


"""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      _UpperCAmelCase					:






		'''simple docstring'''

		def __init__(self							,			a_							,			a_=3_60_00							,			a_=13							,			a_=7							,			a_=True							,			a_=32							,			a_=42							,			a_=5							,			a_=2							,			a_=4							,			a_=2							,			a_=32							,			a_=80							,			a_=33							,			a_=0.0							,			a_=1E-6							,			a_=1_00							,			a_=0.0							,			):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							vocab_size
			__snake_case      :		Any         =							parent
			__snake_case      :		int         =							batch_size
			__snake_case      :		Tuple         =							num_contexts
			# For common tests
			__snake_case      :		List[str]         =							self.num_contexts
			__snake_case      :		str         =							is_training
			__snake_case      :		Optional[int]         =							hidden_size
			__snake_case      :		List[str]         =							num_ext_layers
			__snake_case      :		Dict         =							ext_size
			__snake_case      :		str         =							num_hidden_layers
			__snake_case      :		int         =							num_attention_heads
			__snake_case      :		List[Any]         =							num_experts
			__snake_case      :		List[str]         =							d_ff
			__snake_case      :		Union[str, Any]         =							d_ext
			__snake_case      :		Optional[Any]         =							d_spout
			__snake_case      :		Tuple         =							dropout_rate
			__snake_case      :		int         =							layer_norm_epsilon
			__snake_case      :		int         =							expert_capacity
			__snake_case      :		Optional[Any]         =							router_jitter_noise



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return GPTSanJapaneseConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Dict         =							ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size   )

			__snake_case      :		Union[str, Any]         =							self.get_config()

			return (config, input_ids)



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

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

			__snake_case      :		str         =							self.get_config()

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



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			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 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_							,			):





			'''simple docstring'''

			__snake_case      :		Dict         =							GPTSanJapaneseForConditionalGeneration(config=a_   )
			model.to(a_   )
			model.eval()
			__snake_case      :		Optional[Any]         =							model(
			    input_ids=a_							,			)
			self.parent.assertIsNotNone(a_   )



@require_torch
class      _UpperCAmelCase					(				__snake_case,							__snake_case,							unittest.TestCase							):






		'''simple docstring'''

		lowerCamelCase__           =(GPTSanJapaneseModel,) if is_torch_available() else ()
		lowerCamelCase__           =(
		    {
		        'conversational': GPTSanJapaneseForConditionalGeneration,
		        'feature-extraction': GPTSanJapaneseForConditionalGeneration,
		        'summarization': GPTSanJapaneseForConditionalGeneration,
		        'text2text-generation': GPTSanJapaneseForConditionalGeneration,
		        'translation': GPTSanJapaneseForConditionalGeneration,
		    }
		    if is_torch_available()
		    else {}
		)
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
		lowerCamelCase__           =[0.8, 0.9]



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_							,			a_							,			a_							,			a_   ):





			'''simple docstring'''

			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 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[Any]         =							GPTSanJapaneseTester(self   )
			__snake_case      :		Dict         =							ConfigTester(self							,			config_class=a_							,			d_model=37   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			GPTSanJapaneseConfig()



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		str         =							self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_   )



		@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 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			super().test_model_parallelism()



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





			'''simple docstring'''

			pass



@require_torch
class      _UpperCAmelCase					(				__snake_case,							__snake_case,							unittest.TestCase							):






		'''simple docstring'''

		lowerCamelCase__           =(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		lowerCamelCase__           =False
		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
		lowerCamelCase__           =[0.8, 0.9]



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							GPTSanJapaneseTester(self   )
			__snake_case      :		Any         =							ConfigTester(self							,			config_class=a_							,			d_model=37   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			GPTSanJapaneseConfig()



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[int]         =							self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_   )



		@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 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			super().test_model_parallelism()



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





			'''simple docstring'''

			pass



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Tuple         =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
			__snake_case      :		int         =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
			__snake_case      :		int         =							tokenizer.encode('''武田信玄は'''							,			return_tensors='''pt'''   )
			__snake_case      :		List[str]         =							model(a_   )
			__snake_case      :		Optional[int]         =							outputs.logits.detach().cpu().numpy()
			# Output of original model created with mesh-tensoflow
			__snake_case      :		Optional[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
			]
			__snake_case      :		Dict         =							np.array(a_   ).flatten()
			__snake_case      :		Optional[Any]         =							output_logits[0, :, :20].flatten()

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

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



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Dict         =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
			__snake_case      :		Optional[Any]         =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
			model.to(a_   )

			# set deterministically
			__snake_case      :		int         =							GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
			__snake_case      :		Dict         =							1

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

			__snake_case      :		Dict         =							'''left'''
			__snake_case      :		Dict         =							tokenizer(a_							,			return_tensors='''pt'''							,			padding=a_   )
			__snake_case      :		Union[str, Any]         =							inputs['''input_ids'''].to(a_   )

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

			__snake_case      :		List[str]         =							model.generate(
			    input_ids=a_							,			attention_mask=inputs['''attention_mask'''].to(a_   )							,			max_new_tokens=3							,			generation_config=a_							,			)

			__snake_case      :		int         =							tokenizer(sentences[0]							,			return_tensors='''pt'''   ).input_ids.to(a_   )
			__snake_case      :		Union[str, Any]         =							model.generate(
			    input_ids=a_							,			max_new_tokens=3							,			generation_config=a_   )

			__snake_case      :		int         =							tokenizer(sentences[1]							,			return_tensors='''pt'''   ).input_ids.to(a_   )
			__snake_case      :		str         =							model.generate(input_ids=a_							,			max_new_tokens=3							,			generation_config=a_   )

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

			__snake_case      :		Optional[Any]         =							tokenizer.batch_decode(a_							,			skip_special_tokens=a_   )
			__snake_case      :		List[str]         =							tokenizer.decode(output_non_padded[0]							,			skip_special_tokens=a_   )
			__snake_case      :		int         =							tokenizer.decode(output_padded[0]							,			skip_special_tokens=a_   )

			__snake_case      :		Optional[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(a_							,			a_   )
			self.assertListEqual(a_							,			[non_padded_sentence, padded_sentence]   )



		@tooslow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Tuple         =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
			__snake_case      :		Union[str, Any]         =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
			# Output of original model created with mesh-tensoflow
			__snake_case      :		Tuple         =							[
			    ('''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      :		str         =							tokenizer.encode(a_							,			return_tensors='''pt'''   )
				__snake_case      :		List[str]         =							model(a_   )
				__snake_case      :		List[Any]         =							outputs.logits.detach().cpu().numpy()[0]
				__snake_case      :		List[Any]         =							np.argmax(output_logits[-1]   )
				self.assertEqual(a_							,			a_   )



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
			__snake_case      :		Optional[Any]         =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
			model.to(a_   )

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

			__snake_case      :		int         =							'''武田信玄は、'''
			__snake_case      :		Tuple         =							tokenizer(a_							,			return_tensors='''pt'''   ).input_ids.to(a_   )
			__snake_case      :		str         =							tokenizer([input_text, input_text]							,			return_tensors='''pt'''   ).input_ids.to(a_   )

			# spout from uniform and one-hot
			__snake_case      :		List[Any]         =							[
			    # 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
			]

			__snake_case      :		Any         =							model.generate(
			    input_ids=a_							,			spout=spouts[0]							,			max_new_tokens=20							,			generation_config=a_							,			)

			__snake_case      :		List[str]         =							model.generate(
			    input_ids=a_							,			spout=spouts[1]							,			max_new_tokens=20							,			generation_config=a_							,			)

			__snake_case      :		Optional[int]         =							model.generate(
			    input_ids=a_							,			spout=a_							,			max_new_tokens=20							,			generation_config=a_							,			)

			__snake_case      :		Any         =							tokenizer.decode(outputa[0]   )
			__snake_case      :		Union[str, Any]         =							tokenizer.decode(outputa[0]   )
			__snake_case      :		Optional[Any]         =							tokenizer.batch_decode(a_   )

			__snake_case      :		str         =							[
			    '''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(a_							,			a_   )
			self.assertListEqual(a_							,			[outa_sentence, outa_sentence]   )



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
			__snake_case      :		List[Any]         =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
			model.to(a_   )

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

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

			__snake_case      :		List[Any]         =							model.generate(
			    input_ids=input_tok_a.input_ids.to(a_   )							,			token_type_ids=input_tok_a.token_type_ids.to(a_   )							,			max_new_tokens=20							,			generation_config=a_							,			)

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

			__snake_case      :		Any         =							model.generate(
			    input_ids=input_tok_a.input_ids.to(a_   )							,			token_type_ids=input_tok_a.token_type_ids.to(a_   )							,			attention_mask=input_tok_a.attention_mask.to(a_   )							,			max_new_tokens=20							,			generation_config=a_							,			)

			__snake_case      :		List[Any]         =							tokenizer.decode(outputa[0]   )
			__snake_case      :		Optional[Any]         =							tokenizer.decode(outputa[0]   )
			__snake_case      :		Union[str, Any]         =							tokenizer.batch_decode(a_   )

			__snake_case      :		Tuple         =							[
			    '''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(a_							,			a_   )
			self.assertListEqual(a_							,			[outa_sentence, outa_sentence]   )












import matplotlib.pyplot as plt
import numpy as np




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  len(A )  # Number of training samples
	UpperCAmelCase_		    =  np.eye(A )  # Initialize weights as identity matrix
	for j in range(A ):
		UpperCAmelCase_		    =  point - x_train[j]
		UpperCAmelCase_		    =  np.exp(diff @ diff.T / (-2.0 * tau**2) )

	return weights




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  weight_matrix(A					,   A					,   A )
	UpperCAmelCase_		    =  np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  np.zeros(len(A ) )  # Initialize array of predictions
	for i, item in enumerate(A ):
		UpperCAmelCase_		    =  item @ local_weight(A					,   A					,   A					,   A )

	return y_pred




def  A_(      A					,   A					,   A ):
	import seaborn as sns

	UpperCAmelCase_		    =  sns.load_dataset(A )
	UpperCAmelCase_		    =  np.array(data[x_name] )
	UpperCAmelCase_		    =  np.array(data[y_name] )

	UpperCAmelCase_		    =  np.ones(len(A ) )

	# pairing elements of one and x_data
	UpperCAmelCase_		    =  np.column_stack((one, x_data) )

	return x_train, x_data, y_data




def  A_(      A					,   A					,   A					,   A					,   A					,   A					,   ):
	UpperCAmelCase_		    =  np.sort(A					,   axis=0 )
	plt.scatter(A					,   A					,   color="""blue""" )
	plt.plot(
	    x_train_sorted[:, 1]					,   preds[x_train[:, 1].argsort(0 )]					,   color="""yellow"""					,   linewidth=5					,   )
	plt.title("""Local Weighted Regression""" )
	plt.xlabel(A )
	plt.ylabel(A )
	plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Demo with a dataset from the seaborn module
				UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__ :			Tuple       	=			load_data("""tips""", """total_bill""", """tip""")
				UpperCamelCase__ :			Tuple       	=			local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")


