

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


     def   UpperCAmelCase  (							self      :		List[Any] )							->     Union[str, Any]:
      return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese' )


     def   UpperCAmelCase  (							self      :		Dict )							->     int:
      __lowerCAmelCase:       Union[str, Any]            =				ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

      __lowerCAmelCase:       Optional[Any]            =				self.get_config()

      return (config, input_ids)


     def   UpperCAmelCase  (							self      :		str )							->     Optional[Any]:
      __lowerCAmelCase:       Dict            =				ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

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

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


     def   UpperCAmelCase  (							self      :		Any )							->     List[str]:
      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      :		Optional[Any]      ,  UpperCAmelCase      :		Optional[Any]      ,  UpperCAmelCase      :		Union[str, Any]      ,  )							->     Any:
      __lowerCAmelCase:       Union[str, Any]            =				GPTSanJapaneseForConditionalGeneration(config=UpperCAmelCase )
      model.to(UpperCAmelCase )
      model.eval()
      __lowerCAmelCase:       int            =				model(
          input_ids=UpperCAmelCase      ,  )
      self.parent.assertIsNotNone(UpperCAmelCase )







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


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		int )							->     Dict:
      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      :		Optional[Any] )							->     int:
      __lowerCAmelCase:       Any            =				GPTSanJapaneseTester(self )
      __lowerCAmelCase:       Any            =				ConfigTester(self      ,  config_class=UpperCAmelCase      ,  d_model=3_7 )


     def   UpperCAmelCase  (							self      :		Tuple )							->     Any:
      GPTSanJapaneseConfig()


     def   UpperCAmelCase  (							self      :		Optional[int] )							->     Optional[Any]:
      __lowerCAmelCase:       Dict            =				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      :		List[str] )							->     Any:
      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[str] )							->     Tuple:
      pass







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


     def   UpperCAmelCase  (							self      :		int )							->     int:
      __lowerCAmelCase:       Tuple            =				GPTSanJapaneseTester(self )
      __lowerCAmelCase:       str            =				ConfigTester(self      ,  config_class=UpperCAmelCase      ,  d_model=3_7 )


     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     Optional[int]:
      GPTSanJapaneseConfig()


     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     List[Any]:
      __lowerCAmelCase:       Tuple            =				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      :		int )							->     Optional[int]:
      super().test_model_parallelism()


     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
     def   UpperCAmelCase  (							self      :		str )							->     Optional[Any]:
      pass


     @slow
     def   UpperCAmelCase  (							self      :		Optional[int] )							->     Optional[Any]:
      __lowerCAmelCase:       List[str]            =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
      __lowerCAmelCase:       Optional[Any]            =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
      __lowerCAmelCase:       Union[str, Any]            =				tokenizer.encode('武田信玄は'      ,  return_tensors='pt' )
      __lowerCAmelCase:       Any            =				model(UpperCAmelCase )
      __lowerCAmelCase:       str            =				outputs.logits.detach().cpu().numpy()
      # Output of original model created with mesh-tensoflow
      __lowerCAmelCase:       int            =				[
          # fmt: off
          [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
          -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
          -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
          -12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
          [-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
          -7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
          -6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
          -8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
          [-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
          -11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
          -9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
          -10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
          [-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
          -9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
          -8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
          -11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
          [-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
          -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
          -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
          -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
          # fmt: on
      ]
      __lowerCAmelCase:       Dict            =				np.array(UpperCAmelCase ).flatten()
      __lowerCAmelCase:       Optional[Any]            =				output_logits[0, :, :2_0].flatten()

      def check(UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		Union[str, 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   UpperCAmelCase  (							self      :		Tuple )							->     List[str]:
      __lowerCAmelCase:       str            =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
      __lowerCAmelCase:       Any            =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
      model.to(UpperCAmelCase )

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

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

      __lowerCAmelCase:       Tuple            =				'left'
      __lowerCAmelCase:       Optional[Any]            =				tokenizer(UpperCAmelCase      ,  return_tensors='pt'      ,  padding=UpperCAmelCase )
      __lowerCAmelCase:       int            =				inputs['input_ids'].to(UpperCAmelCase )

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

      __lowerCAmelCase:       Optional[int]            =				model.generate(
          input_ids=UpperCAmelCase      ,  attention_mask=inputs['attention_mask'].to(UpperCAmelCase )      ,  max_new_tokens=3      ,  generation_config=UpperCAmelCase      ,  )

      __lowerCAmelCase:       List[Any]            =				tokenizer(sentences[0]      ,  return_tensors='pt' ).input_ids.to(UpperCAmelCase )
      __lowerCAmelCase:       List[Any]            =				model.generate(
          input_ids=UpperCAmelCase      ,  max_new_tokens=3      ,  generation_config=UpperCAmelCase )

      __lowerCAmelCase:       List[str]            =				tokenizer(sentences[1]      ,  return_tensors='pt' ).input_ids.to(UpperCAmelCase )
      __lowerCAmelCase:       int            =				model.generate(input_ids=UpperCAmelCase      ,  max_new_tokens=3      ,  generation_config=UpperCAmelCase )

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

      __lowerCAmelCase:       Dict            =				tokenizer.batch_decode(UpperCAmelCase      ,  skip_special_tokens=UpperCAmelCase )
      __lowerCAmelCase:       Optional[Any]            =				tokenizer.decode(output_non_padded[0]      ,  skip_special_tokens=UpperCAmelCase )
      __lowerCAmelCase:       List[Any]            =				tokenizer.decode(output_padded[0]      ,  skip_special_tokens=UpperCAmelCase )

      __lowerCAmelCase:       List[Any]            =				[
          '甲斐なら武田と言うほど甲斐の武田',
          '織田信長は、このような',
      ]
      self.assertListEqual(UpperCAmelCase      ,  UpperCAmelCase )
      self.assertListEqual(UpperCAmelCase      ,  [non_padded_sentence, padded_sentence] )


     @tooslow
     def   UpperCAmelCase  (							self      :		Any )							->     Tuple:
      __lowerCAmelCase:       Optional[Any]            =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
      __lowerCAmelCase:       Union[str, Any]            =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
      # Output of original model created with mesh-tensoflow
      __lowerCAmelCase:       int            =				[
          ('武田信玄は', 3_5_6_7_5),
          ('武田信玄は、', 4_5),
          ('武田信玄は、この', 2_9),
          ('武田信玄は、このよう', 3_0_6_4_2),
          ('武田信玄は、このような', 3_5_6_8_0),
          ('武田信玄は、このような「', 8_6_4_0),
          ('武田信玄は、このような「武田', 3_1_6_1_7),
          ('武田信玄は、このような「武田家', 3_0_6_4_6),
          ('武田信玄は、このような「武田家の', 3_1_6_1_7),
          ('武田信玄は、このような「武田家の家', 3_1_3_8_1),
      ]
      for input, output in target:
       __lowerCAmelCase:       Optional[int]            =				tokenizer.encode(UpperCAmelCase      ,  return_tensors='pt' )
       __lowerCAmelCase:       Dict            =				model(UpperCAmelCase )
       __lowerCAmelCase:       Tuple            =				outputs.logits.detach().cpu().numpy()[0]
       __lowerCAmelCase:       int            =				np.argmax(output_logits[-1] )
       self.assertEqual(UpperCAmelCase      ,  UpperCAmelCase )


     @slow
     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     str:
      __lowerCAmelCase:       Optional[int]            =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
      __lowerCAmelCase:       Union[str, Any]            =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
      model.to(UpperCAmelCase )

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

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

      # spout from uniform and one-hot
      __lowerCAmelCase:       str            =				[
          # fmt: off
          [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
          0.04803985, 0.211572  , 0.23188473, 0.37153068, 0.7836377 ,
          0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
          0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
          0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
          0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
          0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
          0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
          0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
          0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
          0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
          0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
          0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363  ,
          0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
          0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
          0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
          0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
          0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
          0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
          0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
          0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
          0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
          0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
          0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
          0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
          0.9552151 , 0.93901511, 0.22489288], # 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
      ]

      __lowerCAmelCase:       Optional[int]            =				model.generate(
          input_ids=UpperCAmelCase      ,  spout=spouts[0]      ,  max_new_tokens=2_0      ,  generation_config=UpperCAmelCase      ,  )

      __lowerCAmelCase:       Any            =				model.generate(
          input_ids=UpperCAmelCase      ,  spout=spouts[1]      ,  max_new_tokens=2_0      ,  generation_config=UpperCAmelCase      ,  )

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

      __lowerCAmelCase:       List[str]            =				tokenizer.decode(outputa[0] )
      __lowerCAmelCase:       List[Any]            =				tokenizer.decode(outputa[0] )
      __lowerCAmelCase:       Optional[int]            =				tokenizer.batch_decode(UpperCAmelCase )

      __lowerCAmelCase:       Dict            =				[
          '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      :		Optional[int] )							->     str:
      __lowerCAmelCase:       int            =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
      __lowerCAmelCase:       int            =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
      model.to(UpperCAmelCase )

      # set deterministically
      __lowerCAmelCase:       Union[str, Any]            =				GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese' )
      __lowerCAmelCase:       Dict            =				1

      __lowerCAmelCase:       Dict            =				'武田信玄'
      __lowerCAmelCase:       Tuple            =				'織田信長'
      __lowerCAmelCase:       Tuple            =				'は、'
      __lowerCAmelCase:       Optional[Any]            =				'が、'
      __lowerCAmelCase:       Dict            =				tokenizer(UpperCAmelCase      ,  prefix_text=UpperCAmelCase      ,  return_tensors='pt' )
      __lowerCAmelCase:       Tuple            =				tokenizer(UpperCAmelCase      ,  prefix_text=UpperCAmelCase      ,  return_tensors='pt' )
      __lowerCAmelCase:       Union[str, Any]            =				tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]      ,  return_tensors='pt' )

      __lowerCAmelCase:       Optional[Any]            =				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=2_0      ,  generation_config=UpperCAmelCase      ,  )

      __lowerCAmelCase:       int            =				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=2_0      ,  generation_config=UpperCAmelCase      ,  )

      __lowerCAmelCase:       Any            =				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=2_0      ,  generation_config=UpperCAmelCase      ,  )

      __lowerCAmelCase:       Optional[Any]            =				tokenizer.decode(outputa[0] )
      __lowerCAmelCase:       str            =				tokenizer.decode(outputa[0] )
      __lowerCAmelCase:       int            =				tokenizer.batch_decode(UpperCAmelCase )

      __lowerCAmelCase:       Dict            =				[
          '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] )








from __future__ import annotations







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]		)   ->   list[int]:

 """simple docstring"""






 if not list_of_ints:
  return []

 if min(SCREAMING_SNAKE_CASE		) < 0:
  raise ValueError('All numbers must be positive'		)

 __lowerCAmelCase:       Tuple            =				max(len(bin(SCREAMING_SNAKE_CASE		)[2:]		) for x in list_of_ints		)
 return _msd_radix_sort(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]   , SCREAMING_SNAKE_CASE		:      int		)   ->   list[int]:

 """simple docstring"""






 if bit_position == 0 or len(SCREAMING_SNAKE_CASE		) in [0, 1]:
  return list_of_ints

 __lowerCAmelCase:       List[str]            =				[]
 __lowerCAmelCase:       Any            =				[]
 # Split numbers based on bit at bit_position from the right
 for number in list_of_ints:
  if (number >> (bit_position - 1)) & 1:
   # number has a one at bit bit_position
   ones.append(SCREAMING_SNAKE_CASE		)
  else:
   # number has a zero at bit bit_position
   zeros.append(SCREAMING_SNAKE_CASE		)

    # recursively split both lists further
 __lowerCAmelCase:       Dict            =				_msd_radix_sort(SCREAMING_SNAKE_CASE   , bit_position - 1		)
 __lowerCAmelCase:       Union[str, Any]            =				_msd_radix_sort(SCREAMING_SNAKE_CASE   , bit_position - 1		)

 # recombine lists
 __lowerCAmelCase:       List[str]            =				zeros
 res.extend(SCREAMING_SNAKE_CASE		)

 return res







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]		)   ->   List[str]:

 """simple docstring"""






 __lowerCAmelCase:       Optional[int]            =				len(SCREAMING_SNAKE_CASE		)
 if not list_of_ints or length == 1:
  return

 if min(SCREAMING_SNAKE_CASE		) < 0:
  raise ValueError('All numbers must be positive'		)

 __lowerCAmelCase:       Optional[int]            =				max(len(bin(SCREAMING_SNAKE_CASE		)[2:]		) for x in list_of_ints		)
 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , 0   , SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int		)   ->   str:

 """simple docstring"""






 if bit_position == 0 or end_index - begin_index <= 1:
  return

 bit_position -= 1

 __lowerCAmelCase:       Optional[int]            =				begin_index
 __lowerCAmelCase:       Optional[Any]            =				end_index - 1
 while i <= j:
  __lowerCAmelCase:       Any            =				False
  if not (list_of_ints[i] >> bit_position) & 1:
   # found zero at the beginning
   i += 1
   __lowerCAmelCase:       Optional[Any]            =				True
  if (list_of_ints[j] >> bit_position) & 1:
   # found one at the end
   j -= 1
   __lowerCAmelCase:       List[str]            =				True

  if changed:
   continue

  __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				list_of_ints[j], list_of_ints[i]
  j -= 1
  if j != i:
   i += 1

 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





