




'''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      UpperCamelCase_  (unittest.TestCase  ):







						"""simple docstring"""


						def __init__(  self   :							List[Any]				,			_lowerCamelCase   :							Any				,			_lowerCamelCase   :							str=13				,			_lowerCamelCase   :							str=7				,			_lowerCamelCase   :							Tuple=True				,			_lowerCamelCase   :							List[str]=True				,			_lowerCamelCase   :							Optional[int]=True				,			_lowerCamelCase   :							List[str]=True				,			_lowerCamelCase   :							str=99				,			_lowerCamelCase   :							Optional[int]=24				,			_lowerCamelCase   :							str=32				,			_lowerCamelCase   :							List[str]=5				,			_lowerCamelCase   :							Optional[Any]=4				,			_lowerCamelCase   :							List[str]=37				,			_lowerCamelCase   :							Any="gelu"				,			_lowerCamelCase   :							Union[str, Any]=0.1				,			_lowerCamelCase   :							Dict=0.1				,			_lowerCamelCase   :							Dict=512				,			_lowerCamelCase   :							int=16				,			_lowerCamelCase   :							List[Any]=2				,			_lowerCamelCase   :							int=0.02				,			_lowerCamelCase   :							int=4				,			):





											"""simple docstring"""







											A_			:  Optional[Any]			=				parent
											A_			:  List[str]			=				batch_size
											A_			:  List[Any]			=				seq_length
											A_			:  int			=				is_training
											A_			:  str			=				use_attention_mask
											A_			:  Tuple			=				use_token_type_ids
											A_			:  Optional[Any]			=				use_labels
											A_			:  List[Any]			=				vocab_size
											A_			:  Union[str, Any]			=				hidden_size
											A_			:  Optional[Any]			=				embedding_size
											A_			:  Optional[int]			=				num_hidden_layers
											A_			:  Tuple			=				num_attention_heads
											A_			:  str			=				intermediate_size
											A_			:  List[Any]			=				hidden_act
											A_			:  Optional[Any]			=				hidden_dropout_prob
											A_			:  Union[str, Any]			=				attention_probs_dropout_prob
											A_			:  Union[str, Any]			=				max_position_embeddings
											A_			:  List[str]			=				type_vocab_size
											A_			:  Any			=				type_sequence_label_size
											A_			:  Dict			=				initializer_range
											A_			:  Optional[Any]			=				num_choices







						def    _a     (  self   :							List[str]     ):





											"""simple docstring"""







											A_			:  List[str]			=				ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size     )

											A_			:  Optional[int]			=				None
											if self.use_attention_mask:
																A_			:  Any			=				random_attention_mask([self.batch_size, self.seq_length]     )

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

											A_			:  str			=				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   :							str     ):





											"""simple docstring"""







											A_			:  Dict			=				self.prepare_config_and_inputs()
											A_			,A_			,A_			,A_			:  List[Any]			=				config_and_inputs
											A_			:  List[Any]			=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
											return config, inputs_dict






@require_flax
class      UpperCamelCase_  (a__,   unittest.TestCase  ):







						"""simple docstring"""


						_lowerCAmelCase         =       True

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







						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											A_			:  List[Any]			=				FlaxElectraModelTester(self     )







						@slow
						def    _a     (  self   :							List[Any]     ):





											"""simple docstring"""







											for model_class_name in self.all_model_classes:
																if model_class_name == FlaxElectraForMaskedLM:
																					A_			:  int			=				model_class_name.from_pretrained('''google/electra-small-generator'''     )
																else:
																					A_			:  Dict			=				model_class_name.from_pretrained('''google/electra-small-discriminator'''     )
																A_			:  str			=				model(np.ones((1, 1)     )     )
																self.assertIsNotNone(_lowerCamelCase     )









'''simple docstring'''




from itertools import permutations




def     snake_case__     (			lowerCamelCase__					: int = 5      ) -> int:
					if gon_side < 3 or gon_side > 5:
										raise ValueError('''gon_side must be in the range [3, 5]'''      )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					A_			:  int			=				list(range(gon_side + 1      ,		0      ,		-1      )      )
					A_			:  List[Any]			=				list(range(gon_side + 2      ,		gon_side * 2 + 1      )      )

					for perm in permutations(small_numbers + big_numbers      ):
										A_			:  Optional[int]			=				generate_gon_ring(lowerCamelCase__      ,		list(lowerCamelCase__      )      )
										if is_magic_gon(lowerCamelCase__      ):
															return int(''''''.join(str(lowerCamelCase__      ) for n in numbers      )      )

					A_			:  Optional[Any]			=				f'Magic {gon_side}-gon ring is impossible'
					raise ValueError(lowerCamelCase__      )




def     snake_case__     (			lowerCamelCase__					: int      ,		lowerCamelCase__					: list[int]      ) -> list[int]:
					A_			:  List[Any]			=				[0] * (gon_side * 3)
					A_			:  Optional[Any]			=				perm[0:3]
					perm.append(perm[1]      )

					A_			:  Optional[Any]			=				1 if gon_side < 5 else 2

					for i in range(1      ,		len(lowerCamelCase__      ) // 3 + magic_number      ):
										A_			:  List[str]			=				perm[2 * i + 1]
										A_			:  str			=				result[3 * i - 1]
										A_			:  Union[str, Any]			=				perm[2 * i + 2]

					return result




def     snake_case__     (			lowerCamelCase__					: list[int]      ) -> bool:
					if len(lowerCamelCase__      ) % 3 != 0:
										raise ValueError('''a gon ring should have a length that is a multiple of 3'''      )

					if min(numbers[::3]      ) != numbers[0]:
										return False

					A_			:  Union[str, Any]			=				sum(numbers[:3]      )

					return all(sum(numbers[i : i + 3]      ) == total for i in range(3      ,		len(lowerCamelCase__      )      ,		3      )      )


if __name__ == "__main__":
							print(solution())



