

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				:




      """simple docstring"""

      def __init__(      self       :							Optional[Any]	, UpperCAmelCase       :							str	, UpperCAmelCase       :							int=36000	, UpperCAmelCase       :							int=13	, UpperCAmelCase       :							List[Any]=7	, UpperCAmelCase       :							List[str]=True	, UpperCAmelCase       :							str=32	, UpperCAmelCase       :							Optional[Any]=42	, UpperCAmelCase       :							str=5	, UpperCAmelCase       :							List[str]=2	, UpperCAmelCase       :							str=4	, UpperCAmelCase       :							Tuple=2	, UpperCAmelCase       :							Optional[Any]=32	, UpperCAmelCase       :							List[str]=80	, UpperCAmelCase       :							Tuple=33	, UpperCAmelCase       :							Dict=0.0	, UpperCAmelCase       :							Any=1E-6	, UpperCAmelCase       :							str=100	, UpperCAmelCase       :							Tuple=0.0	, ):
            A_	     =							vocab_size
            A_	     =							parent
            A_	     =							batch_size
            A_	     =							num_contexts
            # For common tests
            A_	     =							self.num_contexts
            A_	     =							is_training
            A_	     =							hidden_size
            A_	     =							num_ext_layers
            A_	     =							ext_size
            A_	     =							num_hidden_layers
            A_	     =							num_attention_heads
            A_	     =							num_experts
            A_	     =							d_ff
            A_	     =							d_ext
            A_	     =							d_spout
            A_	     =							dropout_rate
            A_	     =							layer_norm_epsilon
            A_	     =							expert_capacity
            A_	     =							router_jitter_noise





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





      def 		__A  (      self       :							Any  ):
            A_	     =							ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size  )

            A_	     =							self.get_config()

            return (config, input_ids)





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

            A_	     =							self.get_config()

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





      def 		__A  (      self       :							Any  ):
            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 		__A  (      self       :							Union[str, Any]	, UpperCAmelCase       :							Dict	, UpperCAmelCase       :							Optional[int]	, ):
            A_	     =							GPTSanJapaneseForConditionalGeneration(config=UpperCamelCase__  )
            model.to(UpperCamelCase__  )
            model.eval()
            A_	     =							model(
                input_ids=UpperCamelCase__	, )
            self.parent.assertIsNotNone(UpperCamelCase__  )




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




      """simple docstring"""

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





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							List[str]	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Tuple  ):







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

            return False





      def 		__A  (      self       :							Union[str, Any]  ):
            A_	     =							GPTSanJapaneseTester(self  )
            A_	     =							ConfigTester(self	, config_class=UpperCamelCase__	, d_model=37  )





      def 		__A  (      self       :							Any  ):
            GPTSanJapaneseConfig()





      def 		__A  (      self       :							Optional[Any]  ):
            A_	     =							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 		__A  (      self       :							List[str]  ):
            super().test_model_parallelism()







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




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




      """simple docstring"""

      _lowerCamelCase					: Dict					=					(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
      _lowerCamelCase					: int					=					False
      _lowerCamelCase					: Union[str, Any]					=					False
      _lowerCamelCase					: Tuple					=					False
      _lowerCamelCase					: Optional[Any]					=					False
      _lowerCamelCase					: List[str]					=					False
      _lowerCamelCase					: Union[str, Any]					=					False
      # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
      _lowerCamelCase					: List[Any]					=					[0.8, 0.9]





      def 		__A  (      self       :							Any  ):
            A_	     =							GPTSanJapaneseTester(self  )
            A_	     =							ConfigTester(self	, config_class=UpperCamelCase__	, d_model=37  )





      def 		__A  (      self       :							Dict  ):
            GPTSanJapaneseConfig()





      def 		__A  (      self       :							Tuple  ):
            A_	     =							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 		__A  (      self       :							Optional[Any]  ):
            super().test_model_parallelism()





      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
      def 		__A  (      self       :							Union[str, Any]  ):
            pass





      @slow
      def 		__A  (      self       :							Union[str, Any]  ):
            A_	     =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
            A_	     =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
            A_	     =							tokenizer.encode("武田信玄は"	, return_tensors="pt"  )
            A_	     =							model(UpperCamelCase__  )
            A_	     =							outputs.logits.detach().cpu().numpy()
            # Output of original model created with mesh-tensoflow
            A_	     =							[
                # 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
            ]
            A_	     =							np.array(UpperCamelCase__  ).flatten()
            A_	     =							output_logits[0, :, :20].flatten()

            def check(UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							List[Any]=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 		__A  (      self       :							Any  ):
            A_	     =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
            A_	     =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
            model.to(UpperCamelCase__  )

            # set deterministically
            A_	     =							GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"  )
            A_	     =							1

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

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

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

            A_	     =							model.generate(
                input_ids=UpperCamelCase__	, attention_mask=inputs["attention_mask"].to(UpperCamelCase__  )	, max_new_tokens=3	, generation_config=UpperCamelCase__	, )

            A_	     =							tokenizer(sentences[0]	, return_tensors="pt"  ).input_ids.to(UpperCamelCase__  )
            A_	     =							model.generate(
                input_ids=UpperCamelCase__	, max_new_tokens=3	, generation_config=UpperCamelCase__  )

            A_	     =							tokenizer(sentences[1]	, return_tensors="pt"  ).input_ids.to(UpperCamelCase__  )
            A_	     =							model.generate(input_ids=UpperCamelCase__	, max_new_tokens=3	, generation_config=UpperCamelCase__  )

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

            A_	     =							tokenizer.batch_decode(UpperCamelCase__	, skip_special_tokens=UpperCamelCase__  )
            A_	     =							tokenizer.decode(output_non_padded[0]	, skip_special_tokens=UpperCamelCase__  )
            A_	     =							tokenizer.decode(output_padded[0]	, skip_special_tokens=UpperCamelCase__  )

            A_	     =							[
                '''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 		__A  (      self       :							int  ):
            A_	     =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
            A_	     =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
            # Output of original model created with mesh-tensoflow
            A_	     =							[
                ('''武田信玄は''', 35675),
                ('''武田信玄は、''', 45),
                ('''武田信玄は、この''', 29),
                ('''武田信玄は、このよう''', 30642),
                ('''武田信玄は、このような''', 35680),
                ('''武田信玄は、このような「''', 8640),
                ('''武田信玄は、このような「武田''', 31617),
                ('''武田信玄は、このような「武田家''', 30646),
                ('''武田信玄は、このような「武田家の''', 31617),
                ('''武田信玄は、このような「武田家の家''', 31381),
            ]
            for input, output in target:
                  A_	     =							tokenizer.encode(UpperCamelCase__	, return_tensors="pt"  )
                  A_	     =							model(UpperCamelCase__  )
                  A_	     =							outputs.logits.detach().cpu().numpy()[0]
                  A_	     =							np.argmax(output_logits[-1]  )
                  self.assertEqual(UpperCamelCase__	, UpperCamelCase__  )





      @slow
      def 		__A  (      self       :							Tuple  ):
            A_	     =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
            A_	     =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
            model.to(UpperCamelCase__  )

            # set deterministically
            A_	     =							GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"  )
            A_	     =							1

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

            # spout from uniform and one-hot
            A_	     =							[
                # 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
            ]

            A_	     =							model.generate(
                input_ids=UpperCamelCase__	, spout=spouts[0]	, max_new_tokens=20	, generation_config=UpperCamelCase__	, )

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

            A_	     =							model.generate(
                input_ids=UpperCamelCase__	, spout=UpperCamelCase__	, max_new_tokens=20	, generation_config=UpperCamelCase__	, )

            A_	     =							tokenizer.decode(outputa[0]  )
            A_	     =							tokenizer.decode(outputa[0]  )
            A_	     =							tokenizer.batch_decode(UpperCamelCase__  )

            A_	     =							[
                '''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 		__A  (      self       :							str  ):
            A_	     =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
            A_	     =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
            model.to(UpperCamelCase__  )

            # set deterministically
            A_	     =							GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"  )
            A_	     =							1

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

            A_	     =							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__	, )

            A_	     =							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__	, )

            A_	     =							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__	, )

            A_	     =							tokenizer.decode(outputa[0]  )
            A_	     =							tokenizer.decode(outputa[0]  )
            A_	     =							tokenizer.batch_decode(UpperCamelCase__  )

            A_	     =							[
                '''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]  )









__a						:Dict            = {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'

def 			__snake_case	(   __UpperCamelCase  :   str						):



      """simple docstring"""

      A_	     =							hex_string.strip().lower()
      if not hex_string:
            raise ValueError("Empty string was passed to the function"						)
      A_	     =							hex_string[0] == "-"
      if is_negative:
            A_	     =							hex_string[1:]
      if not all(char in hex_table for char in hex_string						):
            raise ValueError("Non-hexadecimal value was passed to the function"						)
      A_	     =							0
      for char in hex_string:
            A_	     =							16 * decimal_number + hex_table[char]
      return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
      from doctest import testmod

      testmod()