code: 
  
  """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     __UpperCamelCase				:
  
  
  		def __init__(self				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=3_6_0_0_0  ,  __SCREAMING_SNAKE_CASE				:	Any=1_3  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=7  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=True  ,  __SCREAMING_SNAKE_CASE				:	Tuple=3_2  ,  __SCREAMING_SNAKE_CASE				:	Any=4_2  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=5  ,  __SCREAMING_SNAKE_CASE				:	Tuple=2  ,  __SCREAMING_SNAKE_CASE				:	str=4  ,  __SCREAMING_SNAKE_CASE				:	List[str]=2  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=3_2  ,  __SCREAMING_SNAKE_CASE				:	Any=8_0  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=3_3  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0.0  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1E-6  ,  __SCREAMING_SNAKE_CASE				:	int=1_0_0  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0.0  ,  ):
  							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       SCREAMING_SNAKE_CASE__     (self				:	int):
  							return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese")
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	int):
  							A    =      ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size)
  
  							A    =      self.get_config()
  
  							return (config, input_ids)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							A    =      ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size)
  
  							A    =      self.get_config()
  
  							return (config, {"input_ids": input_ids})
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							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       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Any  ,  ):
  							A    =      GPTSanJapaneseForConditionalGeneration(config=lowercase_)
  							model.to(lowercase_)
  							model.eval()
  							A    =      model(
  							    input_ids=lowercase_  ,  )
  							self.parent.assertIsNotNone(lowercase_)
  
  
  
  
  
  @require_torch
  class     __UpperCamelCase				(     __UpperCAmelCase  ,	__UpperCAmelCase  ,	unittest.TestCase		):
  		SCREAMING_SNAKE_CASE              =					(GPTSanJapaneseModel,) if is_torch_available() else ()
  		SCREAMING_SNAKE_CASE              =					(
  		    {
  		        "conversational": GPTSanJapaneseForConditionalGeneration,
  		        "feature-extraction": GPTSanJapaneseForConditionalGeneration,
  		        "summarization": GPTSanJapaneseForConditionalGeneration,
  		        "text2text-generation": GPTSanJapaneseForConditionalGeneration,
  		        "translation": GPTSanJapaneseForConditionalGeneration,
  		    }
  		    if is_torch_available()
  		    else {}
  		)
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  		SCREAMING_SNAKE_CASE              =					[0.8, 0.9]
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]):
  
  							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       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							A    =      GPTSanJapaneseTester(self)
  							A    =      ConfigTester(self  ,  config_class=lowercase_  ,  d_model=3_7)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							GPTSanJapaneseConfig()
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							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       SCREAMING_SNAKE_CASE__     (self				:	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       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							pass
  
  
  
  
  
  @require_torch
  class     __UpperCamelCase				(     __UpperCAmelCase  ,	__UpperCAmelCase  ,	unittest.TestCase		):
  		SCREAMING_SNAKE_CASE              =					(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  		SCREAMING_SNAKE_CASE              =					[0.8, 0.9]
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
  							A    =      GPTSanJapaneseTester(self)
  							A    =      ConfigTester(self  ,  config_class=lowercase_  ,  d_model=3_7)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
  							GPTSanJapaneseConfig()
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							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       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							super().test_model_parallelism()
  
  
  		@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
  		def       SCREAMING_SNAKE_CASE__     (self				:	int):
  							pass
  
  
  		@slow
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							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
  							    [-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    =      np.array(lowercase_).flatten()
  							A    =      output_logits[0, :, :2_0].flatten()
  
  							def check(__SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	List[str]=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       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							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       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
  							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''', 3_5_6_7_5),
  							    ('''武田信玄は、''', 4_5),
  							    ('''武田信玄は、この''', 2_9),
  							    ('''武田信玄は、このよう''', 3_0_6_4_2),
  							    ('''武田信玄は、このような''', 3_5_6_8_0),
  							    ('''武田信玄は、このような「''', 8_6_4_0),
  							    ('''武田信玄は、このような「武田''', 3_1_6_1_7),
  							    ('''武田信玄は、このような「武田家''', 3_0_6_4_6),
  							    ('''武田信玄は、このような「武田家の''', 3_1_6_1_7),
  							    ('''武田信玄は、このような「武田家の家''', 3_1_3_8_1),
  							]
  							for input, output in target:
  												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       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							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
  							    [0.8_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    =      model.generate(
  							    input_ids=lowercase_  ,  spout=spouts[0]  ,  max_new_tokens=2_0  ,  generation_config=lowercase_  ,  )
  
  							A    =      model.generate(
  							    input_ids=lowercase_  ,  spout=spouts[1]  ,  max_new_tokens=2_0  ,  generation_config=lowercase_  ,  )
  
  							A    =      model.generate(
  							    input_ids=lowercase_  ,  spout=lowercase_  ,  max_new_tokens=2_0  ,  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       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							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=2_0  ,  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=2_0  ,  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=2_0  ,  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])
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A  :					int												=	{
      'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A  :					Any												=	[
  	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'NystromformerForMaskedLM',
  	    'NystromformerForMultipleChoice',
  	    'NystromformerForQuestionAnswering',
  	    'NystromformerForSequenceClassification',
  	    'NystromformerForTokenClassification',
  	    'NystromformerLayer',
  	    'NystromformerModel',
  	    'NystromformerPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_nystromformer import (
  		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    NystromformerForMaskedLM,
  		    NystromformerForMultipleChoice,
  		    NystromformerForQuestionAnswering,
  		    NystromformerForSequenceClassification,
  		    NystromformerForTokenClassification,
  		    NystromformerLayer,
  		    NystromformerModel,
  		    NystromformerPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__A  :					Optional[Any]												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  