase :			str    		=list(set(xmulti + zmulti		)		)
		return sum(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				print(F"""{solution() = }""")

