

def       UpperCAmelCase__    (      _A				:			float		):





  '''simple docstring'''







  return int(_A		) if x - int(_A		) <= 0 else int(_A		) + 1


if __name__ == "__main__":
       import doctest

       doctest.testmod()



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__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_,   lowercase_=36000,   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,   )							->   Optional[int]:






     """simple docstring"""






     a__  =vocab_size
     a__  =parent
     a__  =batch_size
     a__  =num_contexts
     # For common tests
     a__  =self.num_contexts
     a__  =is_training
     a__  =hidden_size
     a__  =num_ext_layers
     a__  =ext_size
     a__  =num_hidden_layers
     a__  =num_attention_heads
     a__  =num_experts
     a__  =d_ff
     a__  =d_ext
     a__  =d_spout
     a__  =dropout_rate
     a__  =layer_norm_epsilon
     a__  =expert_capacity
     a__  =router_jitter_noise



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     a__  =ids_tensor([self.batch_size, self.seq_length],   self.vocab_size	)

     a__  =self.get_config()

     return (config, input_ids)



   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     a__  =ids_tensor([self.batch_size, self.seq_length],   self.vocab_size	)

     a__  =self.get_config()

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



   def 			_UpperCAmelCase			(    self	)							->   str:






     """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,   lowercase_,   lowercase_,   )							->   Tuple:






     """simple docstring"""






     a__  =GPTSanJapaneseForConditionalGeneration(config=lowercase_	)
     model.to(lowercase_	)
     model.eval()
     a__  =model(
         input_ids=lowercase_,   )
     self.parent.assertIsNotNone(lowercase_	)







@require_torch
class 						__magic_name__      (							lowerCamelCase__		,	lowerCamelCase__		,	unittest.TestCase							):

   '''simple docstring'''




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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   Tuple:






     """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	)							->   Union[str, Any]:






     """simple docstring"""






     a__  =GPTSanJapaneseTester(self	)
     a__  =ConfigTester(self,   config_class=lowercase_,   d_model=37	)



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     GPTSanJapaneseConfig()



   def 			_UpperCAmelCase			(    self	)							->   str:






     """simple docstring"""






     a__  =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 			_UpperCAmelCase			(    self	)							->   int:






     """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]:






     """simple docstring"""






     pass







@require_torch
class 						__magic_name__      (							lowerCamelCase__		,	lowerCamelCase__		,	unittest.TestCase							):

   '''simple docstring'''




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



   def 			_UpperCAmelCase			(    self	)							->   int:






     """simple docstring"""






     a__  =GPTSanJapaneseTester(self	)
     a__  =ConfigTester(self,   config_class=lowercase_,   d_model=37	)



   def 			_UpperCAmelCase			(    self	)							->   Dict:






     """simple docstring"""






     GPTSanJapaneseConfig()



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     a__  =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 			_UpperCAmelCase			(    self	)							->   Dict:






     """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	)							->   Optional[int]:






     """simple docstring"""






     pass



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






     """simple docstring"""






     a__  =GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     a__  =GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     a__  =tokenizer.encode('''武田信玄は''',   return_tensors='''pt'''	)
     a__  =model(lowercase_	)
     a__  =outputs.logits.detach().cpu().numpy()
     # Output of original model created with mesh-tensoflow
     a__  =[
         # 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
     ]
     a__  =np.array(lowercase_	).flatten()
     a__  =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 			_UpperCAmelCase			(    self	)							->   Optional[int]:






     """simple docstring"""






     a__  =GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     a__  =GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     model.to(lowercase_	)

     # set deterministically
     a__  =GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     a__  =1

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

     a__  ='''left'''
     a__  =tokenizer(lowercase_,   return_tensors='''pt''',   padding=lowercase_	)
     a__  =inputs['''input_ids'''].to(lowercase_	)

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

     a__  =model.generate(
         input_ids=lowercase_,   attention_mask=inputs['''attention_mask'''].to(lowercase_	),   max_new_tokens=3,   generation_config=lowercase_,   )

     a__  =tokenizer(sentences[0],   return_tensors='''pt'''	).input_ids.to(lowercase_	)
     a__  =model.generate(
         input_ids=lowercase_,   max_new_tokens=3,   generation_config=lowercase_	)

     a__  =tokenizer(sentences[1],   return_tensors='''pt'''	).input_ids.to(lowercase_	)
     a__  =model.generate(input_ids=lowercase_,   max_new_tokens=3,   generation_config=lowercase_	)

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

     a__  =tokenizer.batch_decode(lowercase_,   skip_special_tokens=lowercase_	)
     a__  =tokenizer.decode(output_non_padded[0],   skip_special_tokens=lowercase_	)
     a__  =tokenizer.decode(output_padded[0],   skip_special_tokens=lowercase_	)

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



   @tooslow
   def 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






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



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






     """simple docstring"""






     a__  =GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     a__  =GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     model.to(lowercase_	)

     # set deterministically
     a__  =GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     a__  =1

     a__  ='''武田信玄は、'''
     a__  =tokenizer(lowercase_,   return_tensors='''pt'''	).input_ids.to(lowercase_	)
     a__  =tokenizer([input_text, input_text],   return_tensors='''pt'''	).input_ids.to(lowercase_	)

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

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

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

     a__  =model.generate(
         input_ids=lowercase_,   spout=lowercase_,   max_new_tokens=20,   generation_config=lowercase_,   )

     a__  =tokenizer.decode(outputa[0]	)
     a__  =tokenizer.decode(outputa[0]	)
     a__  =tokenizer.batch_decode(lowercase_	)

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



   @slow
   def 			_UpperCAmelCase			(    self	)							->   str:






     """simple docstring"""






     a__  =GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     a__  =GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     model.to(lowercase_	)

     # set deterministically
     a__  =GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     a__  =1

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

     a__  =model.generate(
         input_ids=input_tok_a.input_ids.to(lowercase_	),   token_type_ids=input_tok_a.token_type_ids.to(lowercase_	),   max_new_tokens=20,   generation_config=lowercase_,   )

     a__  =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_,   )

     a__  =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_,   )

     a__  =tokenizer.decode(outputa[0]	)
     a__  =tokenizer.decode(outputa[0]	)
     a__  =tokenizer.batch_decode(lowercase_	)

     a__  =[
         '''武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して''',
         '''織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残''',
     ]
     self.assertListEqual(lowercase_,   lowercase_	)
     self.assertListEqual(lowercase_,   [outa_sentence, outa_sentence]	)
