





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,							A,							A=3_6000,							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=100,							A=0.0,							):





            """simple docstring"""

            _UpperCamelCase														=    vocab_size
            _UpperCamelCase														=    parent
            _UpperCamelCase														=    batch_size
            _UpperCamelCase														=    num_contexts
            # For common tests
            _UpperCamelCase														=    self.num_contexts
            _UpperCamelCase														=    is_training
            _UpperCamelCase														=    hidden_size
            _UpperCamelCase														=    num_ext_layers
            _UpperCamelCase														=    ext_size
            _UpperCamelCase														=    num_hidden_layers
            _UpperCamelCase														=    num_attention_heads
            _UpperCamelCase														=    num_experts
            _UpperCamelCase														=    d_ff
            _UpperCamelCase														=    d_ext
            _UpperCamelCase														=    d_spout
            _UpperCamelCase														=    dropout_rate
            _UpperCamelCase														=    layer_norm_epsilon
            _UpperCamelCase														=    expert_capacity
            _UpperCamelCase														=    router_jitter_noise

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    ids_tensor([self.batch_size, self.seq_length],							self.vocab_size   )

            _UpperCamelCase														=    self.get_config()

            return (config, input_ids)

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    ids_tensor([self.batch_size, self.seq_length],							self.vocab_size   )

            _UpperCamelCase														=    self.get_config()

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

     def 						_UpperCamelCase  (   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 						_UpperCamelCase  (   self,							A,							A,							):





            """simple docstring"""

            _UpperCamelCase														=    GPTSanJapaneseForConditionalGeneration(config=__UpperCamelCase   )
            model.to(__UpperCamelCase   )
            model.eval()
            _UpperCamelCase														=    model(
                input_ids=__UpperCamelCase,							)
            self.parent.assertIsNotNone(__UpperCamelCase   )






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

     def 						_UpperCamelCase  (   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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    GPTSanJapaneseTester(self   )
            _UpperCamelCase														=    ConfigTester(self,							config_class=__UpperCamelCase,							d_model=37   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            GPTSanJapaneseConfig()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__UpperCamelCase   )

     @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   ):





            """simple docstring"""

            super().test_model_parallelism()



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





            """simple docstring"""

            pass






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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    GPTSanJapaneseTester(self   )
            _UpperCamelCase														=    ConfigTester(self,							config_class=__UpperCamelCase,							d_model=37   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            GPTSanJapaneseConfig()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__UpperCamelCase   )

     @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   ):





            """simple docstring"""

            super().test_model_parallelism()

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





            """simple docstring"""

            pass

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

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

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

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
            _UpperCamelCase														=    GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
            model.to(__UpperCamelCase   )

            # set deterministically
            _UpperCamelCase														=    GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
            _UpperCamelCase														=    1

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

            _UpperCamelCase														=    "left"
            _UpperCamelCase														=    tokenizer(__UpperCamelCase,							return_tensors='''pt''',							padding=__UpperCamelCase   )
            _UpperCamelCase														=    inputs["input_ids"].to(__UpperCamelCase   )

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

            _UpperCamelCase														=    model.generate(
                input_ids=__UpperCamelCase,							attention_mask=inputs['''attention_mask'''].to(__UpperCamelCase   ),							max_new_tokens=3,							generation_config=__UpperCamelCase,							)

            _UpperCamelCase														=    tokenizer(sentences[0],							return_tensors='''pt'''   ).input_ids.to(__UpperCamelCase   )
            _UpperCamelCase														=    model.generate(
                input_ids=__UpperCamelCase,							max_new_tokens=3,							generation_config=__UpperCamelCase   )

            _UpperCamelCase														=    tokenizer(sentences[1],							return_tensors='''pt'''   ).input_ids.to(__UpperCamelCase   )
            _UpperCamelCase														=    model.generate(input_ids=__UpperCamelCase,							max_new_tokens=3,							generation_config=__UpperCamelCase   )

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

            _UpperCamelCase														=    tokenizer.batch_decode(__UpperCamelCase,							skip_special_tokens=__UpperCamelCase   )
            _UpperCamelCase														=    tokenizer.decode(output_non_padded[0],							skip_special_tokens=__UpperCamelCase   )
            _UpperCamelCase														=    tokenizer.decode(output_padded[0],							skip_special_tokens=__UpperCamelCase   )

            _UpperCamelCase														=    [
                "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(__UpperCamelCase,							__UpperCamelCase   )
            self.assertListEqual(__UpperCamelCase,							[non_padded_sentence, padded_sentence]   )

     @tooslow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
            _UpperCamelCase														=    GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
            # Output of original model created with mesh-tensoflow
            _UpperCamelCase														=    [
                ("武田信玄は", 3_5675),
                ("武田信玄は、", 45),
                ("武田信玄は、この", 29),
                ("武田信玄は、このよう", 3_0642),
                ("武田信玄は、このような", 3_5680),
                ("武田信玄は、このような「", 8640),
                ("武田信玄は、このような「武田", 3_1617),
                ("武田信玄は、このような「武田家", 3_0646),
                ("武田信玄は、このような「武田家の", 3_1617),
                ("武田信玄は、このような「武田家の家", 3_1381),
            ]
            for input, output in target:
                   _UpperCamelCase														=    tokenizer.encode(__UpperCamelCase,							return_tensors='''pt'''   )
                   _UpperCamelCase														=    model(__UpperCamelCase   )
                   _UpperCamelCase														=    outputs.logits.detach().cpu().numpy()[0]
                   _UpperCamelCase														=    np.argmax(output_logits[-1]   )
                   self.assertEqual(__UpperCamelCase,							__UpperCamelCase   )

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
            _UpperCamelCase														=    GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
            model.to(__UpperCamelCase   )

            # set deterministically
            _UpperCamelCase														=    GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
            _UpperCamelCase														=    1

            _UpperCamelCase														=    "武田信玄は、"
            _UpperCamelCase														=    tokenizer(__UpperCamelCase,							return_tensors='''pt'''   ).input_ids.to(__UpperCamelCase   )
            _UpperCamelCase														=    tokenizer([input_text, input_text],							return_tensors='''pt'''   ).input_ids.to(__UpperCamelCase   )

            # spout from uniform and one-hot
            _UpperCamelCase														=    [
                # 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														=    model.generate(
                input_ids=__UpperCamelCase,							spout=spouts[0],							max_new_tokens=20,							generation_config=__UpperCamelCase,							)

            _UpperCamelCase														=    model.generate(
                input_ids=__UpperCamelCase,							spout=spouts[1],							max_new_tokens=20,							generation_config=__UpperCamelCase,							)

            _UpperCamelCase														=    model.generate(
                input_ids=__UpperCamelCase,							spout=__UpperCamelCase,							max_new_tokens=20,							generation_config=__UpperCamelCase,							)

            _UpperCamelCase														=    tokenizer.decode(outputa[0]   )
            _UpperCamelCase														=    tokenizer.decode(outputa[0]   )
            _UpperCamelCase														=    tokenizer.batch_decode(__UpperCamelCase   )

            _UpperCamelCase														=    [
                "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(__UpperCamelCase,							__UpperCamelCase   )
            self.assertListEqual(__UpperCamelCase,							[outa_sentence, outa_sentence]   )



     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
            _UpperCamelCase														=    GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
            model.to(__UpperCamelCase   )

            # set deterministically
            _UpperCamelCase														=    GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''   )
            _UpperCamelCase														=    1

            _UpperCamelCase														=    "武田信玄"
            _UpperCamelCase														=    "織田信長"
            _UpperCamelCase														=    "は、"
            _UpperCamelCase														=    "が、"
            _UpperCamelCase														=    tokenizer(__UpperCamelCase,							prefix_text=__UpperCamelCase,							return_tensors='''pt'''   )
            _UpperCamelCase														=    tokenizer(__UpperCamelCase,							prefix_text=__UpperCamelCase,							return_tensors='''pt'''   )
            _UpperCamelCase														=    tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]],							return_tensors='''pt'''   )

            _UpperCamelCase														=    model.generate(
                input_ids=input_tok_a.input_ids.to(__UpperCamelCase   ),							token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase   ),							max_new_tokens=20,							generation_config=__UpperCamelCase,							)

            _UpperCamelCase														=    model.generate(
                input_ids=input_tok_a.input_ids.to(__UpperCamelCase   ),							token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase   ),							max_new_tokens=20,							generation_config=__UpperCamelCase,							)

            _UpperCamelCase														=    model.generate(
                input_ids=input_tok_a.input_ids.to(__UpperCamelCase   ),							token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase   ),							attention_mask=input_tok_a.attention_mask.to(__UpperCamelCase   ),							max_new_tokens=20,							generation_config=__UpperCamelCase,							)

            _UpperCamelCase														=    tokenizer.decode(outputa[0]   )
            _UpperCamelCase														=    tokenizer.decode(outputa[0]   )
            _UpperCamelCase														=    tokenizer.batch_decode(__UpperCamelCase   )

            _UpperCamelCase														=    [
                "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(__UpperCamelCase,							__UpperCamelCase   )
            self.assertListEqual(__UpperCamelCase,							[outa_sentence, outa_sentence]   )








"""simple docstring"""




import argparse
import os


A__   :						Any 							=     {
    """pytorch""": {
        """1.13""": {
            """torch""": """1.13.1""",
            """torchvision""": """0.14.1""",
            """torchaudio""": """0.13.1""",
            """python""": 3.9,
            """cuda""": """cu116""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu116"""
            ),
            """base_image""": """nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.12""": {
            """torch""": """1.12.1""",
            """torchvision""": """0.13.1""",
            """torchaudio""": """0.12.1""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.11""": {
            """torch""": """1.11.0""",
            """torchvision""": """0.12.0""",
            """torchaudio""": """0.11.0""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.10""": {
            """torch""": """1.10.2""",
            """torchvision""": """0.11.3""",
            """torchaudio""": """0.10.2""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        """1.9""": {
            """torch""": """1.9.1""",
            """torchvision""": """0.10.1""",
            """torchaudio""": """0.9.1""",
            """python""": 3.9,
            """cuda""": """cu111""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu111"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
    """tensorflow""": {
        """2.11""": {
            """tensorflow""": """2.11.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.11.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.10""": {
            """tensorflow""": """2.10.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.10.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.9""": {
            """tensorflow""": """2.9.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.9.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.8""": {
            """tensorflow""": """2.8.2""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.8.2""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.7""": {
            """tensorflow""": """2.7.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.7.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.6""": {
            """tensorflow""": """2.6.5""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.6.5""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.5""": {
            """tensorflow""": """2.5.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.5.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
}


if __name__ == "__main__":
    A__   :						str 							=     argparse.ArgumentParser("""Choose the framework and version to install""")
    parser.add_argument(
        """--framework""", help="""The framework to install. Should be `torch` or `tensorflow`""", type=str, required=True
    )
    parser.add_argument("""--version""", help="""The version of the framework to install.""", type=str, required=True)
    A__   :						Any 							=     parser.parse_args()

    A__   :						List[Any] 							=     past_versions_testing[args.framework][args.version]

    os.system(f"""echo \"export INSTALL_CMD=\'{info["install"]}\'\" >> ~/.profile""")
    print(f"""echo \"export INSTALL_CMD=\'{info["install"]}\'\" >> ~/.profile""")

    A__   :						Any 							=     """"""
    if args.framework == "pytorch":
        A__   :						str 							=     info["""cuda"""]
    os.system(f"""echo \"export CUDA='{cuda}'\" >> ~/.profile""")
    print(f"""echo \"export CUDA='{cuda}'\" >> ~/.profile""")



