


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    __magic_name__						:


     def __init__(   self				:  List[Any]				,						lowerCamelCase__				:  List[Any]				,						lowerCamelCase__				:  List[str]=36000				,						lowerCamelCase__				:  Optional[Any]=13				,						lowerCamelCase__				:  int=7				,						lowerCamelCase__				:  Optional[int]=True				,						lowerCamelCase__				:  Union[str, Any]=32				,						lowerCamelCase__				:  Optional[Any]=42				,						lowerCamelCase__				:  str=5				,						lowerCamelCase__				:  Tuple=2				,						lowerCamelCase__				:  int=4				,						lowerCamelCase__				:  List[Any]=2				,						lowerCamelCase__				:  Union[str, Any]=32				,						lowerCamelCase__				:  int=80				,						lowerCamelCase__				:  Dict=33				,						lowerCamelCase__				:  str=0.0				,						lowerCamelCase__				:  str=1E-6				,						lowerCamelCase__				:  Any=100				,						lowerCamelCase__				:  Union[str, Any]=0.0				,						)	->   str:





      '''simple docstring'''






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


     def   UpperCAmelCase__						(   self				:  Any      )	->   List[Any]:





      '''simple docstring'''






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


     def   UpperCAmelCase__						(   self				:  List[str]      )	->   Tuple:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size      )

      UpperCamelCase__       :     Dict 							= self.get_config()

      return (config, input_ids)


     def   UpperCAmelCase__						(   self				:  List[str]      )	->   Optional[int]:





      '''simple docstring'''






      UpperCamelCase__       :     List[Any] 							= ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size      )

      UpperCamelCase__       :     List[Any] 							= self.get_config()

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


     def   UpperCAmelCase__						(   self				:  Tuple      )	->   Optional[Any]:





      '''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				:  List[str]				,						lowerCamelCase__				:  Any				,						lowerCamelCase__				:  Optional[int]				,						)	->   Dict:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= GPTSanJapaneseForConditionalGeneration(config=lowerCamelCase__      )
      model.to(lowerCamelCase__      )
      model.eval()
      UpperCamelCase__       :     List[str] 							= model(
          input_ids=lowerCamelCase__				,						)
      self.parent.assertIsNotNone(lowerCamelCase__      )



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


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  Tuple				,						lowerCamelCase__				:  Any				,						lowerCamelCase__				:  Dict				,						lowerCamelCase__				:  List[Any]				,						lowerCamelCase__				:  Optional[Any]      )	->   Union[str, Any]:





      '''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				:  str      )	->   Tuple:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= GPTSanJapaneseTester(self      )
      UpperCamelCase__       :     Dict 							= ConfigTester(self				,						config_class=lowerCamelCase__				,						d_model=37      )


     def   UpperCAmelCase__						(   self				:  Any      )	->   List[str]:





      '''simple docstring'''






      GPTSanJapaneseConfig()


     def   UpperCAmelCase__						(   self				:  Dict      )	->   Tuple:





      '''simple docstring'''






      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				:  List[str]      )	->   List[str]:





      '''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				:  Tuple      )	->   Tuple:





      '''simple docstring'''






      pass



@require_torch
class    __magic_name__						(  __lowerCAmelCase       ,					__lowerCAmelCase       ,					unittest.TestCase):
     A:		Dict								=   (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
     A:		Optional[Any]								=   False
     A:		Optional[Any]								=   False
     A:		Any								=   False
     A:		str								=   False
     A:		List[Any]								=   False
     A:		Any								=   False
     # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
     A:		Optional[Any]								=   [0.8, 0.9]


     def   UpperCAmelCase__						(   self				:  Optional[int]      )	->   Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Any 							= GPTSanJapaneseTester(self      )
      UpperCamelCase__       :     Tuple 							= ConfigTester(self				,						config_class=lowerCamelCase__				,						d_model=37      )


     def   UpperCAmelCase__						(   self				:  str      )	->   Optional[int]:





      '''simple docstring'''






      GPTSanJapaneseConfig()


     def   UpperCAmelCase__						(   self				:  Tuple      )	->   Optional[int]:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= 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				:  Union[str, Any]      )	->   str:





      '''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				:  List[Any]      )	->   Optional[Any]:





      '''simple docstring'''






      pass


     @slow
     def   UpperCAmelCase__						(   self				:  Optional[int]      )	->   Optional[int]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
      UpperCamelCase__       :     Optional[Any] 							= GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
      UpperCamelCase__       :     List[Any] 							= tokenizer.encode('''武田信玄は'''				,						return_tensors='''pt'''      )
      UpperCamelCase__       :     Tuple 							= model(lowerCamelCase__      )
      UpperCamelCase__       :     Optional[int] 							= outputs.logits.detach().cpu().numpy()
      # Output of original model created with mesh-tensoflow
      UpperCamelCase__       :     Optional[int] 							= [
          # fmt: off
          [-12.037_8398_8952_6367, -12.433_0615_9973_1445, -14.333_8403_7017_8223, -12.450_3459_9304_1992, -11.1_6613_7695_3125,
          -11.930_1376_3427_7344, -10.659_7404_4799_8047, -12.909_5745_0866_6992, -13.241_0430_9082_0312, -13.398_5795_9747_3145,
          -11.107_5248_7182_6172, -12.3_6859_4169_6167, -22.97_9431_1523_4375, -10.481_0676_5747_0703, -12.484_0307_2357_1777,
          -12.807_3606_4910_8887, -14.769_7000_5035_4004, -12.233_5796_3562_0117, -13.428_1454_0863_0371, -22.624_1779_3273_9258],
          [-7.511_1498_8327_0264, -8.281_8517_6849_3652, -7.943_1271_5530_3955, -7.55_0213_3369_4458, -6.49_8699_6650_6958,
          -7.586_7962_8372_1924, -6.978_0859_9472_0459, -7.839_1451_8356_3232, -8.21_9641_6854_8584, -8.695_0912_4755_8594,
          -6.706_9106_1019_8975, -6.6585_7982_6354_9805, -19.565_6986_2365_7227, -5.353_8427_3529_0527, -8.350_6860_7330_3223,
          -8.039_3886_5661_6211, -10.856_5692_9016_1133, -7.75_1544_4755_5542, -8.819_0221_7864_9902, -19.51_5327_4536_1328],
          [-9.73_0662_3458_8623, -10.223_9227_2949_2188, -9.932_9814_9108_8867, -11.857_8367_2332_7637, -7.662_6262_6647_9492,
          -11.13_5291_0995_4834, -7.765_0976_1810_3027, -11.472_9232_7880_8594, -9.543_1499_4812_0117, -11.905_6339_2639_1602,
          -9.366_1642_0745_8496, -11.5_7342_8153_9917, -23.699_0032_1960_4492, -9.429_5902_2521_9727, -10.42_8392_4102_7832,
          -10.585_2403_6407_4707, -10.94_7719_5739_7461, -11.095_4160_6903_0762, -10.390_2406_6925_0488, -23.769_3729_4006_3477],
          [-9.728_2657_6232_9102, -9.859_7126_0070_8008, -10.09_7292_9000_8545, -9.678_5221_0998_5352, -6.879_5199_3942_2607,
          -9.68_4875_4882_8125, -4.2803_4257_8887_9395, -10.018_9142_2271_7285, -9.308_4459_3048_0957, -10.63_3945_4650_8789,
          -8.083_6467_7429_1992, -9.06_3014_9841_3086, -21.904_2663_5742_1875, -8.90_1608_4671_0205, -8.841_8760_2996_8262,
          -11.856_7199_7070_3125, -12.079_3981_5521_2402, -11.233_7532_0434_5703, -10.177_3386_0015_8691, -21.87_2566_2231_4453],
          [-9.669_7645_1873_7793, -9.614_1986_8469_2383, -9.814_5103_4545_8984, -9.996_5019_2260_7422, -11.375_6904_6020_5078,
          -10.113_4052_2766_1133, -10.546_8673_7060_5469, -10.04_3690_6814_5752, -10.907_8092_5750_7324, -10.504_2161_9415_2832,
          -11.129_1990_2801_5137, -10.151_1240_0054_9316, -21.96_5867_9962_1582, -9.086_3494_8730_4688, -11.730_3390_5029_2969,
          -10.460_6676_1016_8457, -10.298_0499_2675_7812, -10.784_1482_1624_7559, -10.840_6934_7381_5918, -22.03_1528_4729_0039],
          # fmt: on
      ]
      UpperCamelCase__       :     Dict 							= np.array(lowerCamelCase__      ).flatten()
      UpperCamelCase__       :     Any 							= output_logits[0, :, :20].flatten()

      def check(lowerCamelCase__				:  int				,						lowerCamelCase__				:  Any				,						lowerCamelCase__				:  Optional[int]=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				:  List[str]      )	->   Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Any 							= GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
      UpperCamelCase__       :     Union[str, Any] 							= 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__       :     Dict 							= [
          '''甲斐なら武田と言うほど''',
          '''織田信長は、''',
      ]

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

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

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

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

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

      UpperCamelCase__       :     Tuple 							= [
          '''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				:  Any      )	->   str:





      '''simple docstring'''






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


     @slow
     def   UpperCAmelCase__						(   self				:  Optional[Any]      )	->   Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__       :     List[str] 							= GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
      UpperCamelCase__       :     int 							= GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
      model.to(lowerCamelCase__      )

      # set deterministically
      UpperCamelCase__       :     List[Any] 							= GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
      UpperCamelCase__       :     Dict 							= 1

      UpperCamelCase__       :     Any 							= '''武田信玄は、'''
      UpperCamelCase__       :     Optional[int] 							= tokenizer(lowerCamelCase__				,						return_tensors='''pt'''      ).input_ids.to(lowerCamelCase__      )
      UpperCamelCase__       :     List[Any] 							= tokenizer([input_text, input_text]				,						return_tensors='''pt'''      ).input_ids.to(lowerCamelCase__      )

      # spout from uniform and one-hot
      UpperCamelCase__       :     int 							= [
          # fmt: off
          [0.8788_2208, 0.3842_6396, 0.3322_0248, 0.4389_0406, 0.1656_2252,
          0.0480_3985, 0.21_1572  , 0.2318_8473, 0.3715_3068, 0.783_6377 ,
          0.0216_0172, 0.3876_1719, 0.7529_0772, 0.9019_8857, 0.3436_5777,
          0.6416_8169, 0.4431_8471, 0.1457_5746, 0.9256_2881, 0.4081_2148,
          0.2901_9122, 0.8886_1599, 0.6552_4846, 0.4356_3456, 0.3817_7187,
          0.7083_2965, 0.8152_7892, 0.6883_2812, 0.3883_3192, 0.456_1522 ,
          0.1482_8817, 0.4724_8213, 0.5435_7335, 0.8200_9566, 0.133_8884 ,
          0.0275_5417, 0.1976_4677, 0.242_2084 , 0.0475_7674, 0.6540_9606,
          0.082_4589 , 0.0330_4383, 0.9438_7689, 0.9876_4509, 0.8243_3901,
          0.2764_6741, 0.6490_7493, 0.7600_9406, 0.3008_7915, 0.1790_4689,
          0.4160_1714, 0.6704_6398, 0.1042_2822, 0.0844_7374, 0.0735_4344,
          0.6142_3565, 0.7028_4866, 0.753_2333 , 0.197_2038 , 0.2957_5659,
          0.9058_3886, 0.2926_5307, 0.5000_0175, 0.7040_7655, 0.88_9363  ,
          0.8190_4418, 0.6682_9128, 0.6446_8815, 0.5656_3723, 0.8560_1875,
          0.9492_4672, 0.0016_6762, 0.2522_0643, 0.7454_0219, 0.6799_3247,
          0.154_9675 , 0.3938_5352, 0.9215_3607, 0.6374_5931, 0.2775_9043,
          0.8470_2295, 0.6590_4271, 0.5867_6614, 0.866_6936 , 0.3960_7438,
          0.7995_4983, 0.4222_0697, 0.3965_0381, 0.784_9864 , 0.5615_0201,
          0.1567_8925, 0.1474_6032, 0.3454_2114, 0.4702_6783, 0.1195_6489,
          0.2542_1435, 0.3378_8901, 0.6893_4842, 0.3642_4685, 0.7173_7898,
          0.3898_3449, 0.9439_3779, 0.3957_5588, 0.3661_6553, 0.8710_4665,
          0.6463_0203, 0.2251_6905, 0.8827_0804, 0.1503_1338, 0.7514_4345,
          0.4645_9025, 0.8539_6454, 0.8635_5643, 0.6513_9851, 0.7026_6061,
          0.3024_1389, 0.8105_6497, 0.8886_5969, 0.3877_3807, 0.7063_5849,
          0.9071_8459, 0.4324_5789, 0.2800_0654, 0.4593_5562, 0.0877_3519,
          0.955_2151 , 0.9390_1511, 0.2248_9288], # uniform
          [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0.],
          # fmt: on
      ]

      UpperCamelCase__       :     List[str] 							= model.generate(
          input_ids=lowerCamelCase__				,						spout=spouts[0]				,						max_new_tokens=20				,						generation_config=lowerCamelCase__				,						)

      UpperCamelCase__       :     Optional[int] 							= model.generate(
          input_ids=lowerCamelCase__				,						spout=spouts[1]				,						max_new_tokens=20				,						generation_config=lowerCamelCase__				,						)

      UpperCamelCase__       :     Union[str, Any] 							= model.generate(
          input_ids=lowerCamelCase__				,						spout=lowerCamelCase__				,						max_new_tokens=20				,						generation_config=lowerCamelCase__				,						)

      UpperCamelCase__       :     Dict 							= tokenizer.decode(outputa[0]      )
      UpperCamelCase__       :     int 							= tokenizer.decode(outputa[0]      )
      UpperCamelCase__       :     Tuple 							= tokenizer.batch_decode(lowerCamelCase__      )

      UpperCamelCase__       :     List[str] 							= [
          '''武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である''',
          '''武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討''',
      ]
      self.assertListEqual(lowerCamelCase__				,						lowerCamelCase__      )
      self.assertListEqual(lowerCamelCase__				,						[outa_sentence, outa_sentence]      )


     @slow
     def   UpperCAmelCase__						(   self				:  Optional[Any]      )	->   Dict:





      '''simple docstring'''






      UpperCamelCase__       :     Any 							= GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
      UpperCamelCase__       :     Any 							= GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
      model.to(lowerCamelCase__      )

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

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

      UpperCamelCase__       :     Tuple 							= 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=20				,						generation_config=lowerCamelCase__				,						)

      UpperCamelCase__       :     List[Any] 							= 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=20				,						generation_config=lowerCamelCase__				,						)

      UpperCamelCase__       :     Dict 							= 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=20				,						generation_config=lowerCamelCase__				,						)

      UpperCamelCase__       :     Optional[Any] 							= tokenizer.decode(outputa[0]      )
      UpperCamelCase__       :     Optional[Any] 							= tokenizer.decode(outputa[0]      )
      UpperCamelCase__       :     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]      )






from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__UpperCamelCase							:							Any    							=					logging.get_logger(__name__)  # pylint: disable=invalid-name

class    __magic_name__						(  __lowerCAmelCase):
     A:		Dict								=   ["safety_checker", "feature_extractor"]


     def __init__(   self				:  List[str]				,						lowerCamelCase__				:  AutoencoderKL				,						lowerCamelCase__				:  CLIPTextModel				,						lowerCamelCase__				:  CLIPTokenizer				,						lowerCamelCase__				:  UNetaDConditionModel				,						lowerCamelCase__				:  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				,						lowerCamelCase__				:  StableDiffusionSafetyChecker				,						lowerCamelCase__				:  CLIPImageProcessor				,						lowerCamelCase__				:  bool = True				,						)	->   Optional[Any]:





      '''simple docstring'''






      super().__init__()
      if hasattr(scheduler.config				,						'''steps_offset'''      ) and scheduler.config.steps_offset != 1:
       UpperCamelCase__       :     int 							= (
           F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
           F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
           '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
           ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
           ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
           ''' file'''
       )
       deprecate('''steps_offset!=1'''				,						'''1.0.0'''				,						lowerCamelCase__				,						standard_warn=lowerCamelCase__      )
       UpperCamelCase__       :     int 							= dict(scheduler.config      )
       UpperCamelCase__       :     List[str] 							= 1
       UpperCamelCase__       :     List[str] 							= FrozenDict(lowerCamelCase__      )

      self.register_modules(
          vae=lowerCamelCase__				,						text_encoder=lowerCamelCase__				,						tokenizer=lowerCamelCase__				,						unet=lowerCamelCase__				,						scheduler=lowerCamelCase__				,						safety_checker=lowerCamelCase__				,						feature_extractor=lowerCamelCase__				,						)
      self.register_to_config(requires_safety_checker=lowerCamelCase__      )


     @property
     def   UpperCAmelCase__						(   self				:  str      )	->   Dict[str, Any]:





      '''simple docstring'''






      return {k: getattr(self				,						lowerCamelCase__      ) for k in self.config.keys() if not k.startswith('''_'''      )}


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  Optional[Union[str, int]] = "auto"      )	->   str:





      '''simple docstring'''






      if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       UpperCamelCase__       :     Dict 							= self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(lowerCamelCase__      )


     def   UpperCAmelCase__						(   self				:  int      )	->   Tuple:





      '''simple docstring'''






      self.enable_attention_slicing(lowerCamelCase__      )


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Optional[Any]				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  float = 0.8				,						lowerCamelCase__				:  Optional[int] = 50				,						lowerCamelCase__				:  Optional[float] = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  Optional[float] = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						)	->   Optional[Any]:





      '''simple docstring'''






      return StableDiffusionInpaintPipelineLegacy(**self.components      )(
          prompt=lowerCamelCase__				,						image=lowerCamelCase__				,						mask_image=lowerCamelCase__				,						strength=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						)


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Any				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  float = 0.8				,						lowerCamelCase__				:  Optional[int] = 50				,						lowerCamelCase__				:  Optional[float] = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  Optional[float] = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						**lowerCamelCase__				:  str				,						)	->   str:





      '''simple docstring'''






      return StableDiffusionImgaImgPipeline(**self.components      )(
          prompt=lowerCamelCase__				,						image=lowerCamelCase__				,						strength=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						callback_steps=lowerCamelCase__				,						)


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  int = 512				,						lowerCamelCase__				:  int = 512				,						lowerCamelCase__				:  int = 50				,						lowerCamelCase__				:  float = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  float = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[torch.FloatTensor] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						)	->   Optional[Any]:





      '''simple docstring'''






      return StableDiffusionPipeline(**self.components      )(
          prompt=lowerCamelCase__				,						height=lowerCamelCase__				,						width=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						latents=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						callback_steps=lowerCamelCase__				,						)


