
'''simple docstring'''





import unittest

import numpy as np

from transformers import (
    GPTSanJapaneseConfig,
    GPTSanJapaneseForConditionalGeneration,
    GPTSanJapaneseModel,
    GPTSanJapaneseTokenizer,
    is_torch_available,
)
from transformers.generation import GenerationConfig
from transformers.testing_utils import require_torch, slow, tooslow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin

class     a :




				def __init__(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=3_60_00,     SCREAMING_SNAKE_CASE_						:					Optional[int]=13,     SCREAMING_SNAKE_CASE_						:					Optional[int]=7,     SCREAMING_SNAKE_CASE_						:					List[str]=True,     SCREAMING_SNAKE_CASE_						:					Optional[int]=32,     SCREAMING_SNAKE_CASE_						:					int=42,     SCREAMING_SNAKE_CASE_						:					Optional[int]=5,     SCREAMING_SNAKE_CASE_						:					Optional[int]=2,     SCREAMING_SNAKE_CASE_						:					int=4,     SCREAMING_SNAKE_CASE_						:					List[str]=2,     SCREAMING_SNAKE_CASE_						:					List[str]=32,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=80,     SCREAMING_SNAKE_CASE_						:					str=33,     SCREAMING_SNAKE_CASE_						:					str=0.0,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=1e-6,     SCREAMING_SNAKE_CASE_						:					Tuple=1_00,     SCREAMING_SNAKE_CASE_						:					str=0.0,     ):
										snake_case							:  List[Any]			=     vocab_size
										snake_case							:  Tuple			=     parent
										snake_case							:  List[str]			=     batch_size
										snake_case							:  Tuple			=     num_contexts
										# For common tests
										snake_case							:  int			=     self.num_contexts
										snake_case							:  Optional[int]			=     is_training
										snake_case							:  Tuple			=     hidden_size
										snake_case							:  Optional[int]			=     num_ext_layers
										snake_case							:  str			=     ext_size
										snake_case							:  int			=     num_hidden_layers
										snake_case							:  str			=     num_attention_heads
										snake_case							:  Optional[int]			=     num_experts
										snake_case							:  Tuple			=     d_ff
										snake_case							:  List[str]			=     d_ext
										snake_case							:  int			=     d_spout
										snake_case							:  int			=     dropout_rate
										snake_case							:  Dict			=     layer_norm_epsilon
										snake_case							:  Union[str, Any]			=     expert_capacity
										snake_case							:  Dict			=     router_jitter_noise




				def 						__snake_case				(				self						:					Any   ):
										return GPTSanJapaneseConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''   )




				def 						__snake_case				(				self						:					str   ):
										snake_case							:  List[str]			=     ids_tensor([self.batch_size, self.seq_length],     self.vocab_size   )

										snake_case							:  int			=     self.get_config()

										return (config, input_ids)




				def 						__snake_case				(				self						:					List[str]   ):
										snake_case							:  List[str]			=     ids_tensor([self.batch_size, self.seq_length],     self.vocab_size   )

										snake_case							:  Optional[int]			=     self.get_config()

										return (config, {"input_ids": input_ids})




				def 						__snake_case				(				self						:					int   ):
										return GPTSanJapaneseConfig(
										    vocab_size=self.vocab_size,     num_contexts=self.seq_length,     d_model=self.hidden_size,     d_ff=self.d_ff,     d_ext=self.d_ext,     d_spout=self.d_spout,     num_switch_layers=self.num_hidden_layers - self.num_ext_layers,     num_ext_layers=self.num_ext_layers,     num_heads=self.num_attention_heads,     num_experts=self.num_experts,     expert_capacity=self.expert_capacity,     dropout_rate=self.dropout_rate,     layer_norm_epsilon=self.layer_norm_epsilon,     router_jitter_noise=self.router_jitter_noise,     )




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[str],     ):
										snake_case							:  Optional[int]			=     GPTSanJapaneseForConditionalGeneration(config=lowercase__   )
										model.to(lowercase__   )
										model.eval()
										snake_case							:  List[str]			=     model(
										    input_ids=lowercase__,     )
										self.parent.assertIsNotNone(lowercase__   )




@require_torch
class     a (					__magic_name__					,__magic_name__					,unittest.TestCase     ):
				_snake_case		     =			(GPTSanJapaneseModel,) if is_torch_available() else ()
				_snake_case		     =			(
				    {
				        "conversational": GPTSanJapaneseForConditionalGeneration,
				        "feature-extraction": GPTSanJapaneseForConditionalGeneration,
				        "summarization": GPTSanJapaneseForConditionalGeneration,
				        "text2text-generation": GPTSanJapaneseForConditionalGeneration,
				        "translation": GPTSanJapaneseForConditionalGeneration,
				    }
				    if is_torch_available()
				    else {}
				)
				_snake_case		     =			False
				_snake_case		     =			False
				_snake_case		     =			False
				_snake_case		     =			False
				_snake_case		     =			False
				_snake_case		     =			False
				_snake_case		     =			False
				_snake_case		     =			False
				# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
				_snake_case		     =			[0.8, 0.9]




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Tuple   ):







										if pipeline_test_casse_name == "SummarizationPipelineTests":
																# TODO: fix `_reorder_cache` is not implemented for this model
																return True
										elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
																# TODO: check this.
																return True

										return False




				def 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  str			=     GPTSanJapaneseTester(self   )
										snake_case							:  Dict			=     ConfigTester(self,     config_class=lowercase__,     d_model=37   )




				def 						__snake_case				(				self						:					Any   ):
										GPTSanJapaneseConfig()




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  List[str]			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__   )




				@unittest.skip(
				    reason='''skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'''   )
				def 						__snake_case				(				self						:					int   ):
										super().test_model_parallelism()




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
				def 						__snake_case				(				self						:					List[str]   ):
										pass




@require_torch
class     a (					__magic_name__					,__magic_name__					,unittest.TestCase     ):
				_snake_case		     =			(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
				_snake_case		     =			False
				_snake_case		     =			False
				_snake_case		     =			False
				_snake_case		     =			False
				_snake_case		     =			False
				_snake_case		     =			False
				# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
				_snake_case		     =			[0.8, 0.9]




				def 						__snake_case				(				self						:					Dict   ):
										snake_case							:  Dict			=     GPTSanJapaneseTester(self   )
										snake_case							:  Any			=     ConfigTester(self,     config_class=lowercase__,     d_model=37   )




				def 						__snake_case				(				self						:					List[Any]   ):
										GPTSanJapaneseConfig()




				def 						__snake_case				(				self						:					List[str]   ):
										snake_case							:  Optional[int]			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__   )




				@unittest.skip(
				    reason='''skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'''   )
				def 						__snake_case				(				self						:					Optional[int]   ):
										super().test_model_parallelism()




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
				def 						__snake_case				(				self						:					Any   ):
										pass




				@slow
				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  List[Any]			=     GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										snake_case							:  Optional[Any]			=     GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										snake_case							:  Optional[int]			=     tokenizer.encode('''武田信玄は''',     return_tensors='''pt'''   )
										snake_case							:  Union[str, Any]			=     model(lowercase__   )
										snake_case							:  Union[str, Any]			=     outputs.logits.detach().cpu().numpy()
										# Output of original model created with mesh-tensoflow
										snake_case							:  List[str]			=     [
										    # fmt: off
										    [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
										    -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
										    -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
										    -12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
										    [-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
										    -7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
										    -6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
										    -8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
										    [-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
										    -11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
										    -9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
										    -10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
										    [-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
										    -9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
										    -8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
										    -11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
										    [-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
										    -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
										    -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
										    -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
										    # fmt: on
										]
										snake_case							:  Optional[Any]			=     np.array(lowercase__   ).flatten()
										snake_case							:  str			=     output_logits[0, :, :20].flatten()

										def check(SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5e-4   ):
																return abs(a - b   ) < epsilon * max(abs(lowercase__   ),     abs(lowercase__   )   )

										self.assertTrue(np.all([check(target[i],     predict[i]   ) for i in range(len(lowercase__   )   )]   )   )




				@slow
				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  str			=     GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										snake_case							:  Union[str, Any]			=     GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										model.to(lowercase__   )

										# set deterministically
										snake_case							:  Tuple			=     GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										snake_case							:  Optional[int]			=     1

										# use different length sentences to test batching
										snake_case							:  List[str]			=     [
										    '''甲斐なら武田と言うほど''',
										    '''織田信長は、''',
										]

										snake_case							:  Tuple			=     '''left'''
										snake_case							:  Optional[Any]			=     tokenizer(lowercase__,     return_tensors='''pt''',     padding=lowercase__   )
										snake_case							:  int			=     inputs['''input_ids'''].to(lowercase__   )

										self.assertNotEqual(inputs['''attention_mask'''][0].numpy().tolist(),     inputs['''attention_mask'''][1].numpy().tolist()   )

										snake_case							:  Dict			=     model.generate(
										    input_ids=lowercase__,     attention_mask=inputs['''attention_mask'''].to(lowercase__   ),     max_new_tokens=3,     generation_config=lowercase__,     )

										snake_case							:  List[str]			=     tokenizer(sentences[0],     return_tensors='''pt'''   ).input_ids.to(lowercase__   )
										snake_case							:  Optional[Any]			=     model.generate(
										    input_ids=lowercase__,     max_new_tokens=3,     generation_config=lowercase__   )

										snake_case							:  Dict			=     tokenizer(sentences[1],     return_tensors='''pt'''   ).input_ids.to(lowercase__   )
										snake_case							:  List[str]			=     model.generate(input_ids=lowercase__,     max_new_tokens=3,     generation_config=lowercase__   )

										self.assertNotEqual(inputs_non_padded.shape,     inputs_padded.shape   )

										snake_case							:  Dict			=     tokenizer.batch_decode(lowercase__,     skip_special_tokens=lowercase__   )
										snake_case							:  List[str]			=     tokenizer.decode(output_non_padded[0],     skip_special_tokens=lowercase__   )
										snake_case							:  int			=     tokenizer.decode(output_padded[0],     skip_special_tokens=lowercase__   )

										snake_case							:  str			=     [
										    '''甲斐なら武田と言うほど甲斐の武田''',
										    '''織田信長は、このような''',
										]
										self.assertListEqual(lowercase__,     lowercase__   )
										self.assertListEqual(lowercase__,     [non_padded_sentence, padded_sentence]   )




				@tooslow
				def 						__snake_case				(				self						:					Optional[int]   ):
										snake_case							:  Any			=     GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										snake_case							:  Dict			=     GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										# Output of original model created with mesh-tensoflow
										snake_case							:  Union[str, Any]			=     [
										    ('''武田信玄は''', 3_56_75),
										    ('''武田信玄は、''', 45),
										    ('''武田信玄は、この''', 29),
										    ('''武田信玄は、このよう''', 3_06_42),
										    ('''武田信玄は、このような''', 3_56_80),
										    ('''武田信玄は、このような「''', 86_40),
										    ('''武田信玄は、このような「武田''', 3_16_17),
										    ('''武田信玄は、このような「武田家''', 3_06_46),
										    ('''武田信玄は、このような「武田家の''', 3_16_17),
										    ('''武田信玄は、このような「武田家の家''', 3_13_81),
										]
										for input, output in target:
																snake_case							:  List[str]			=     tokenizer.encode(lowercase__,     return_tensors='''pt'''   )
																snake_case							:  Any			=     model(lowercase__   )
																snake_case							:  Optional[Any]			=     outputs.logits.detach().cpu().numpy()[0]
																snake_case							:  List[str]			=     np.argmax(output_logits[-1]   )
																self.assertEqual(lowercase__,     lowercase__   )




				@slow
				def 						__snake_case				(				self						:					List[str]   ):
										snake_case							:  int			=     GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										snake_case							:  List[str]			=     GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										model.to(lowercase__   )

										# set deterministically
										snake_case							:  List[Any]			=     GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										snake_case							:  Any			=     1

										snake_case							:  Optional[Any]			=     '''武田信玄は、'''
										snake_case							:  List[Any]			=     tokenizer(lowercase__,     return_tensors='''pt'''   ).input_ids.to(lowercase__   )
										snake_case							:  Any			=     tokenizer([input_text, input_text],     return_tensors='''pt'''   ).input_ids.to(lowercase__   )

										# spout from uniform and one-hot
										snake_case							:  int			=     [
										    # fmt: off
										    [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
										    0.04803985, 0.211572  , 0.23188473, 0.37153068, 0.7836377 ,
										    0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
										    0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
										    0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
										    0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
										    0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
										    0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
										    0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
										    0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
										    0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
										    0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
										    0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363  ,
										    0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
										    0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
										    0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
										    0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
										    0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
										    0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
										    0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
										    0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
										    0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
										    0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
										    0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
										    0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
										    0.9552151 , 0.93901511, 0.22489288], # uniform
										    [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0.],
										    # fmt: on
										]

										snake_case							:  Optional[Any]			=     model.generate(
										    input_ids=lowercase__,     spout=spouts[0],     max_new_tokens=20,     generation_config=lowercase__,     )

										snake_case							:  List[Any]			=     model.generate(
										    input_ids=lowercase__,     spout=spouts[1],     max_new_tokens=20,     generation_config=lowercase__,     )

										snake_case							:  int			=     model.generate(
										    input_ids=lowercase__,     spout=lowercase__,     max_new_tokens=20,     generation_config=lowercase__,     )

										snake_case							:  Dict			=     tokenizer.decode(outputa[0]   )
										snake_case							:  int			=     tokenizer.decode(outputa[0]   )
										snake_case							:  List[Any]			=     tokenizer.batch_decode(lowercase__   )

										snake_case							:  Optional[Any]			=     [
										    '''武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である''',
										    '''武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討''',
										]
										self.assertListEqual(lowercase__,     lowercase__   )
										self.assertListEqual(lowercase__,     [outa_sentence, outa_sentence]   )




				@slow
				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  Any			=     GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										snake_case							:  List[str]			=     GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										model.to(lowercase__   )

										# set deterministically
										snake_case							:  int			=     GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
										snake_case							:  Optional[Any]			=     1

										snake_case							:  Dict			=     '''武田信玄'''
										snake_case							:  Optional[int]			=     '''織田信長'''
										snake_case							:  Optional[int]			=     '''は、'''
										snake_case							:  List[str]			=     '''が、'''
										snake_case							:  Dict			=     tokenizer(lowercase__,     prefix_text=lowercase__,     return_tensors='''pt'''   )
										snake_case							:  Tuple			=     tokenizer(lowercase__,     prefix_text=lowercase__,     return_tensors='''pt'''   )
										snake_case							:  Any			=     tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]],     return_tensors='''pt'''   )

										snake_case							:  Union[str, Any]			=     model.generate(
										    input_ids=input_tok_a.input_ids.to(lowercase__   ),     token_type_ids=input_tok_a.token_type_ids.to(lowercase__   ),     max_new_tokens=20,     generation_config=lowercase__,     )

										snake_case							:  Any			=     model.generate(
										    input_ids=input_tok_a.input_ids.to(lowercase__   ),     token_type_ids=input_tok_a.token_type_ids.to(lowercase__   ),     max_new_tokens=20,     generation_config=lowercase__,     )

										snake_case							:  Dict			=     model.generate(
										    input_ids=input_tok_a.input_ids.to(lowercase__   ),     token_type_ids=input_tok_a.token_type_ids.to(lowercase__   ),     attention_mask=input_tok_a.attention_mask.to(lowercase__   ),     max_new_tokens=20,     generation_config=lowercase__,     )

										snake_case							:  Any			=     tokenizer.decode(outputa[0]   )
										snake_case							:  Optional[int]			=     tokenizer.decode(outputa[0]   )
										snake_case							:  int			=     tokenizer.batch_decode(lowercase__   )

										snake_case							:  Dict			=     [
										    '''武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して''',
										    '''織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残''',
										]
										self.assertListEqual(lowercase__,     lowercase__   )
										self.assertListEqual(lowercase__,     [outa_sentence, outa_sentence]   )



"""simple docstring"""




from itertools import permutations

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = 5     ):
					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
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 1,	0,	-1     )     )
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 2,	gon_side * 2 + 1     )     )

					for perm in permutations(small_numbers + big_numbers     ):
										SCREAMING_SNAKE_CASE           =      generate_gon_ring(SCREAMING_SNAKE_CASE_,	list(SCREAMING_SNAKE_CASE_     )     )
										if is_magic_gon(SCREAMING_SNAKE_CASE_     ):
															return int(''.join(str(SCREAMING_SNAKE_CASE_     ) for n in numbers     )     )

					SCREAMING_SNAKE_CASE           =      f'''Magic {gon_side}-gon ring is impossible'''
					raise ValueError(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      [0] * (gon_side * 3)
					SCREAMING_SNAKE_CASE           =      perm[0:3]
					perm.append(perm[1]     )

					SCREAMING_SNAKE_CASE           =      1 if gon_side < 5 else 2

					for i in range(1,	len(SCREAMING_SNAKE_CASE_     ) // 3 + magic_number     ):
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 1]
										SCREAMING_SNAKE_CASE           =      result[3 * i - 1]
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 2]

					return result

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if len(SCREAMING_SNAKE_CASE_     ) % 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

					SCREAMING_SNAKE_CASE           =      sum(numbers[:3]     )

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


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