





"""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	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=3_60_00	,SCREAMING_SNAKE_CASE__=13	,SCREAMING_SNAKE_CASE__=7	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=32	,SCREAMING_SNAKE_CASE__=42	,SCREAMING_SNAKE_CASE__=5	,SCREAMING_SNAKE_CASE__=2	,SCREAMING_SNAKE_CASE__=4	,SCREAMING_SNAKE_CASE__=2	,SCREAMING_SNAKE_CASE__=32	,SCREAMING_SNAKE_CASE__=80	,SCREAMING_SNAKE_CASE__=33	,SCREAMING_SNAKE_CASE__=0.0	,SCREAMING_SNAKE_CASE__=1E-6	,SCREAMING_SNAKE_CASE__=1_00	,SCREAMING_SNAKE_CASE__=0.0	,)    ->   int:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self							)    ->   Any:





             """simple docstring"""






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




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





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE     :int	       =						self.get_config()

             return (config, input_ids)




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE     :Tuple	       =						self.get_config()

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




       def 					_UpperCamelCase  (		self							)    ->   int:





             """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	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						GPTSanJapaneseForConditionalGeneration(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						model(
                 input_ids=SCREAMING_SNAKE_CASE__	,)
             self.parent.assertIsNotNone(SCREAMING_SNAKE_CASE__							)






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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   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							)    ->   Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :int	       =						GPTSanJapaneseTester(self							)
             __SCREAMING_SNAKE_CASE     :Tuple	       =						ConfigTester(self	,config_class=SCREAMING_SNAKE_CASE__	,d_model=37							)




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             GPTSanJapaneseConfig()




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						GPTSanJapaneseTester(self							)
             __SCREAMING_SNAKE_CASE     :str	       =						ConfigTester(self	,config_class=SCREAMING_SNAKE_CASE__	,d_model=37							)




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             GPTSanJapaneseConfig()




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




       @unittest.skip(
           reason='''skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'''							)
       def 					_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							)    ->   str:





             """simple docstring"""






             pass




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''							)
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''							)
             __SCREAMING_SNAKE_CASE     :Any	       =						tokenizer.encode('''武田信玄は'''	,return_tensors='''pt'''							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						outputs.logits.detach().cpu().numpy()
             # Output of original model created with mesh-tensoflow
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						[
                 # fmt: off
                 [-1_2.0_3_7_8_3_9_8_8_9_5_2_6_3_6_7, -1_2.4_3_3_0_6_1_5_9_9_7_3_1_4_4_5, -1_4.3_3_3_8_4_0_3_7_0_1_7_8_2_2_3, -1_2.4_5_0_3_4_5_9_9_3_0_4_1_9_9_2, -1_1.1_6_6_1_3_7_6_9_5_3_1_2_5,
                 -1_1.9_3_0_1_3_7_6_3_4_2_7_7_3_4_4, -1_0.6_5_9_7_4_0_4_4_7_9_9_8_0_4_7, -1_2.9_0_9_5_7_4_5_0_8_6_6_6_9_9_2, -1_3.2_4_1_0_4_3_0_9_0_8_2_0_3_1_2, -1_3.3_9_8_5_7_9_5_9_7_4_7_3_1_4_5,
                 -1_1.1_0_7_5_2_4_8_7_1_8_2_6_1_7_2, -1_2.3_6_8_5_9_4_1_6_9_6_1_6_7, -2_2.9_7_9_4_3_1_1_5_2_3_4_3_7_5, -1_0.4_8_1_0_6_7_6_5_7_4_7_0_7_0_3, -1_2.4_8_4_0_3_0_7_2_3_5_7_1_7_7_7,
                 -1_2.8_0_7_3_6_0_6_4_9_1_0_8_8_8_7, -1_4.7_6_9_7_0_0_0_5_0_3_5_4_0_0_4, -1_2.2_3_3_5_7_9_6_3_5_6_2_0_1_1_7, -1_3.4_2_8_1_4_5_4_0_8_6_3_0_3_7_1, -2_2.6_2_4_1_7_7_9_3_2_7_3_9_2_5_8],
                 [-7.5_1_1_1_4_9_8_8_3_2_7_0_2_6_4, -8.2_8_1_8_5_1_7_6_8_4_9_3_6_5_2, -7.9_4_3_1_2_7_1_5_5_3_0_3_9_5_5, -7.5_5_0_2_1_3_3_3_6_9_4_4_5_8, -6.4_9_8_6_9_9_6_6_5_0_6_9_5_8,
                 -7.5_8_6_7_9_6_2_8_3_7_2_1_9_2_4, -6.9_7_8_0_8_5_9_9_4_7_2_0_4_5_9, -7.8_3_9_1_4_5_1_8_3_5_6_3_2_3_2, -8.2_1_9_6_4_1_6_8_5_4_8_5_8_4, -8.6_9_5_0_9_1_2_4_7_5_5_8_5_9_4,
                 -6.7_0_6_9_1_0_6_1_0_1_9_8_9_7_5, -6.6_5_8_5_7_9_8_2_6_3_5_4_9_8_0_5, -1_9.5_6_5_6_9_8_6_2_3_6_5_7_2_2_7, -5.3_5_3_8_4_2_7_3_5_2_9_0_5_2_7, -8.3_5_0_6_8_6_0_7_3_3_0_3_2_2_3,
                 -8.0_3_9_3_8_8_6_5_6_6_1_6_2_1_1, -1_0.8_5_6_5_6_9_2_9_0_1_6_1_1_3_3, -7.7_5_1_5_4_4_4_7_5_5_5_5_4_2, -8.8_1_9_0_2_2_1_7_8_6_4_9_9_0_2, -1_9.5_1_5_3_2_7_4_5_3_6_1_3_2_8],
                 [-9.7_3_0_6_6_2_3_4_5_8_8_6_2_3, -1_0.2_2_3_9_2_2_7_2_9_4_9_2_1_8_8, -9.9_3_2_9_8_1_4_9_1_0_8_8_8_6_7, -1_1.8_5_7_8_3_6_7_2_3_3_2_7_6_3_7, -7.6_6_2_6_2_6_2_6_6_4_7_9_4_9_2,
                 -1_1.1_3_5_2_9_1_0_9_9_5_4_8_3_4, -7.7_6_5_0_9_7_6_1_8_1_0_3_0_2_7, -1_1.4_7_2_9_2_3_2_7_8_8_0_8_5_9_4, -9.5_4_3_1_4_9_9_4_8_1_2_0_1_1_7, -1_1.9_0_5_6_3_3_9_2_6_3_9_1_6_0_2,
                 -9.3_6_6_1_6_4_2_0_7_4_5_8_4_9_6, -1_1.5_7_3_4_2_8_1_5_3_9_9_1_7, -2_3.6_9_9_0_0_3_2_1_9_6_0_4_4_9_2, -9.4_2_9_5_9_0_2_2_5_2_1_9_7_2_7, -1_0.4_2_8_3_9_2_4_1_0_2_7_8_3_2,
                 -1_0.5_8_5_2_4_0_3_6_4_0_7_4_7_0_7, -1_0.9_4_7_7_1_9_5_7_3_9_7_4_6_1, -1_1.0_9_5_4_1_6_0_6_9_0_3_0_7_6_2, -1_0.3_9_0_2_4_0_6_6_9_2_5_0_4_8_8, -2_3.7_6_9_3_7_2_9_4_0_0_6_3_4_7_7],
                 [-9.7_2_8_2_6_5_7_6_2_3_2_9_1_0_2, -9.8_5_9_7_1_2_6_0_0_7_0_8_0_0_8, -1_0.0_9_7_2_9_2_9_0_0_0_8_5_4_5, -9.6_7_8_5_2_2_1_0_9_9_8_5_3_5_2, -6.8_7_9_5_1_9_9_3_9_4_2_2_6_0_7,
                 -9.6_8_4_8_7_5_4_8_8_2_8_1_2_5, -4.2_8_0_3_4_2_5_7_8_8_8_7_9_3_9_5, -1_0.0_1_8_9_1_4_2_2_2_7_1_7_2_8_5, -9.3_0_8_4_4_5_9_3_0_4_8_0_9_5_7, -1_0.6_3_3_9_4_5_4_6_5_0_8_7_8_9,
                 -8.0_8_3_6_4_6_7_7_4_2_9_1_9_9_2, -9.0_6_3_0_1_4_9_8_4_1_3_0_8_6, -2_1.9_0_4_2_6_6_3_5_7_4_2_1_8_7_5, -8.9_0_1_6_0_8_4_6_7_1_0_2_0_5, -8.8_4_1_8_7_6_0_2_9_9_6_8_2_6_2,
                 -1_1.8_5_6_7_1_9_9_7_0_7_0_3_1_2_5, -1_2.0_7_9_3_9_8_1_5_5_2_1_2_4_0_2, -1_1.2_3_3_7_5_3_2_0_4_3_4_5_7_0_3, -1_0.1_7_7_3_3_8_6_0_0_1_5_8_6_9_1, -2_1.8_7_2_5_6_6_2_2_3_1_4_4_5_3],
                 [-9.6_6_9_7_6_4_5_1_8_7_3_7_7_9_3, -9.6_1_4_1_9_8_6_8_4_6_9_2_3_8_3, -9.8_1_4_5_1_0_3_4_5_4_5_8_9_8_4, -9.9_9_6_5_0_1_9_2_2_6_0_7_4_2_2, -1_1.3_7_5_6_9_0_4_6_0_2_0_5_0_7_8,
                 -1_0.1_1_3_4_0_5_2_2_7_6_6_1_1_3_3, -1_0.5_4_6_8_6_7_3_7_0_6_0_5_4_6_9, -1_0.0_4_3_6_9_0_6_8_1_4_5_7_5_2, -1_0.9_0_7_8_0_9_2_5_7_5_0_7_3_2_4, -1_0.5_0_4_2_1_6_1_9_4_1_5_2_8_3_2,
                 -1_1.1_2_9_1_9_9_0_2_8_0_1_5_1_3_7, -1_0.1_5_1_1_2_4_0_0_0_5_4_9_3_1_6, -2_1.9_6_5_8_6_7_9_9_6_2_1_5_8_2, -9.0_8_6_3_4_9_4_8_7_3_0_4_6_8_8, -1_1.7_3_0_3_3_9_0_5_0_2_9_2_9_6_9,
                 -1_0.4_6_0_6_6_7_6_1_0_1_6_8_4_5_7, -1_0.2_9_8_0_4_9_9_2_6_7_5_7_8_1_2, -1_0.7_8_4_1_4_8_2_1_6_2_4_7_5_5_9, -1_0.8_4_0_6_9_3_4_7_3_8_1_5_9_1_8, -2_2.0_3_1_5_2_8_4_7_2_9_0_0_3_9],
                 # fmt: on
             ]
             __SCREAMING_SNAKE_CASE     :Tuple	       =						np.array(SCREAMING_SNAKE_CASE__							).flatten()
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						output_logits[0, :, :20].flatten()

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

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




       @slow
       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''							)
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''							)
             model.to(SCREAMING_SNAKE_CASE__							)

             # set deterministically
             __SCREAMING_SNAKE_CASE     :Tuple	       =						GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''							)
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						1

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

             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						'''left'''
             __SCREAMING_SNAKE_CASE     :List[str]	       =						tokenizer(SCREAMING_SNAKE_CASE__	,return_tensors='''pt'''	,padding=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						inputs['''input_ids'''].to(SCREAMING_SNAKE_CASE__							)

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

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

             __SCREAMING_SNAKE_CASE     :List[str]	       =						tokenizer(sentences[0]	,return_tensors='''pt'''							).input_ids.to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						model.generate(
                 input_ids=SCREAMING_SNAKE_CASE__	,max_new_tokens=3	,generation_config=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :Any	       =						tokenizer(sentences[1]	,return_tensors='''pt'''							).input_ids.to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						model.generate(input_ids=SCREAMING_SNAKE_CASE__	,max_new_tokens=3	,generation_config=SCREAMING_SNAKE_CASE__							)

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

             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						tokenizer.batch_decode(SCREAMING_SNAKE_CASE__	,skip_special_tokens=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						tokenizer.decode(output_non_padded[0]	,skip_special_tokens=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						tokenizer.decode(output_padded[0]	,skip_special_tokens=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :int	       =						[
                 '''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(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
             self.assertListEqual(SCREAMING_SNAKE_CASE__	,[non_padded_sentence, padded_sentence]							)




       @tooslow
       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						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_56_75),
                 ('''武田信玄は、''', 45),
                 ('''武田信玄は、この''', 29),
                 ('''武田信玄は、このよう''', 3_06_42),
                 ('''武田信玄は、このような''', 3_56_80),
                 ('''武田信玄は、このような「''', 86_40),
                 ('''武田信玄は、このような「武田''', 3_16_17),
                 ('''武田信玄は、このような「武田家''', 3_06_46),
                 ('''武田信玄は、このような「武田家の''', 3_16_17),
                 ('''武田信玄は、このような「武田家の家''', 3_13_81),
             ]
             for input, output in target:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						tokenizer.encode(SCREAMING_SNAKE_CASE__	,return_tensors='''pt'''							)
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						model(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						outputs.logits.detach().cpu().numpy()[0]
                   __SCREAMING_SNAKE_CASE     :int	       =						np.argmax(output_logits[-1]							)
                   self.assertEqual(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)




       @slow
       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''							)
             model.to(SCREAMING_SNAKE_CASE__							)

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

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

             # spout from uniform and one-hot
             __SCREAMING_SNAKE_CASE     :int	       =						[
                 # fmt: off
                 [0.8_7_8_8_2_2_0_8, 0.3_8_4_2_6_3_9_6, 0.3_3_2_2_0_2_4_8, 0.4_3_8_9_0_4_0_6, 0.1_6_5_6_2_2_5_2,
                 0.0_4_8_0_3_9_8_5, 0.2_1_1_5_7_2  , 0.2_3_1_8_8_4_7_3, 0.3_7_1_5_3_0_6_8, 0.7_8_3_6_3_7_7 ,
                 0.0_2_1_6_0_1_7_2, 0.3_8_7_6_1_7_1_9, 0.7_5_2_9_0_7_7_2, 0.9_0_1_9_8_8_5_7, 0.3_4_3_6_5_7_7_7,
                 0.6_4_1_6_8_1_6_9, 0.4_4_3_1_8_4_7_1, 0.1_4_5_7_5_7_4_6, 0.9_2_5_6_2_8_8_1, 0.4_0_8_1_2_1_4_8,
                 0.2_9_0_1_9_1_2_2, 0.8_8_8_6_1_5_9_9, 0.6_5_5_2_4_8_4_6, 0.4_3_5_6_3_4_5_6, 0.3_8_1_7_7_1_8_7,
                 0.7_0_8_3_2_9_6_5, 0.8_1_5_2_7_8_9_2, 0.6_8_8_3_2_8_1_2, 0.3_8_8_3_3_1_9_2, 0.4_5_6_1_5_2_2 ,
                 0.1_4_8_2_8_8_1_7, 0.4_7_2_4_8_2_1_3, 0.5_4_3_5_7_3_3_5, 0.8_2_0_0_9_5_6_6, 0.1_3_3_8_8_8_4 ,
                 0.0_2_7_5_5_4_1_7, 0.1_9_7_6_4_6_7_7, 0.2_4_2_2_0_8_4 , 0.0_4_7_5_7_6_7_4, 0.6_5_4_0_9_6_0_6,
                 0.0_8_2_4_5_8_9 , 0.0_3_3_0_4_3_8_3, 0.9_4_3_8_7_6_8_9, 0.9_8_7_6_4_5_0_9, 0.8_2_4_3_3_9_0_1,
                 0.2_7_6_4_6_7_4_1, 0.6_4_9_0_7_4_9_3, 0.7_6_0_0_9_4_0_6, 0.3_0_0_8_7_9_1_5, 0.1_7_9_0_4_6_8_9,
                 0.4_1_6_0_1_7_1_4, 0.6_7_0_4_6_3_9_8, 0.1_0_4_2_2_8_2_2, 0.0_8_4_4_7_3_7_4, 0.0_7_3_5_4_3_4_4,
                 0.6_1_4_2_3_5_6_5, 0.7_0_2_8_4_8_6_6, 0.7_5_3_2_3_3_3 , 0.1_9_7_2_0_3_8 , 0.2_9_5_7_5_6_5_9,
                 0.9_0_5_8_3_8_8_6, 0.2_9_2_6_5_3_0_7, 0.5_0_0_0_0_1_7_5, 0.7_0_4_0_7_6_5_5, 0.8_8_9_3_6_3  ,
                 0.8_1_9_0_4_4_1_8, 0.6_6_8_2_9_1_2_8, 0.6_4_4_6_8_8_1_5, 0.5_6_5_6_3_7_2_3, 0.8_5_6_0_1_8_7_5,
                 0.9_4_9_2_4_6_7_2, 0.0_0_1_6_6_7_6_2, 0.2_5_2_2_0_6_4_3, 0.7_4_5_4_0_2_1_9, 0.6_7_9_9_3_2_4_7,
                 0.1_5_4_9_6_7_5 , 0.3_9_3_8_5_3_5_2, 0.9_2_1_5_3_6_0_7, 0.6_3_7_4_5_9_3_1, 0.2_7_7_5_9_0_4_3,
                 0.8_4_7_0_2_2_9_5, 0.6_5_9_0_4_2_7_1, 0.5_8_6_7_6_6_1_4, 0.8_6_6_6_9_3_6 , 0.3_9_6_0_7_4_3_8,
                 0.7_9_9_5_4_9_8_3, 0.4_2_2_2_0_6_9_7, 0.3_9_6_5_0_3_8_1, 0.7_8_4_9_8_6_4 , 0.5_6_1_5_0_2_0_1,
                 0.1_5_6_7_8_9_2_5, 0.1_4_7_4_6_0_3_2, 0.3_4_5_4_2_1_1_4, 0.4_7_0_2_6_7_8_3, 0.1_1_9_5_6_4_8_9,
                 0.2_5_4_2_1_4_3_5, 0.3_3_7_8_8_9_0_1, 0.6_8_9_3_4_8_4_2, 0.3_6_4_2_4_6_8_5, 0.7_1_7_3_7_8_9_8,
                 0.3_8_9_8_3_4_4_9, 0.9_4_3_9_3_7_7_9, 0.3_9_5_7_5_5_8_8, 0.3_6_6_1_6_5_5_3, 0.8_7_1_0_4_6_6_5,
                 0.6_4_6_3_0_2_0_3, 0.2_2_5_1_6_9_0_5, 0.8_8_2_7_0_8_0_4, 0.1_5_0_3_1_3_3_8, 0.7_5_1_4_4_3_4_5,
                 0.4_6_4_5_9_0_2_5, 0.8_5_3_9_6_4_5_4, 0.8_6_3_5_5_6_4_3, 0.6_5_1_3_9_8_5_1, 0.7_0_2_6_6_0_6_1,
                 0.3_0_2_4_1_3_8_9, 0.8_1_0_5_6_4_9_7, 0.8_8_8_6_5_9_6_9, 0.3_8_7_7_3_8_0_7, 0.7_0_6_3_5_8_4_9,
                 0.9_0_7_1_8_4_5_9, 0.4_3_2_4_5_7_8_9, 0.2_8_0_0_0_6_5_4, 0.4_5_9_3_5_5_6_2, 0.0_8_7_7_3_5_1_9,
                 0.9_5_5_2_1_5_1 , 0.9_3_9_0_1_5_1_1, 0.2_2_4_8_9_2_8_8], # 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     :Union[str, Any]	       =						model.generate(
                 input_ids=SCREAMING_SNAKE_CASE__	,spout=spouts[0]	,max_new_tokens=20	,generation_config=SCREAMING_SNAKE_CASE__	,)

             __SCREAMING_SNAKE_CASE     :str	       =						model.generate(
                 input_ids=SCREAMING_SNAKE_CASE__	,spout=spouts[1]	,max_new_tokens=20	,generation_config=SCREAMING_SNAKE_CASE__	,)

             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						model.generate(
                 input_ids=SCREAMING_SNAKE_CASE__	,spout=SCREAMING_SNAKE_CASE__	,max_new_tokens=20	,generation_config=SCREAMING_SNAKE_CASE__	,)

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

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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''							)
             model.to(SCREAMING_SNAKE_CASE__							)

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

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

             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model.generate(
                 input_ids=input_tok_a.input_ids.to(SCREAMING_SNAKE_CASE__							)	,token_type_ids=input_tok_a.token_type_ids.to(SCREAMING_SNAKE_CASE__							)	,max_new_tokens=20	,generation_config=SCREAMING_SNAKE_CASE__	,)

             __SCREAMING_SNAKE_CASE     :int	       =						model.generate(
                 input_ids=input_tok_a.input_ids.to(SCREAMING_SNAKE_CASE__							)	,token_type_ids=input_tok_a.token_type_ids.to(SCREAMING_SNAKE_CASE__							)	,max_new_tokens=20	,generation_config=SCREAMING_SNAKE_CASE__	,)

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

             __SCREAMING_SNAKE_CASE     :Dict	       =						tokenizer.decode(outputa[0]							)
             __SCREAMING_SNAKE_CASE     :Any	       =						tokenizer.decode(outputa[0]							)
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :str	       =						[
                 '''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(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
             self.assertListEqual(SCREAMING_SNAKE_CASE__	,[outa_sentence, outa_sentence]							)






"""simple docstring"""
def   __lowerCamelCase     (							a_  :	int = 10**6							)    ->   int:

      if n <= 0:
            raise ValueError('''Please enter an integer greater than 0'''							)

      __SCREAMING_SNAKE_CASE     :Tuple	       =						list(range(n + 1							)							)
      for number in range(2				,     n + 1							):
            if phi[number] == number:
                  phi[number] -= 1
                  for multiple in range(number * 2				,     n + 1				,     a_							):
                        __SCREAMING_SNAKE_CASE     :List[Any]	       =						(phi[multiple] // number) * (number - 1)

      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						1
      for number in range(1				,     n + 1							):
            if (answer / phi[answer]) < (number / phi[number]):
                  __SCREAMING_SNAKE_CASE     :str	       =						number

      return answer


if __name__ == "__main__":
  print(solution())