
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 __snake_case	:
    def __init__(						self	,snake_case	,snake_case=36000	,snake_case=13	,snake_case=7	,snake_case=True	,snake_case=32	,snake_case=42	,snake_case=5	,snake_case=2	,snake_case=4	,snake_case=2	,snake_case=32	,snake_case=80	,snake_case=33	,snake_case=0.0	,snake_case=1e-6	,snake_case=100	,snake_case=0.0	,):



           '''simple docstring'''







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







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

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

           return (config, input_ids)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size   )

           lowercase							:  Optional[Any] 			=   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	,snake_case	,snake_case	,):



           '''simple docstring'''







           lowercase							:  Tuple 			=   GPTSanJapaneseForConditionalGeneration(config=snake_case   )
           model.to(snake_case   )
           model.eval()
           lowercase							:  Dict 			=   model(
               input_ids=snake_case	,)
           self.parent.assertIsNotNone(snake_case   )






@require_torch
class __snake_case	(   lowerCAmelCase      ,			lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		str=       (GPTSanJapaneseModel,) if is_torch_available() else ()
    _a	:		List[Any]=       (
        {
            "conversational": GPTSanJapaneseForConditionalGeneration,
            "feature-extraction": GPTSanJapaneseForConditionalGeneration,
            "summarization": GPTSanJapaneseForConditionalGeneration,
            "text2text-generation": GPTSanJapaneseForConditionalGeneration,
            "translation": GPTSanJapaneseForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    _a	:		List[str]=       False
    _a	:		int=       False
    _a	:		Union[str, Any]=       False
    _a	:		Any=       False
    _a	:		int=       False
    _a	:		Dict=       False
    _a	:		List[Any]=       False
    _a	:		List[Any]=       False
    # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
    _a	:		str=       [0.8, 0.9]
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case   ):



           '''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'''







           lowercase							:  int 			=   GPTSanJapaneseTester(self   )
           lowercase							:  Dict 			=   ConfigTester(self	,config_class=snake_case	,d_model=37   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           GPTSanJapaneseConfig()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case   )
    @unittest.skip(
        reason="""skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"""   )
    def    _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 __snake_case	(   lowerCAmelCase      ,			lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		str=       (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
    _a	:		Tuple=       False
    _a	:		int=       False
    _a	:		List[Any]=       False
    _a	:		Dict=       False
    _a	:		str=       False
    _a	:		Any=       False
    # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
    _a	:		Optional[int]=       [0.8, 0.9]
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   GPTSanJapaneseTester(self   )
           lowercase							:  List[Any] 			=   ConfigTester(self	,config_class=snake_case	,d_model=37   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           GPTSanJapaneseConfig()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case   )
    @unittest.skip(
        reason="""skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"""   )
    def    _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'''







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

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

           self.assertTrue(np.all([check(target[i]	,predict[i]   ) for i in range(len(snake_case   )   )]   )   )
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
           lowercase							:  Union[str, Any] 			=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
           model.to(snake_case   )

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

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

           lowercase							:  Dict 			=   """left"""
           lowercase							:  List[str] 			=   tokenizer(snake_case	,return_tensors="""pt"""	,padding=snake_case   )
           lowercase							:  List[Any] 			=   inputs["""input_ids"""].to(snake_case   )

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

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

           lowercase							:  List[Any] 			=   tokenizer(sentences[0]	,return_tensors="""pt"""   ).input_ids.to(snake_case   )
           lowercase							:  Union[str, Any] 			=   model.generate(
               input_ids=snake_case	,max_new_tokens=3	,generation_config=snake_case   )

           lowercase							:  Any 			=   tokenizer(sentences[1]	,return_tensors="""pt"""   ).input_ids.to(snake_case   )
           lowercase							:  List[Any] 			=   model.generate(input_ids=snake_case	,max_new_tokens=3	,generation_config=snake_case   )

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

           lowercase							:  Dict 			=   tokenizer.batch_decode(snake_case	,skip_special_tokens=snake_case   )
           lowercase							:  Union[str, Any] 			=   tokenizer.decode(output_non_padded[0]	,skip_special_tokens=snake_case   )
           lowercase							:  Any 			=   tokenizer.decode(output_padded[0]	,skip_special_tokens=snake_case   )

           lowercase							:  str 			=   [
               """甲斐なら武田と言うほど甲斐の武田""",
               """織田信長は、このような""",
           ]
           self.assertListEqual(snake_case	,snake_case   )
           self.assertListEqual(snake_case	,[non_padded_sentence, padded_sentence]   )
    @tooslow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
           lowercase							:  Optional[Any] 			=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
           model.to(snake_case   )

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

           lowercase							:  List[Any] 			=   """武田信玄は、"""
           lowercase							:  str 			=   tokenizer(snake_case	,return_tensors="""pt"""   ).input_ids.to(snake_case   )
           lowercase							:  List[str] 			=   tokenizer([input_text, input_text]	,return_tensors="""pt"""   ).input_ids.to(snake_case   )

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

           lowercase							:  Optional[Any] 			=   model.generate(
               input_ids=snake_case	,spout=spouts[0]	,max_new_tokens=20	,generation_config=snake_case	,)

           lowercase							:  Tuple 			=   model.generate(
               input_ids=snake_case	,spout=spouts[1]	,max_new_tokens=20	,generation_config=snake_case	,)

           lowercase							:  Tuple 			=   model.generate(
               input_ids=snake_case	,spout=snake_case	,max_new_tokens=20	,generation_config=snake_case	,)

           lowercase							:  List[str] 			=   tokenizer.decode(outputa[0]   )
           lowercase							:  Optional[Any] 			=   tokenizer.decode(outputa[0]   )
           lowercase							:  str 			=   tokenizer.batch_decode(snake_case   )

           lowercase							:  List[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(snake_case	,snake_case   )
           self.assertListEqual(snake_case	,[outa_sentence, outa_sentence]   )
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
           lowercase							:  Dict 			=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
           model.to(snake_case   )

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

           lowercase							:  Union[str, Any] 			=   """武田信玄"""
           lowercase							:  Dict 			=   """織田信長"""
           lowercase							:  Tuple 			=   """は、"""
           lowercase							:  int 			=   """が、"""
           lowercase							:  List[str] 			=   tokenizer(snake_case	,prefix_text=snake_case	,return_tensors="""pt"""   )
           lowercase							:  Tuple 			=   tokenizer(snake_case	,prefix_text=snake_case	,return_tensors="""pt"""   )
           lowercase							:  List[Any] 			=   tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]	,return_tensors="""pt"""   )

           lowercase							:  Optional[Any] 			=   model.generate(
               input_ids=input_tok_a.input_ids.to(snake_case   )	,token_type_ids=input_tok_a.token_type_ids.to(snake_case   )	,max_new_tokens=20	,generation_config=snake_case	,)

           lowercase							:  Optional[int] 			=   model.generate(
               input_ids=input_tok_a.input_ids.to(snake_case   )	,token_type_ids=input_tok_a.token_type_ids.to(snake_case   )	,max_new_tokens=20	,generation_config=snake_case	,)

           lowercase							:  int 			=   model.generate(
               input_ids=input_tok_a.input_ids.to(snake_case   )	,token_type_ids=input_tok_a.token_type_ids.to(snake_case   )	,attention_mask=input_tok_a.attention_mask.to(snake_case   )	,max_new_tokens=20	,generation_config=snake_case	,)

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

           lowercase							:  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(snake_case	,snake_case   )
           self.assertListEqual(snake_case	,[outa_sentence, outa_sentence]   )


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class __snake_case	(   unittest.TestCase  ):
    def __init__(						self	,snake_case	,snake_case=7	,snake_case=3	,snake_case=10	,snake_case=18	,snake_case=30	,snake_case=400	,snake_case=True	,snake_case=None	,snake_case=True	,snake_case=[0.5, 0.5, 0.5]	,snake_case=[0.5, 0.5, 0.5]	,snake_case=None	,):



           '''simple docstring'''







           lowercase							:  Dict 			=   size if size is not None else {"""shortest_edge""": 18}
           lowercase							:  Tuple 			=   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           lowercase							:  Optional[int] 			=   parent
           lowercase							:  Any 			=   batch_size
           lowercase							:  int 			=   num_channels
           lowercase							:  Dict 			=   num_frames
           lowercase							:  Any 			=   image_size
           lowercase							:  str 			=   min_resolution
           lowercase							:  Any 			=   max_resolution
           lowercase							:  Tuple 			=   do_resize
           lowercase							:  Optional[Any] 			=   size
           lowercase							:  str 			=   do_normalize
           lowercase							:  Optional[Any] 			=   image_mean
           lowercase							:  Optional[Any] 			=   image_std
           lowercase							:  Union[str, Any] 			=   crop_size
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "crop_size": self.crop_size,
           }






@require_torch
@require_vision
class __snake_case	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		Optional[Any]=       VideoMAEImageProcessor if is_vision_available() else None
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   VideoMAEImageProcessingTester(self   )
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(snake_case	,"""image_mean"""   )   )
           self.assertTrue(hasattr(snake_case	,"""image_std"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_normalize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_resize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_center_crop"""   )   )
           self.assertTrue(hasattr(snake_case	,"""size"""   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 18}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 18, """width""": 18}   )

           lowercase							:  Any 			=   self.image_processing_class.from_dict(self.image_processor_dict	,size=42	,crop_size=84   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 42}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 84, """width""": 84}   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           pass
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PIL videos
           lowercase							:  Optional[int] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,Image.Image   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random numpy tensors
           lowercase							:  Optional[Any] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,numpify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,np.ndarray   )

           # Test not batched input
           lowercase							:  int 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Optional[int] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PyTorch tensors
           lowercase							:  List[str] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,torchify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,torch.Tensor   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
