





'''simple docstring'''






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      SCREAMING_SNAKE_CASE__      :




    def __init__( self    ,					lowercase__    ,					lowercase__=3_6000    ,					lowercase__=13    ,					lowercase__=7    ,					lowercase__=True    ,					lowercase__=32    ,					lowercase__=42    ,					lowercase__=5    ,					lowercase__=2    ,					lowercase__=4    ,					lowercase__=2    ,					lowercase__=32    ,					lowercase__=80    ,					lowercase__=33    ,					lowercase__=0.0    ,					lowercase__=1e-6    ,					lowercase__=100    ,					lowercase__=0.0    ,					):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	self.get_config()

          return (config, input_ids)




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

          SCREAMING_SNAKE_CASE_				:      List[str]					=	self.get_config()

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




    def   __lowerCamelCase				( 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   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	GPTSanJapaneseForConditionalGeneration(config=lowercase__      )
          model.to(lowercase__      )
          model.eval()
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	model(
              input_ids=lowercase__    ,					)
          self.parent.assertIsNotNone(lowercase__      )






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




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__      ):






          """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   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	GPTSanJapaneseTester(self      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	ConfigTester(self    ,					config_class=lowercase__    ,					d_model=37      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          GPTSanJapaneseConfig()




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase__      )




    @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   __lowerCamelCase				( 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   __lowerCamelCase				( self      ):






          """simple docstring"""






          pass






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	GPTSanJapaneseTester(self      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	ConfigTester(self    ,					config_class=lowercase__    ,					d_model=37      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          GPTSanJapaneseConfig()




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase__      )




    @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   __lowerCamelCase				( 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   __lowerCamelCase				( self      ):






          """simple docstring"""






          pass




    @slow
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

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

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




    @slow
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"      )
          SCREAMING_SNAKE_CASE_				:      Tuple					=	GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"      )
          model.to(lowercase__      )

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

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

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	"left"
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	tokenizer(lowercase__    ,					return_tensors="pt"    ,					padding=lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	inputs["input_ids"].to(lowercase__      )

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

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

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

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	tokenizer(sentences[1]    ,					return_tensors="pt"      ).input_ids.to(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	model.generate(input_ids=lowercase__    ,					max_new_tokens=3    ,					generation_config=lowercase__      )

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

          SCREAMING_SNAKE_CASE_				:      Dict					=	tokenizer.batch_decode(lowercase__    ,					skip_special_tokens=lowercase__      )
          SCREAMING_SNAKE_CASE_				:      str					=	tokenizer.decode(output_non_padded[0]    ,					skip_special_tokens=lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Any					=	tokenizer.decode(output_padded[0]    ,					skip_special_tokens=lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Union[str, 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(lowercase__    ,					lowercase__      )
          self.assertListEqual(lowercase__    ,					[non_padded_sentence, padded_sentence]      )




    @tooslow
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    @slow
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"      )
          SCREAMING_SNAKE_CASE_				:      Tuple					=	GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"      )
          model.to(lowercase__      )

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

          SCREAMING_SNAKE_CASE_				:      Tuple					=	"武田信玄は、"
          SCREAMING_SNAKE_CASE_				:      int					=	tokenizer(lowercase__    ,					return_tensors="pt"      ).input_ids.to(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Any					=	tokenizer([input_text, input_text]    ,					return_tensors="pt"      ).input_ids.to(lowercase__      )

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

          SCREAMING_SNAKE_CASE_				:      int					=	model.generate(
              input_ids=lowercase__    ,					spout=spouts[0]    ,					max_new_tokens=20    ,					generation_config=lowercase__    ,					)

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

          SCREAMING_SNAKE_CASE_				:      Dict					=	model.generate(
              input_ids=lowercase__    ,					spout=lowercase__    ,					max_new_tokens=20    ,					generation_config=lowercase__    ,					)

          SCREAMING_SNAKE_CASE_				:      str					=	tokenizer.decode(outputa[0]      )
          SCREAMING_SNAKE_CASE_				:      Any					=	tokenizer.decode(outputa[0]      )
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	tokenizer.batch_decode(lowercase__      )

          SCREAMING_SNAKE_CASE_				:      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(lowercase__    ,					lowercase__      )
          self.assertListEqual(lowercase__    ,					[outa_sentence, outa_sentence]      )







    @slow
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"      )
          model.to(lowercase__      )

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

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

          SCREAMING_SNAKE_CASE_				:      Any					=	model.generate(
              input_ids=input_tok_a.input_ids.to(lowercase__      )    ,					token_type_ids=input_tok_a.token_type_ids.to(lowercase__      )    ,					max_new_tokens=20    ,					generation_config=lowercase__    ,					)

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

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	model.generate(
              input_ids=input_tok_a.input_ids.to(lowercase__      )    ,					token_type_ids=input_tok_a.token_type_ids.to(lowercase__      )    ,					attention_mask=input_tok_a.attention_mask.to(lowercase__      )    ,					max_new_tokens=20    ,					generation_config=lowercase__    ,					)

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

          SCREAMING_SNAKE_CASE_				:      Tuple					=	[
              "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(lowercase__    ,					lowercase__      )
          self.assertListEqual(lowercase__    ,					[outa_sentence, outa_sentence]      )




'''simple docstring'''






def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[int]  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       List[str]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	(boundary[1] - boundary[0]) / steps
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	boundary[0]
      SCREAMING_SNAKE_CASE_				:      int					=	boundary[1]
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	make_points(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      Dict					=	0.0
      y += (h / 3.0) * f(SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      Tuple					=	2
      for i in x_i:
            y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE_   )
            cnt += 1
      y += (h / 3.0) * f(SCREAMING_SNAKE_CASE_   )
      return y


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[Any]  ,  SCREAMING_SNAKE_CASE_					:     List[Any]  ,  SCREAMING_SNAKE_CASE_					:     Optional[Any]   )							->       List[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	a + h
      while x < (b - h):
            yield x
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	x + h


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     str   )							->       List[Any]:  # enter your function here
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	(x - 0) * (x - 0)
      return y


def  __lowerCamelCase      (				)							->       int:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	0.0  # Lower bound of integration
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	1.0  # Upper bound of integration
      SCREAMING_SNAKE_CASE_				:      str					=	10.0  # define number of steps or resolution
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	[a, b]  # define boundary of integration
      SCREAMING_SNAKE_CASE_				:      List[str]					=	method_a(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_   )
      print(F"y = {y}"   )


if __name__ == "__main__":
      main()

