






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  UpperCamelCase    :


 def __init__(  self	:		Optional[Any]							,		UpperCAmelCase__	:		str							,		UpperCAmelCase__	:		int=36000							,		UpperCAmelCase__	:		int=13							,		UpperCAmelCase__	:		List[Any]=7							,		UpperCAmelCase__	:		List[str]=True							,		UpperCAmelCase__	:		str=32							,		UpperCAmelCase__	:		Optional[Any]=42							,		UpperCAmelCase__	:		str=5							,		UpperCAmelCase__	:		List[str]=2							,		UpperCAmelCase__	:		str=4							,		UpperCAmelCase__	:		Tuple=2							,		UpperCAmelCase__	:		Optional[Any]=32							,		UpperCAmelCase__	:		List[str]=80							,		UpperCAmelCase__	:		Tuple=33							,		UpperCAmelCase__	:		Dict=0.0							,		UpperCAmelCase__	:		Any=1E-6							,		UpperCAmelCase__	:		str=100							,		UpperCAmelCase__	:		Tuple=0.0							,		)      -> Tuple:
   _a						: str    =			vocab_size
   _a						: List[Any]    =			parent
   _a						: Dict    =			batch_size
   _a						: Tuple    =			num_contexts
   # For common tests
   _a						: List[Any]    =			self.num_contexts
   _a						: Any    =			is_training
   _a						: Union[str, Any]    =			hidden_size
   _a						: Dict    =			num_ext_layers
   _a						: Union[str, Any]    =			ext_size
   _a						: str    =			num_hidden_layers
   _a						: Optional[Any]    =			num_attention_heads
   _a						: List[Any]    =			num_experts
   _a						: List[str]    =			d_ff
   _a						: Any    =			d_ext
   _a						: Optional[int]    =			d_spout
   _a						: str    =			dropout_rate
   _a						: Optional[Any]    =			layer_norm_epsilon
   _a						: str    =			expert_capacity
   _a						: str    =			router_jitter_noise


 def _lowercase			(  self	:		str							)      -> List[str]:
   return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""							)


 def _lowercase			(  self	:		Any							)      -> Dict:
   _a						: Any    =			ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size							)

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

   return (config, input_ids)


 def _lowercase			(  self	:		List[str]							)      -> Dict:
   _a						: Optional[int]    =			ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size							)

   _a						: str    =			self.get_config()

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


 def _lowercase			(  self	:		Any							)      -> 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 _lowercase			(  self	:		Union[str, Any]							,		UpperCAmelCase__	:		Dict							,		UpperCAmelCase__	:		Optional[int]							,		)      -> List[Any]:
   _a						: List[str]    =			GPTSanJapaneseForConditionalGeneration(config=UpperCamelCase__							)
   model.to(UpperCamelCase__							)
   model.eval()
   _a						: List[str]    =			model(
       input_ids=UpperCamelCase__							,		)
   self.parent.assertIsNotNone(UpperCamelCase__							)







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


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







   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 _lowercase			(  self	:		Union[str, Any]							)      -> Tuple:
   _a						: int    =			GPTSanJapaneseTester(self							)
   _a						: Tuple    =			ConfigTester(self							,		config_class=UpperCamelCase__							,		d_model=37							)


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


 def _lowercase			(  self	:		Optional[Any]							)      -> str:
   _a						: str    =			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 _lowercase			(  self	:		List[str]							)      -> List[str]:
   super().test_model_parallelism()







 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
 def _lowercase			(  self	:		List[Any]							)      -> Dict:
   pass







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


 def _lowercase			(  self	:		Any							)      -> str:
   _a						: Any    =			GPTSanJapaneseTester(self							)
   _a						: Optional[Any]    =			ConfigTester(self							,		config_class=UpperCamelCase__							,		d_model=37							)


 def _lowercase			(  self	:		Dict							)      -> Optional[int]:
   GPTSanJapaneseConfig()


 def _lowercase			(  self	:		Tuple							)      -> str:
   _a						: str    =			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 _lowercase			(  self	:		Optional[Any]							)      -> Union[str, Any]:
   super().test_model_parallelism()


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


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

   def check(UpperCAmelCase__	:		Optional[Any]							,		UpperCAmelCase__	:		List[Any]							,		UpperCAmelCase__	:		List[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 _lowercase			(  self	:		Any							)      -> Dict:
   _a						: Tuple    =			GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   _a						: Any    =			GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   model.to(UpperCamelCase__							)

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

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

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

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

   _a						: Optional[Any]    =			model.generate(
       input_ids=UpperCamelCase__							,		attention_mask=inputs["""attention_mask"""].to(UpperCamelCase__							)							,		max_new_tokens=3							,		generation_config=UpperCamelCase__							,		)

   _a						: Tuple    =			tokenizer(sentences[0]							,		return_tensors="""pt"""							).input_ids.to(UpperCamelCase__							)
   _a						: Union[str, Any]    =			model.generate(
       input_ids=UpperCamelCase__							,		max_new_tokens=3							,		generation_config=UpperCamelCase__							)

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

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

   _a						: int    =			tokenizer.batch_decode(UpperCamelCase__							,		skip_special_tokens=UpperCamelCase__							)
   _a						: List[Any]    =			tokenizer.decode(output_non_padded[0]							,		skip_special_tokens=UpperCamelCase__							)
   _a						: Dict    =			tokenizer.decode(output_padded[0]							,		skip_special_tokens=UpperCamelCase__							)

   _a						: Optional[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(UpperCamelCase__							,		UpperCamelCase__							)
   self.assertListEqual(UpperCamelCase__							,		[non_padded_sentence, padded_sentence]							)


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


 @slow
 def _lowercase			(  self	:		Tuple							)      -> Tuple:
   _a						: List[str]    =			GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   _a						: List[Any]    =			GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   model.to(UpperCamelCase__							)

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

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

   # spout from uniform and one-hot
   _a						: Union[str, Any]    =			[
       # 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
   ]

   _a						: Optional[int]    =			model.generate(
       input_ids=UpperCamelCase__							,		spout=spouts[0]							,		max_new_tokens=20							,		generation_config=UpperCamelCase__							,		)

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

   _a						: str    =			model.generate(
       input_ids=UpperCamelCase__							,		spout=UpperCamelCase__							,		max_new_tokens=20							,		generation_config=UpperCamelCase__							,		)

   _a						: int    =			tokenizer.decode(outputa[0]							)
   _a						: Optional[int]    =			tokenizer.decode(outputa[0]							)
   _a						: int    =			tokenizer.batch_decode(UpperCamelCase__							)

   _a						: Tuple    =			[
       '''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 _lowercase			(  self	:		str							)      -> Optional[Any]:
   _a						: Optional[Any]    =			GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   _a						: Tuple    =			GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   model.to(UpperCamelCase__							)

   # set deterministically
   _a						: Dict    =			GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   _a						: int    =			1

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

   _a						: List[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=20							,		generation_config=UpperCamelCase__							,		)

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

   _a						: Union[str, 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=20							,		generation_config=UpperCamelCase__							,		)

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

   _a						: Any    =			[
       '''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]							)









"""simple docstring"""




from __future__ import annotations

import math
from itertools import permutations

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5  ,     int(math.sqrt(UpperCamelCase__       ) + 1       )  ,     6       ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True

def     lowerCAmelCase__  (  UpperCamelCase__ = 7       ):



  '''simple docstring'''
  _a						: Tuple    =			"""""".join(str(UpperCamelCase__       ) for i in range(1  ,     n + 1       )       )
  _a						: str    =			[int("""""".join(UpperCamelCase__       )       ) for i in permutations(UpperCamelCase__  ,     UpperCamelCase__       )]
  _a						: Union[str, Any]    =			[num for num in perm_list if is_prime(UpperCamelCase__       )]
  return max(UpperCamelCase__       ) if pandigitals else 0


if __name__ == "__main__":
 print(F'''{solution() = }''')

