

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowerCAmelCase     :			Optional[int]    ={"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase     :			Dict    =[
					    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RegNetForImageClassification""",
					    """RegNetModel""",
					    """RegNetPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase     :			Optional[Any]    =[
					    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRegNetForImageClassification""",
					    """TFRegNetModel""",
					    """TFRegNetPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase     :			Union[str, Any]    =[
					    """FlaxRegNetForImageClassification""",
					    """FlaxRegNetModel""",
					    """FlaxRegNetPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_regnet import (
										    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RegNetForImageClassification,
										    RegNetModel,
										    RegNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_regnet import (
										    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRegNetForImageClassification,
										    TFRegNetModel,
										    TFRegNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_regnet import (
										    FlaxRegNetForImageClassification,
										    FlaxRegNetModel,
										    FlaxRegNetPreTrainedModel,
										)


else:
					import sys

					_lowerCAmelCase     :			Optional[int]    =_LazyModule(__name__, globals()["""__file__"""], _import_structure)


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





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





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  int		       =   ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size	)

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

						return (config, input_ids)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Tuple		       =   ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size	)

						UpperCAmelCase__:  Tuple		       =   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							,			lowerCamelCase__							,			lowerCamelCase__							,			):
						UpperCAmelCase__:  Any		       =   GPTSanJapaneseForConditionalGeneration(config=lowerCamelCase__	)
						model.to(lowerCamelCase__	)
						model.eval()
						UpperCAmelCase__:  Optional[int]		       =   model(
						    input_ids=lowerCamelCase__							,			)
						self.parent.assertIsNotNone(lowerCamelCase__	)

@require_torch
class __UpperCamelCase    (							_a				,_a				,unittest.TestCase		):






					'''simple docstring'''



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





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
						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	):
						UpperCAmelCase__:  List[Any]		       =   GPTSanJapaneseTester(self	)
						UpperCAmelCase__:  int		       =   ConfigTester(self							,			config_class=lowerCamelCase__							,			d_model=3_7	)





					def       _UpperCAmelCase						(							self	):
						GPTSanJapaneseConfig()





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  int		       =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase__	)





					@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 __UpperCamelCase    (							_a				,_a				,unittest.TestCase		):






					'''simple docstring'''



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





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  str		       =   GPTSanJapaneseTester(self	)
						UpperCAmelCase__:  List[str]		       =   ConfigTester(self							,			config_class=lowerCamelCase__							,			d_model=3_7	)





					def       _UpperCAmelCase						(							self	):
						GPTSanJapaneseConfig()





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Any		       =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase__	)





					@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	):
						UpperCAmelCase__:  str		       =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"	)
						UpperCAmelCase__:  Union[str, Any]		       =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"	)
						UpperCAmelCase__:  List[str]		       =   tokenizer.encode("武田信玄は"							,			return_tensors="pt"	)
						UpperCAmelCase__:  List[str]		       =   model(lowerCamelCase__	)
						UpperCAmelCase__:  Any		       =   outputs.logits.detach().cpu().numpy()
						# Output of original model created with mesh-tensoflow
						UpperCAmelCase__:  int		       =   [
						    # fmt: off
						    [-12.037_839_889_526_367, -12.433_061_599_731_445, -14.333_840_370_178_223, -12.450_345_993_041_992, -11.1_661_376_953_125,
						    -11.930_137_634_277_344, -10.659_740_447_998_047, -12.909_574_508_666_992, -13.241_043_090_820_312, -13.398_579_597_473_145,
						    -11.107_524_871_826_172, -12.3_685_941_696_167, -22.97_943_115_234_375, -10.481_067_657_470_703, -12.484_030_723_571_777,
						    -12.807_360_649_108_887, -14.769_700_050_354_004, -12.233_579_635_620_117, -13.428_145_408_630_371, -22.624_177_932_739_258],
						    [-7.511_149_883_270_264, -8.281_851_768_493_652, -7.943_127_155_303_955, -7.55_021_333_694_458, -6.49_869_966_506_958,
						    -7.586_796_283_721_924, -6.978_085_994_720_459, -7.839_145_183_563_232, -8.21_964_168_548_584, -8.695_091_247_558_594,
						    -6.706_910_610_198_975, -6.6_585_798_263_549_805, -19.565_698_623_657_227, -5.353_842_735_290_527, -8.350_686_073_303_223,
						    -8.039_388_656_616_211, -10.856_569_290_161_133, -7.75_154_447_555_542, -8.819_022_178_649_902, -19.51_532_745_361_328],
						    [-9.73_066_234_588_623, -10.223_922_729_492_188, -9.932_981_491_088_867, -11.857_836_723_327_637, -7.662_626_266_479_492,
						    -11.13_529_109_954_834, -7.765_097_618_103_027, -11.472_923_278_808_594, -9.543_149_948_120_117, -11.905_633_926_391_602,
						    -9.366_164_207_458_496, -11.5_734_281_539_917, -23.699_003_219_604_492, -9.429_590_225_219_727, -10.42_839_241_027_832,
						    -10.585_240_364_074_707, -10.94_771_957_397_461, -11.095_416_069_030_762, -10.390_240_669_250_488, -23.769_372_940_063_477],
						    [-9.728_265_762_329_102, -9.859_712_600_708_008, -10.09_729_290_008_545, -9.678_522_109_985_352, -6.879_519_939_422_607,
						    -9.68_487_548_828_125, -4.2_803_425_788_879_395, -10.018_914_222_717_285, -9.308_445_930_480_957, -10.63_394_546_508_789,
						    -8.083_646_774_291_992, -9.06_301_498_413_086, -21.904_266_357_421_875, -8.90_160_846_710_205, -8.841_876_029_968_262,
						    -11.856_719_970_703_125, -12.079_398_155_212_402, -11.233_753_204_345_703, -10.177_338_600_158_691, -21.87_256_622_314_453],
						    [-9.669_764_518_737_793, -9.614_198_684_692_383, -9.814_510_345_458_984, -9.996_501_922_607_422, -11.375_690_460_205_078,
						    -10.113_405_227_661_133, -10.546_867_370_605_469, -10.04_369_068_145_752, -10.907_809_257_507_324, -10.504_216_194_152_832,
						    -11.129_199_028_015_137, -10.151_124_000_549_316, -21.96_586_799_621_582, -9.086_349_487_304_688, -11.730_339_050_292_969,
						    -10.460_667_610_168_457, -10.298_049_926_757_812, -10.784_148_216_247_559, -10.840_693_473_815_918, -22.03_152_847_290_039],
						    # fmt: on
						]
						UpperCAmelCase__:  Union[str, Any]		       =   np.array(lowerCamelCase__	).flatten()
						UpperCAmelCase__:  int		       =   output_logits[0, :, :2_0].flatten()

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

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





					@slow
					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[int]		       =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"	)
						UpperCAmelCase__:  Tuple		       =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"	)
						model.to(lowerCamelCase__	)

						# set deterministically
						UpperCAmelCase__:  Optional[int]		       =   GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"	)
						UpperCAmelCase__:  Dict		       =   1

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

						UpperCAmelCase__:  Tuple		       =   "left"
						UpperCAmelCase__:  Optional[Any]		       =   tokenizer(lowerCamelCase__							,			return_tensors="pt"							,			padding=lowerCamelCase__	)
						UpperCAmelCase__:  List[str]		       =   inputs["input_ids"].to(lowerCamelCase__	)

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

						UpperCAmelCase__:  Any		       =   model.generate(
						    input_ids=lowerCamelCase__							,			attention_mask=inputs["attention_mask"].to(lowerCamelCase__	)							,			max_new_tokens=3							,			generation_config=lowerCamelCase__							,			)

						UpperCAmelCase__:  Dict		       =   tokenizer(sentences[0]							,			return_tensors="pt"	).input_ids.to(lowerCamelCase__	)
						UpperCAmelCase__:  Optional[Any]		       =   model.generate(
						    input_ids=lowerCamelCase__							,			max_new_tokens=3							,			generation_config=lowerCamelCase__	)

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

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

						UpperCAmelCase__:  Tuple		       =   tokenizer.batch_decode(lowerCamelCase__							,			skip_special_tokens=lowerCamelCase__	)
						UpperCAmelCase__:  List[Any]		       =   tokenizer.decode(output_non_padded[0]							,			skip_special_tokens=lowerCamelCase__	)
						UpperCAmelCase__:  Union[str, Any]		       =   tokenizer.decode(output_padded[0]							,			skip_special_tokens=lowerCamelCase__	)

						UpperCAmelCase__:  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(lowerCamelCase__							,			lowerCamelCase__	)
						self.assertListEqual(lowerCamelCase__							,			[non_padded_sentence, padded_sentence]	)





					@tooslow
					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Dict		       =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"	)
						UpperCAmelCase__:  str		       =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"	)
						# Output of original model created with mesh-tensoflow
						UpperCAmelCase__:  List[Any]		       =   [
						    ("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:
							UpperCAmelCase__:  int		       =   tokenizer.encode(lowerCamelCase__							,			return_tensors="pt"	)
							UpperCAmelCase__:  List[Any]		       =   model(lowerCamelCase__	)
							UpperCAmelCase__:  Optional[Any]		       =   outputs.logits.detach().cpu().numpy()[0]
							UpperCAmelCase__:  Tuple		       =   np.argmax(output_logits[-1]	)
							self.assertEqual(lowerCamelCase__							,			lowerCamelCase__	)





					@slow
					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  str		       =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"	)
						UpperCAmelCase__:  Union[str, Any]		       =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"	)
						model.to(lowerCamelCase__	)

						# set deterministically
						UpperCAmelCase__:  Tuple		       =   GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"	)
						UpperCAmelCase__:  Any		       =   1

						UpperCAmelCase__:  Optional[int]		       =   "武田信玄は、"
						UpperCAmelCase__:  Optional[Any]		       =   tokenizer(lowerCamelCase__							,			return_tensors="pt"	).input_ids.to(lowerCamelCase__	)
						UpperCAmelCase__:  Dict		       =   tokenizer([input_text, input_text]							,			return_tensors="pt"	).input_ids.to(lowerCamelCase__	)

						# spout from uniform and one-hot
						UpperCAmelCase__:  List[str]		       =   [
						    # fmt: off
						    [0.87_882_208, 0.38_426_396, 0.33_220_248, 0.43_890_406, 0.16_562_252,
						    0.04_803_985, 0.211_572  , 0.23_188_473, 0.37_153_068, 0.7_836_377 ,
						    0.02_160_172, 0.38_761_719, 0.75_290_772, 0.90_198_857, 0.34_365_777,
						    0.64_168_169, 0.44_318_471, 0.14_575_746, 0.92_562_881, 0.40_812_148,
						    0.29_019_122, 0.88_861_599, 0.65_524_846, 0.43_563_456, 0.38_177_187,
						    0.70_832_965, 0.81_527_892, 0.68_832_812, 0.38_833_192, 0.4_561_522 ,
						    0.14_828_817, 0.47_248_213, 0.54_357_335, 0.82_009_566, 0.1_338_884 ,
						    0.02_755_417, 0.19_764_677, 0.2_422_084 , 0.04_757_674, 0.65_409_606,
						    0.0_824_589 , 0.03_304_383, 0.94_387_689, 0.98_764_509, 0.82_433_901,
						    0.27_646_741, 0.64_907_493, 0.76_009_406, 0.30_087_915, 0.17_904_689,
						    0.41_601_714, 0.67_046_398, 0.10_422_822, 0.08_447_374, 0.07_354_344,
						    0.61_423_565, 0.70_284_866, 0.7_532_333 , 0.1_972_038 , 0.29_575_659,
						    0.90_583_886, 0.29_265_307, 0.50_000_175, 0.70_407_655, 0.889_363  ,
						    0.81_904_418, 0.66_829_128, 0.64_468_815, 0.56_563_723, 0.85_601_875,
						    0.94_924_672, 0.00_166_762, 0.25_220_643, 0.74_540_219, 0.67_993_247,
						    0.1_549_675 , 0.39_385_352, 0.92_153_607, 0.63_745_931, 0.27_759_043,
						    0.84_702_295, 0.65_904_271, 0.58_676_614, 0.8_666_936 , 0.39_607_438,
						    0.79_954_983, 0.42_220_697, 0.39_650_381, 0.7_849_864 , 0.56_150_201,
						    0.15_678_925, 0.14_746_032, 0.34_542_114, 0.47_026_783, 0.11_956_489,
						    0.25_421_435, 0.33_788_901, 0.68_934_842, 0.36_424_685, 0.71_737_898,
						    0.38_983_449, 0.94_393_779, 0.39_575_588, 0.36_616_553, 0.87_104_665,
						    0.64_630_203, 0.22_516_905, 0.88_270_804, 0.15_031_338, 0.75_144_345,
						    0.46_459_025, 0.85_396_454, 0.86_355_643, 0.65_139_851, 0.70_266_061,
						    0.30_241_389, 0.81_056_497, 0.88_865_969, 0.38_773_807, 0.70_635_849,
						    0.90_718_459, 0.43_245_789, 0.28_000_654, 0.45_935_562, 0.08_773_519,
						    0.9_552_151 , 0.93_901_511, 0.22_489_288], # 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
						]

						UpperCAmelCase__:  str		       =   model.generate(
						    input_ids=lowerCamelCase__							,			spout=spouts[0]							,			max_new_tokens=2_0							,			generation_config=lowerCamelCase__							,			)

						UpperCAmelCase__:  int		       =   model.generate(
						    input_ids=lowerCamelCase__							,			spout=spouts[1]							,			max_new_tokens=2_0							,			generation_config=lowerCamelCase__							,			)

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

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

						UpperCAmelCase__:  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(lowerCamelCase__							,			lowerCamelCase__	)
						self.assertListEqual(lowerCamelCase__							,			[outa_sentence, outa_sentence]	)





					@slow
					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[Any]		       =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"	)
						UpperCAmelCase__:  List[Any]		       =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"	)
						model.to(lowerCamelCase__	)

						# set deterministically
						UpperCAmelCase__:  Any		       =   GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"	)
						UpperCAmelCase__:  Tuple		       =   1

						UpperCAmelCase__:  Optional[Any]		       =   "武田信玄"
						UpperCAmelCase__:  int		       =   "織田信長"
						UpperCAmelCase__:  Any		       =   "は、"
						UpperCAmelCase__:  int		       =   "が、"
						UpperCAmelCase__:  str		       =   tokenizer(lowerCamelCase__							,			prefix_text=lowerCamelCase__							,			return_tensors="pt"	)
						UpperCAmelCase__:  List[str]		       =   tokenizer(lowerCamelCase__							,			prefix_text=lowerCamelCase__							,			return_tensors="pt"	)
						UpperCAmelCase__:  Dict		       =   tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]							,			return_tensors="pt"	)

						UpperCAmelCase__:  Dict		       =   model.generate(
						    input_ids=input_tok_a.input_ids.to(lowerCamelCase__	)							,			token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase__	)							,			max_new_tokens=2_0							,			generation_config=lowerCamelCase__							,			)

						UpperCAmelCase__:  List[str]		       =   model.generate(
						    input_ids=input_tok_a.input_ids.to(lowerCamelCase__	)							,			token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase__	)							,			max_new_tokens=2_0							,			generation_config=lowerCamelCase__							,			)

						UpperCAmelCase__:  Optional[int]		       =   model.generate(
						    input_ids=input_tok_a.input_ids.to(lowerCamelCase__	)							,			token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase__	)							,			attention_mask=input_tok_a.attention_mask.to(lowerCamelCase__	)							,			max_new_tokens=2_0							,			generation_config=lowerCamelCase__							,			)

						UpperCAmelCase__:  List[Any]		       =   tokenizer.decode(outputa[0]	)
						UpperCAmelCase__:  Any		       =   tokenizer.decode(outputa[0]	)
						UpperCAmelCase__:  Union[str, Any]		       =   tokenizer.batch_decode(lowerCamelCase__	)

						UpperCAmelCase__:  int		       =   [
						    "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(lowerCamelCase__							,			lowerCamelCase__	)
						self.assertListEqual(lowerCamelCase__							,			[outa_sentence, outa_sentence]	)