

"""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       _snake_case  :







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=3_6_0_0_0  ,				UpperCAmelCase_=1_3  ,				UpperCAmelCase_=7  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=3_2  ,				UpperCAmelCase_=4_2  ,				UpperCAmelCase_=5  ,				UpperCAmelCase_=2  ,				UpperCAmelCase_=4  ,				UpperCAmelCase_=2  ,				UpperCAmelCase_=3_2  ,				UpperCAmelCase_=8_0  ,				UpperCAmelCase_=3_3  ,				UpperCAmelCase_=0.0  ,				UpperCAmelCase_=1e-6  ,				UpperCAmelCase_=1_0_0  ,				UpperCAmelCase_=0.0  ,				)      ->    List[Any]:
											__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 							_UpperCamelCase     (self				)      ->    Any:
											return GPTSanJapaneseConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''				)







						def 							_UpperCamelCase     (self				)      ->    str:
											__lowercase        =      ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size				)

											__lowercase        =      self.get_config()

											return (config, input_ids)







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size				)

											__lowercase        =      self.get_config()

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







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											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  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				)      ->    List[str]:
											__lowercase        =      GPTSanJapaneseForConditionalGeneration(config=lowercase_				)
											model.to(lowercase_				)
											model.eval()
											__lowercase        =      model(
											    input_ids=lowercase_  ,				)
											self.parent.assertIsNotNone(lowercase_				)



@require_torch
class       _snake_case  (    snake_case__    ,      snake_case__    ,      unittest.TestCase							):
						__snake_case			      =  (GPTSanJapaneseModel,) if is_torch_available() else ()
						__snake_case			      =  (
						    {
						        'conversational': GPTSanJapaneseForConditionalGeneration,
						        'feature-extraction': GPTSanJapaneseForConditionalGeneration,
						        'summarization': GPTSanJapaneseForConditionalGeneration,
						        'text2text-generation': GPTSanJapaneseForConditionalGeneration,
						        'translation': GPTSanJapaneseForConditionalGeneration,
						    }
						    if is_torch_available()
						    else {}
						)
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
						__snake_case			      =  [0.8, 0.9]







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Union[str, 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 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      GPTSanJapaneseTester(self				)
											__lowercase        =      ConfigTester(self  ,				config_class=lowercase_  ,				d_model=3_7				)







						def 							_UpperCamelCase     (self				)      ->    Any:
											GPTSanJapaneseConfig()







						def 							_UpperCamelCase     (self				)      ->    int:
											__lowercase        =      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_				)







						@unittest.skip(
						    reason='''skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'''				)
						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											super().test_model_parallelism()







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



@require_torch
class       _snake_case  (    snake_case__    ,      snake_case__    ,      unittest.TestCase							):
						__snake_case			      =  (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
						__snake_case			      =  [0.8, 0.9]







						def 							_UpperCamelCase     (self				)      ->    int:
											__lowercase        =      GPTSanJapaneseTester(self				)
											__lowercase        =      ConfigTester(self  ,				config_class=lowercase_  ,				d_model=3_7				)







						def 							_UpperCamelCase     (self				)      ->    Dict:
											GPTSanJapaneseConfig()







						def 							_UpperCamelCase     (self				)      ->    str:
											__lowercase        =      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_				)







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







						@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
						def 							_UpperCamelCase     (self				)      ->    Any:
											pass







						@slow
						def 							_UpperCamelCase     (self				)      ->    int:
											__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(lowercase_				)
											__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(lowercase_				).flatten()
											__lowercase        =      output_logits[0, :, :2_0].flatten()

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

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







						@slow
						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__lowercase        =      GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''				)
											__lowercase        =      GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''				)
											model.to(lowercase_				)

											# 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(lowercase_  ,				return_tensors='''pt'''  ,				padding=lowercase_				)
											__lowercase        =      inputs["input_ids"].to(lowercase_				)

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

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

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

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

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

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

											__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(lowercase_  ,				lowercase_				)
											self.assertListEqual(lowercase_  ,				[non_padded_sentence, padded_sentence]				)







						@tooslow
						def 							_UpperCamelCase     (self				)      ->    Any:
											__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", 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:
																__lowercase        =      tokenizer.encode(lowercase_  ,				return_tensors='''pt'''				)
																__lowercase        =      model(lowercase_				)
																__lowercase        =      outputs.logits.detach().cpu().numpy()[0]
																__lowercase        =      np.argmax(output_logits[-1]				)
																self.assertEqual(lowercase_  ,				lowercase_				)







						@slow
						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											__lowercase        =      GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''				)
											__lowercase        =      GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''				)
											model.to(lowercase_				)

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

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

											# 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=lowercase_  ,				spout=spouts[0]  ,				max_new_tokens=2_0  ,				generation_config=lowercase_  ,				)

											__lowercase        =      model.generate(
											    input_ids=lowercase_  ,				spout=spouts[1]  ,				max_new_tokens=2_0  ,				generation_config=lowercase_  ,				)

											__lowercase        =      model.generate(
											    input_ids=lowercase_  ,				spout=lowercase_  ,				max_new_tokens=2_0  ,				generation_config=lowercase_  ,				)

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

											__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(lowercase_  ,				lowercase_				)
											self.assertListEqual(lowercase_  ,				[outa_sentence, outa_sentence]				)







						@slow
						def 							_UpperCamelCase     (self				)      ->    str:
											__lowercase        =      GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''				)
											__lowercase        =      GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''				)
											model.to(lowercase_				)

											# 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(lowercase_  ,				prefix_text=lowercase_  ,				return_tensors='''pt'''				)
											__lowercase        =      tokenizer(lowercase_  ,				prefix_text=lowercase_  ,				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(lowercase_				)  ,				token_type_ids=input_tok_a.token_type_ids.to(lowercase_				)  ,				max_new_tokens=2_0  ,				generation_config=lowercase_  ,				)

											__lowercase        =      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_  ,				)

											__lowercase        =      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_  ,				)

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

											__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(lowercase_  ,				lowercase_				)
											self.assertListEqual(lowercase_  ,				[outa_sentence, outa_sentence]				)




'''simple docstring'''
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if right <= left:
        return
    snake_case_			:     Dict        =    left
    snake_case_			:     Tuple        =    right
    snake_case_			:     List[str]        =    sorting[left]
    while i <= b:
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Union[str, Any]        =    sorting[i], sorting[a]
            a += 1
            i += 1
        elif sorting[i] > pivot:
            snake_case_      ,			snake_case_			:     List[str]        =    sorting[i], sorting[b]
            b -= 1
        else:
            i += 1
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		a - 1							)
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		b + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if left < right:
        snake_case_			:     Dict        =    lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		pivot_index - 1							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		pivot_index + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[Any]        =    sorting[right]
    snake_case_			:     Optional[int]        =    left
    for i in range(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Dict        =    sorting[i], sorting[store_index]
            store_index += 1
    snake_case_      ,			snake_case_			:     Optional[Any]        =    sorting[store_index], sorting[right]
    return store_index

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if len(__SCREAMING_SNAKE_CASE							) <= 1:
        return sorting
    return (
        three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
        + [i for i in sorting if i == sorting[0]]
        + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)

  a_           =    input("Enter numbers separated by a comma:\n").strip()
  a_           =    [int(item) for item in user_input.split(",")]
  quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
  print(unsorted)




