
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				,				snake_case_				,				snake_case_=3_6_0_0_0				,				snake_case_=1_3				,				snake_case_=7				,				snake_case_=True				,				snake_case_=3_2				,				snake_case_=4_2				,				snake_case_=5				,				snake_case_=2				,				snake_case_=4				,				snake_case_=2				,				snake_case_=3_2				,				snake_case_=8_0				,				snake_case_=3_3				,				snake_case_=0.0				,				snake_case_=1E-6				,				snake_case_=1_0_0				,				snake_case_=0.0				,				):
         _lowerCAmelCase    :					Any							=   vocab_size
         _lowerCAmelCase    :					List[str]							=   parent
         _lowerCAmelCase    :					List[Any]							=   batch_size
         _lowerCAmelCase    :					Tuple							=   num_contexts
         # For common tests
         _lowerCAmelCase    :					Tuple							=   self.num_contexts
         _lowerCAmelCase    :					Any							=   is_training
         _lowerCAmelCase    :					int							=   hidden_size
         _lowerCAmelCase    :					Tuple							=   num_ext_layers
         _lowerCAmelCase    :					Union[str, Any]							=   ext_size
         _lowerCAmelCase    :					Optional[Any]							=   num_hidden_layers
         _lowerCAmelCase    :					Any							=   num_attention_heads
         _lowerCAmelCase    :					str							=   num_experts
         _lowerCAmelCase    :					Any							=   d_ff
         _lowerCAmelCase    :					List[str]							=   d_ext
         _lowerCAmelCase    :					Any							=   d_spout
         _lowerCAmelCase    :					Optional[Any]							=   dropout_rate
         _lowerCAmelCase    :					Optional[int]							=   layer_norm_epsilon
         _lowerCAmelCase    :					Union[str, Any]							=   expert_capacity
         _lowerCAmelCase    :					Tuple							=   router_jitter_noise




       def 							__UpperCamelCase    (       self					):
         return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Union[str, Any]							=   ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size					)

         _lowerCAmelCase    :					Optional[Any]							=   self.get_config()

         return (config, input_ids)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Dict							=   ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size					)

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

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




       def 							__UpperCamelCase    (       self					):
         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 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				):
         _lowerCAmelCase    :					Union[str, Any]							=   GPTSanJapaneseForConditionalGeneration(config=snake_case_					)
         model.to(snake_case_					)
         model.eval()
         _lowerCAmelCase    :					int							=   model(
             input_ids=snake_case_				,				)
         self.parent.assertIsNotNone(snake_case_					)



@require_torch
class 					a_					(snake_case__							,     snake_case__							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Optional[int]      = (GPTSanJapaneseModel,) if is_torch_available() else ()
       __lowerCAmelCase      :			List[Any]      = (
           {
               'conversational': GPTSanJapaneseForConditionalGeneration,
               'feature-extraction': GPTSanJapaneseForConditionalGeneration,
               'summarization': GPTSanJapaneseForConditionalGeneration,
               'text2text-generation': GPTSanJapaneseForConditionalGeneration,
               'translation': GPTSanJapaneseForConditionalGeneration,
           }
           if is_torch_available()
           else {}
       )
       __lowerCAmelCase      :			List[str]      = False
       __lowerCAmelCase      :			Dict      = False
       __lowerCAmelCase      :			Union[str, Any]      = False
       __lowerCAmelCase      :			int      = False
       __lowerCAmelCase      :			List[str]      = False
       __lowerCAmelCase      :			Optional[Any]      = False
       __lowerCAmelCase      :			List[Any]      = False
       __lowerCAmelCase      :			List[str]      = False
       # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
       __lowerCAmelCase      :			Union[str, Any]      = [0.8, 0.9]




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					):
         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 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   GPTSanJapaneseTester(self					)
         _lowerCAmelCase    :					Any							=   ConfigTester(self				,				config_class=snake_case_				,				d_model=3_7					)




       def 							__UpperCamelCase    (       self					):
         GPTSanJapaneseConfig()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Dict							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_					)




       @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 							__UpperCamelCase    (       self					):
         super().test_model_parallelism()







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



@require_torch
class 					a_					(snake_case__							,     snake_case__							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Any      = (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
       __lowerCAmelCase      :			Any      = False
       __lowerCAmelCase      :			str      = False
       __lowerCAmelCase      :			Dict      = False
       __lowerCAmelCase      :			Dict      = False
       __lowerCAmelCase      :			List[Any]      = False
       __lowerCAmelCase      :			Optional[Any]      = False
       # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
       __lowerCAmelCase      :			Any      = [0.8, 0.9]




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Tuple							=   GPTSanJapaneseTester(self					)
         _lowerCAmelCase    :					str							=   ConfigTester(self				,				config_class=snake_case_				,				d_model=3_7					)




       def 							__UpperCamelCase    (       self					):
         GPTSanJapaneseConfig()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Tuple							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_					)




       @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 							__UpperCamelCase    (       self					):
         super().test_model_parallelism()




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




       @slow
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[str]							=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
         _lowerCAmelCase    :					Optional[Any]							=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
         _lowerCAmelCase    :					Union[str, Any]							=   tokenizer.encode("""武田信玄は"""				,				return_tensors="""pt"""					)
         _lowerCAmelCase    :					Any							=   model(snake_case_					)
         _lowerCAmelCase    :					str							=   outputs.logits.detach().cpu().numpy()
         # Output of original model created with mesh-tensoflow
         _lowerCAmelCase    :					int							=   [
             # fmt: off
             [-12.037_8398_8952_6367, -12.433_0615_9973_1445, -14.333_8403_7017_8223, -12.450_3459_9304_1992, -11.1_6613_7695_3125,
             -11.930_1376_3427_7344, -10.659_7404_4799_8047, -12.909_5745_0866_6992, -13.241_0430_9082_0312, -13.398_5795_9747_3145,
             -11.107_5248_7182_6172, -12.3_6859_4169_6167, -22.97_9431_1523_4375, -10.481_0676_5747_0703, -12.484_0307_2357_1777,
             -12.807_3606_4910_8887, -14.769_7000_5035_4004, -12.233_5796_3562_0117, -13.428_1454_0863_0371, -22.624_1779_3273_9258],
             [-7.511_1498_8327_0264, -8.281_8517_6849_3652, -7.943_1271_5530_3955, -7.55_0213_3369_4458, -6.49_8699_6650_6958,
             -7.586_7962_8372_1924, -6.978_0859_9472_0459, -7.839_1451_8356_3232, -8.21_9641_6854_8584, -8.695_0912_4755_8594,
             -6.706_9106_1019_8975, -6.6585_7982_6354_9805, -19.565_6986_2365_7227, -5.353_8427_3529_0527, -8.350_6860_7330_3223,
             -8.039_3886_5661_6211, -10.856_5692_9016_1133, -7.75_1544_4755_5542, -8.819_0221_7864_9902, -19.51_5327_4536_1328],
             [-9.73_0662_3458_8623, -10.223_9227_2949_2188, -9.932_9814_9108_8867, -11.857_8367_2332_7637, -7.662_6262_6647_9492,
             -11.13_5291_0995_4834, -7.765_0976_1810_3027, -11.472_9232_7880_8594, -9.543_1499_4812_0117, -11.905_6339_2639_1602,
             -9.366_1642_0745_8496, -11.5_7342_8153_9917, -23.699_0032_1960_4492, -9.429_5902_2521_9727, -10.42_8392_4102_7832,
             -10.585_2403_6407_4707, -10.94_7719_5739_7461, -11.095_4160_6903_0762, -10.390_2406_6925_0488, -23.769_3729_4006_3477],
             [-9.728_2657_6232_9102, -9.859_7126_0070_8008, -10.09_7292_9000_8545, -9.678_5221_0998_5352, -6.879_5199_3942_2607,
             -9.68_4875_4882_8125, -4.2803_4257_8887_9395, -10.018_9142_2271_7285, -9.308_4459_3048_0957, -10.63_3945_4650_8789,
             -8.083_6467_7429_1992, -9.06_3014_9841_3086, -21.904_2663_5742_1875, -8.90_1608_4671_0205, -8.841_8760_2996_8262,
             -11.856_7199_7070_3125, -12.079_3981_5521_2402, -11.233_7532_0434_5703, -10.177_3386_0015_8691, -21.87_2566_2231_4453],
             [-9.669_7645_1873_7793, -9.614_1986_8469_2383, -9.814_5103_4545_8984, -9.996_5019_2260_7422, -11.375_6904_6020_5078,
             -10.113_4052_2766_1133, -10.546_8673_7060_5469, -10.04_3690_6814_5752, -10.907_8092_5750_7324, -10.504_2161_9415_2832,
             -11.129_1990_2801_5137, -10.151_1240_0054_9316, -21.96_5867_9962_1582, -9.086_3494_8730_4688, -11.730_3390_5029_2969,
             -10.460_6676_1016_8457, -10.298_0499_2675_7812, -10.784_1482_1624_7559, -10.840_6934_7381_5918, -22.03_1528_4729_0039],
             # fmt: on
         ]
         _lowerCAmelCase    :					Dict							=   np.array(snake_case_					).flatten()
         _lowerCAmelCase    :					Optional[Any]							=   output_logits[0, :, :2_0].flatten()

         def check(snake_case_				,				snake_case_				,				snake_case_=5E-4					):
           return abs(a - b					) < epsilon * max(abs(snake_case_					)				,				abs(snake_case_					)					)

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




       @slow
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					str							=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
         _lowerCAmelCase    :					Any							=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
         model.to(snake_case_					)

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

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

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

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

         _lowerCAmelCase    :					Optional[int]							=   model.generate(
             input_ids=snake_case_				,				attention_mask=inputs["""attention_mask"""].to(snake_case_					)				,				max_new_tokens=3				,				generation_config=snake_case_				,				)

         _lowerCAmelCase    :					List[Any]							=   tokenizer(sentences[0]				,				return_tensors="""pt"""					).input_ids.to(snake_case_					)
         _lowerCAmelCase    :					List[Any]							=   model.generate(
             input_ids=snake_case_				,				max_new_tokens=3				,				generation_config=snake_case_					)

         _lowerCAmelCase    :					List[str]							=   tokenizer(sentences[1]				,				return_tensors="""pt"""					).input_ids.to(snake_case_					)
         _lowerCAmelCase    :					int							=   model.generate(input_ids=snake_case_				,				max_new_tokens=3				,				generation_config=snake_case_					)

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

         _lowerCAmelCase    :					Dict							=   tokenizer.batch_decode(snake_case_				,				skip_special_tokens=snake_case_					)
         _lowerCAmelCase    :					Optional[Any]							=   tokenizer.decode(output_non_padded[0]				,				skip_special_tokens=snake_case_					)
         _lowerCAmelCase    :					List[Any]							=   tokenizer.decode(output_padded[0]				,				skip_special_tokens=snake_case_					)

         _lowerCAmelCase    :					List[Any]							=   [
             '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(snake_case_				,				snake_case_					)
         self.assertListEqual(snake_case_				,				[non_padded_sentence, padded_sentence]					)




       @tooslow
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[Any]							=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
         _lowerCAmelCase    :					Union[str, Any]							=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
         # Output of original model created with mesh-tensoflow
         _lowerCAmelCase    :					int							=   [
             ('武田信玄は', 3_5_6_7_5),
             ('武田信玄は、', 4_5),
             ('武田信玄は、この', 2_9),
             ('武田信玄は、このよう', 3_0_6_4_2),
             ('武田信玄は、このような', 3_5_6_8_0),
             ('武田信玄は、このような「', 8_6_4_0),
             ('武田信玄は、このような「武田', 3_1_6_1_7),
             ('武田信玄は、このような「武田家', 3_0_6_4_6),
             ('武田信玄は、このような「武田家の', 3_1_6_1_7),
             ('武田信玄は、このような「武田家の家', 3_1_3_8_1),
         ]
         for input, output in target:
           _lowerCAmelCase    :					Optional[int]							=   tokenizer.encode(snake_case_				,				return_tensors="""pt"""					)
           _lowerCAmelCase    :					Dict							=   model(snake_case_					)
           _lowerCAmelCase    :					Tuple							=   outputs.logits.detach().cpu().numpy()[0]
           _lowerCAmelCase    :					int							=   np.argmax(output_logits[-1]					)
           self.assertEqual(snake_case_				,				snake_case_					)




       @slow
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[int]							=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
         _lowerCAmelCase    :					Union[str, Any]							=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
         model.to(snake_case_					)

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

         _lowerCAmelCase    :					Optional[int]							=   '武田信玄は、'
         _lowerCAmelCase    :					List[Any]							=   tokenizer(snake_case_				,				return_tensors="""pt"""					).input_ids.to(snake_case_					)
         _lowerCAmelCase    :					Tuple							=   tokenizer([input_text, input_text]				,				return_tensors="""pt"""					).input_ids.to(snake_case_					)

         # spout from uniform and one-hot
         _lowerCAmelCase    :					str							=   [
             # fmt: off
             [0.8788_2208, 0.3842_6396, 0.3322_0248, 0.4389_0406, 0.1656_2252,
             0.0480_3985, 0.21_1572  , 0.2318_8473, 0.3715_3068, 0.783_6377 ,
             0.0216_0172, 0.3876_1719, 0.7529_0772, 0.9019_8857, 0.3436_5777,
             0.6416_8169, 0.4431_8471, 0.1457_5746, 0.9256_2881, 0.4081_2148,
             0.2901_9122, 0.8886_1599, 0.6552_4846, 0.4356_3456, 0.3817_7187,
             0.7083_2965, 0.8152_7892, 0.6883_2812, 0.3883_3192, 0.456_1522 ,
             0.1482_8817, 0.4724_8213, 0.5435_7335, 0.8200_9566, 0.133_8884 ,
             0.0275_5417, 0.1976_4677, 0.242_2084 , 0.0475_7674, 0.6540_9606,
             0.082_4589 , 0.0330_4383, 0.9438_7689, 0.9876_4509, 0.8243_3901,
             0.2764_6741, 0.6490_7493, 0.7600_9406, 0.3008_7915, 0.1790_4689,
             0.4160_1714, 0.6704_6398, 0.1042_2822, 0.0844_7374, 0.0735_4344,
             0.6142_3565, 0.7028_4866, 0.753_2333 , 0.197_2038 , 0.2957_5659,
             0.9058_3886, 0.2926_5307, 0.5000_0175, 0.7040_7655, 0.88_9363  ,
             0.8190_4418, 0.6682_9128, 0.6446_8815, 0.5656_3723, 0.8560_1875,
             0.9492_4672, 0.0016_6762, 0.2522_0643, 0.7454_0219, 0.6799_3247,
             0.154_9675 , 0.3938_5352, 0.9215_3607, 0.6374_5931, 0.2775_9043,
             0.8470_2295, 0.6590_4271, 0.5867_6614, 0.866_6936 , 0.3960_7438,
             0.7995_4983, 0.4222_0697, 0.3965_0381, 0.784_9864 , 0.5615_0201,
             0.1567_8925, 0.1474_6032, 0.3454_2114, 0.4702_6783, 0.1195_6489,
             0.2542_1435, 0.3378_8901, 0.6893_4842, 0.3642_4685, 0.7173_7898,
             0.3898_3449, 0.9439_3779, 0.3957_5588, 0.3661_6553, 0.8710_4665,
             0.6463_0203, 0.2251_6905, 0.8827_0804, 0.1503_1338, 0.7514_4345,
             0.4645_9025, 0.8539_6454, 0.8635_5643, 0.6513_9851, 0.7026_6061,
             0.3024_1389, 0.8105_6497, 0.8886_5969, 0.3877_3807, 0.7063_5849,
             0.9071_8459, 0.4324_5789, 0.2800_0654, 0.4593_5562, 0.0877_3519,
             0.955_2151 , 0.9390_1511, 0.2248_9288], # 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
         ]

         _lowerCAmelCase    :					Optional[int]							=   model.generate(
             input_ids=snake_case_				,				spout=spouts[0]				,				max_new_tokens=2_0				,				generation_config=snake_case_				,				)

         _lowerCAmelCase    :					Any							=   model.generate(
             input_ids=snake_case_				,				spout=spouts[1]				,				max_new_tokens=2_0				,				generation_config=snake_case_				,				)

         _lowerCAmelCase    :					List[str]							=   model.generate(
             input_ids=snake_case_				,				spout=snake_case_				,				max_new_tokens=2_0				,				generation_config=snake_case_				,				)

         _lowerCAmelCase    :					List[str]							=   tokenizer.decode(outputa[0]					)
         _lowerCAmelCase    :					List[Any]							=   tokenizer.decode(outputa[0]					)
         _lowerCAmelCase    :					Optional[int]							=   tokenizer.batch_decode(snake_case_					)

         _lowerCAmelCase    :					Dict							=   [
             '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(snake_case_				,				snake_case_					)
         self.assertListEqual(snake_case_				,				[outa_sentence, outa_sentence]					)







       @slow
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
         _lowerCAmelCase    :					int							=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
         model.to(snake_case_					)

         # set deterministically
         _lowerCAmelCase    :					Union[str, Any]							=   GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
         _lowerCAmelCase    :					Dict							=   1

         _lowerCAmelCase    :					Dict							=   '武田信玄'
         _lowerCAmelCase    :					Tuple							=   '織田信長'
         _lowerCAmelCase    :					Tuple							=   'は、'
         _lowerCAmelCase    :					Optional[Any]							=   'が、'
         _lowerCAmelCase    :					Dict							=   tokenizer(snake_case_				,				prefix_text=snake_case_				,				return_tensors="""pt"""					)
         _lowerCAmelCase    :					Tuple							=   tokenizer(snake_case_				,				prefix_text=snake_case_				,				return_tensors="""pt"""					)
         _lowerCAmelCase    :					Union[str, Any]							=   tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]				,				return_tensors="""pt"""					)

         _lowerCAmelCase    :					Optional[Any]							=   model.generate(
             input_ids=input_tok_a.input_ids.to(snake_case_					)				,				token_type_ids=input_tok_a.token_type_ids.to(snake_case_					)				,				max_new_tokens=2_0				,				generation_config=snake_case_				,				)

         _lowerCAmelCase    :					int							=   model.generate(
             input_ids=input_tok_a.input_ids.to(snake_case_					)				,				token_type_ids=input_tok_a.token_type_ids.to(snake_case_					)				,				max_new_tokens=2_0				,				generation_config=snake_case_				,				)

         _lowerCAmelCase    :					Any							=   model.generate(
             input_ids=input_tok_a.input_ids.to(snake_case_					)				,				token_type_ids=input_tok_a.token_type_ids.to(snake_case_					)				,				attention_mask=input_tok_a.attention_mask.to(snake_case_					)				,				max_new_tokens=2_0				,				generation_config=snake_case_				,				)

         _lowerCAmelCase    :					Optional[Any]							=   tokenizer.decode(outputa[0]					)
         _lowerCAmelCase    :					str							=   tokenizer.decode(outputa[0]					)
         _lowerCAmelCase    :					int							=   tokenizer.batch_decode(snake_case_					)

         _lowerCAmelCase    :					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(snake_case_				,				snake_case_					)
         self.assertListEqual(snake_case_				,				[outa_sentence, outa_sentence]					)




'''simple docstring'''

def 		_UpperCAmelCase			(					_lowerCamelCase  :    int = 1_00						)      ->  int:
  _lowerCAmelCase    :					Dict							=   [[0 for _ in range(_lowerCamelCase						)] for _ in range(m + 1						)]
  for i in range(m + 1						):
    _lowerCAmelCase    :					List[str]							=   1

  for n in range(m + 1						):
    for k in range(1					, _lowerCamelCase						):
      memo[n][k] += memo[n][k - 1]
      if n > k:
        memo[n][k] += memo[n - k - 1][k]

  return memo[m][m - 1] - 1


if __name__ == "__main__":
     print(solution(int(input("""Enter a number: """).strip())))


