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 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=36000       ,  lowerCAmelCase_=13       ,  lowerCAmelCase_=7       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=42       ,  lowerCAmelCase_=5       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=4       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=80       ,  lowerCAmelCase_=33       ,  lowerCAmelCase_=0.0       ,  lowerCAmelCase_=1e-6       ,  lowerCAmelCase_=100       ,  lowerCAmelCase_=0.0       ,  ):
		_lowercase   =vocab_size
		_lowercase   =parent
		_lowercase   =batch_size
		_lowercase   =num_contexts
		# For common tests
		_lowercase   =self.num_contexts
		_lowercase   =is_training
		_lowercase   =hidden_size
		_lowercase   =num_ext_layers
		_lowercase   =ext_size
		_lowercase   =num_hidden_layers
		_lowercase   =num_attention_heads
		_lowercase   =num_experts
		_lowercase   =d_ff
		_lowercase   =d_ext
		_lowercase   =d_spout
		_lowercase   =dropout_rate
		_lowercase   =layer_norm_epsilon
		_lowercase   =expert_capacity
		_lowercase   =router_jitter_noise


	def __lowerCAmelCase      (   self       ):
		return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size       )

		_lowercase   =self.get_config()

		return (config, input_ids)


	def __lowerCAmelCase      (   self       ):
		_lowercase   =ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size       )

		_lowercase   =self.get_config()

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


	def __lowerCAmelCase      (   self       ):
		return GPTSanJapaneseConfig(
		    vocab_size=self.vocab_size       ,  num_contexts=self.seq_length       ,  d_model=self.hidden_size       ,  d_ff=self.d_ff       ,  d_ext=self.d_ext       ,  d_spout=self.d_spout       ,  num_switch_layers=self.num_hidden_layers - self.num_ext_layers       ,  num_ext_layers=self.num_ext_layers       ,  num_heads=self.num_attention_heads       ,  num_experts=self.num_experts       ,  expert_capacity=self.expert_capacity       ,  dropout_rate=self.dropout_rate       ,  layer_norm_epsilon=self.layer_norm_epsilon       ,  router_jitter_noise=self.router_jitter_noise       ,  )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  ):
		_lowercase   =GPTSanJapaneseForConditionalGeneration(config=snake_case_       )
		model.to(snake_case_       )
		model.eval()
		_lowercase   =model(
		    input_ids=snake_case_       ,  )
		self.parent.assertIsNotNone(snake_case_       )

@require_torch
class 	_a  (							A_							,		A_							,		unittest.TestCase					):



	"""simple docstring"""



	__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 __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):

		if pipeline_test_casse_name == "SummarizationPipelineTests":
			# TODO: fix `_reorder_cache` is not implemented for this model
			return True
		elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
			# TODO: check this.
			return True

		return False


	def __lowerCAmelCase      (   self       ):
		_lowercase   =GPTSanJapaneseTester(self       )
		_lowercase   =ConfigTester(self       ,  config_class=snake_case_       ,  d_model=37       )


	def __lowerCAmelCase      (   self       ):
		GPTSanJapaneseConfig()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*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 __lowerCAmelCase      (   self       ):
		super().test_model_parallelism()


	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
	def __lowerCAmelCase      (   self       ):
		pass

@require_torch
class 	_a  (							A_							,		A_							,		unittest.TestCase					):



	"""simple docstring"""



	__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 __lowerCAmelCase      (   self       ):
		_lowercase   =GPTSanJapaneseTester(self       )
		_lowercase   =ConfigTester(self       ,  config_class=snake_case_       ,  d_model=37       )


	def __lowerCAmelCase      (   self       ):
		GPTSanJapaneseConfig()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*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 __lowerCAmelCase      (   self       ):
		super().test_model_parallelism()


	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
	def __lowerCAmelCase      (   self       ):
		pass


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

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

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


	@slow
	def __lowerCAmelCase      (   self       ):
		_lowercase   =GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
		_lowercase   =GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
		model.to(snake_case_       )

		# set deterministically
		_lowercase   =GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"       )
		_lowercase   =1

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

		_lowercase   ="left"
		_lowercase   =tokenizer(snake_case_       ,  return_tensors="pt"       ,  padding=snake_case_       )
		_lowercase   =inputs["input_ids"].to(snake_case_       )

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

		_lowercase   =model.generate(
		    input_ids=snake_case_       ,  attention_mask=inputs["attention_mask"].to(snake_case_       )       ,  max_new_tokens=3       ,  generation_config=snake_case_       ,  )

		_lowercase   =tokenizer(sentences[0]       ,  return_tensors="pt"       ).input_ids.to(snake_case_       )
		_lowercase   =model.generate(
		    input_ids=snake_case_       ,  max_new_tokens=3       ,  generation_config=snake_case_       )

		_lowercase   =tokenizer(sentences[1]       ,  return_tensors="pt"       ).input_ids.to(snake_case_       )
		_lowercase   =model.generate(input_ids=snake_case_       ,  max_new_tokens=3       ,  generation_config=snake_case_       )

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

		_lowercase   =tokenizer.batch_decode(snake_case_       ,  skip_special_tokens=snake_case_       )
		_lowercase   =tokenizer.decode(output_non_padded[0]       ,  skip_special_tokens=snake_case_       )
		_lowercase   =tokenizer.decode(output_padded[0]       ,  skip_special_tokens=snake_case_       )

		_lowercase   =[
		    "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(snake_case_       ,  snake_case_       )
		self.assertListEqual(snake_case_       ,  [non_padded_sentence, padded_sentence]       )


	@tooslow
	def __lowerCAmelCase      (   self       ):
		_lowercase   =GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
		_lowercase   =GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
		# Output of original model created with mesh-tensoflow
		_lowercase   =[
		    ("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:
			_lowercase   =tokenizer.encode(snake_case_       ,  return_tensors="pt"       )
			_lowercase   =model(snake_case_       )
			_lowercase   =outputs.logits.detach().cpu().numpy()[0]
			_lowercase   =np.argmax(output_logits[-1]       )
			self.assertEqual(snake_case_       ,  snake_case_       )


	@slow
	def __lowerCAmelCase      (   self       ):
		_lowercase   =GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
		_lowercase   =GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
		model.to(snake_case_       )

		# set deterministically
		_lowercase   =GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"       )
		_lowercase   =1

		_lowercase   ="武田信玄は、"
		_lowercase   =tokenizer(snake_case_       ,  return_tensors="pt"       ).input_ids.to(snake_case_       )
		_lowercase   =tokenizer([input_text, input_text]       ,  return_tensors="pt"       ).input_ids.to(snake_case_       )

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

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

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

		_lowercase   =model.generate(
		    input_ids=snake_case_       ,  spout=snake_case_       ,  max_new_tokens=20       ,  generation_config=snake_case_       ,  )

		_lowercase   =tokenizer.decode(outputa[0]       )
		_lowercase   =tokenizer.decode(outputa[0]       )
		_lowercase   =tokenizer.batch_decode(snake_case_       )

		_lowercase   =[
		    "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(snake_case_       ,  snake_case_       )
		self.assertListEqual(snake_case_       ,  [outa_sentence, outa_sentence]       )


	@slow
	def __lowerCAmelCase      (   self       ):
		_lowercase   =GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
		_lowercase   =GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
		model.to(snake_case_       )

		# set deterministically
		_lowercase   =GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"       )
		_lowercase   =1

		_lowercase   ="武田信玄"
		_lowercase   ="織田信長"
		_lowercase   ="は、"
		_lowercase   ="が、"
		_lowercase   =tokenizer(snake_case_       ,  prefix_text=snake_case_       ,  return_tensors="pt"       )
		_lowercase   =tokenizer(snake_case_       ,  prefix_text=snake_case_       ,  return_tensors="pt"       )
		_lowercase   =tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]       ,  return_tensors="pt"       )

		_lowercase   =model.generate(
		    input_ids=input_tok_a.input_ids.to(snake_case_       )       ,  token_type_ids=input_tok_a.token_type_ids.to(snake_case_       )       ,  max_new_tokens=20       ,  generation_config=snake_case_       ,  )

		_lowercase   =model.generate(
		    input_ids=input_tok_a.input_ids.to(snake_case_       )       ,  token_type_ids=input_tok_a.token_type_ids.to(snake_case_       )       ,  max_new_tokens=20       ,  generation_config=snake_case_       ,  )

		_lowercase   =model.generate(
		    input_ids=input_tok_a.input_ids.to(snake_case_       )       ,  token_type_ids=input_tok_a.token_type_ids.to(snake_case_       )       ,  attention_mask=input_tok_a.attention_mask.to(snake_case_       )       ,  max_new_tokens=20       ,  generation_config=snake_case_       ,  )

		_lowercase   =tokenizer.decode(outputa[0]       )
		_lowercase   =tokenizer.decode(outputa[0]       )
		_lowercase   =tokenizer.batch_decode(snake_case_       )

		_lowercase   =[
		    "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(snake_case_       ,  snake_case_       )
		self.assertListEqual(snake_case_       ,  [outa_sentence, outa_sentence]       )










"""simple docstring"""



def     _SCREAMING_SNAKE_CASE  (				lowercase_ = 10_00		)		->						int:
 A__			=							0
 A__			=							0
 while 1:
  num += 3
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
 return total


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





