


'''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_						:





    """simple docstring"""






    def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=3_60_00    ,  SCREAMING_SNAKE_CASE_=13    ,  SCREAMING_SNAKE_CASE_=7    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=32    ,  SCREAMING_SNAKE_CASE_=42    ,  SCREAMING_SNAKE_CASE_=5    ,  SCREAMING_SNAKE_CASE_=2    ,  SCREAMING_SNAKE_CASE_=4    ,  SCREAMING_SNAKE_CASE_=2    ,  SCREAMING_SNAKE_CASE_=32    ,  SCREAMING_SNAKE_CASE_=80    ,  SCREAMING_SNAKE_CASE_=33    ,  SCREAMING_SNAKE_CASE_=0.0    ,  SCREAMING_SNAKE_CASE_=1E-6    ,  SCREAMING_SNAKE_CASE_=1_00    ,  SCREAMING_SNAKE_CASE_=0.0    ,  )       ->  Optional[Any]:
         __lowerCamelCase     :			Tuple              =	vocab_size
         __lowerCamelCase     :			List[str]              =	parent
         __lowerCamelCase     :			int              =	batch_size
         __lowerCamelCase     :			Optional[int]              =	num_contexts
         # For common tests
         __lowerCamelCase     :			int              =	self.num_contexts
         __lowerCamelCase     :			int              =	is_training
         __lowerCamelCase     :			Dict              =	hidden_size
         __lowerCamelCase     :			str              =	num_ext_layers
         __lowerCamelCase     :			List[Any]              =	ext_size
         __lowerCamelCase     :			str              =	num_hidden_layers
         __lowerCamelCase     :			Tuple              =	num_attention_heads
         __lowerCamelCase     :			List[Any]              =	num_experts
         __lowerCamelCase     :			Tuple              =	d_ff
         __lowerCamelCase     :			List[str]              =	d_ext
         __lowerCamelCase     :			Union[str, Any]              =	d_spout
         __lowerCamelCase     :			Tuple              =	dropout_rate
         __lowerCamelCase     :			Union[str, Any]              =	layer_norm_epsilon
         __lowerCamelCase     :			List[str]              =	expert_capacity
         __lowerCamelCase     :			Dict              =	router_jitter_noise







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







    def    lowercase_       (							self			)       ->  Any:
         __lowerCamelCase     :			Any              =	ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size			)

         __lowerCamelCase     :			str              =	self.get_config()

         return (config, input_ids)







    def    lowercase_       (							self			)       ->  List[Any]:
         __lowerCamelCase     :			List[Any]              =	ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size			)

         __lowerCamelCase     :			Optional[int]              =	self.get_config()

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







    def    lowercase_       (							self			)       ->  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    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  )       ->  Any:
         __lowerCamelCase     :			List[str]              =	GPTSanJapaneseForConditionalGeneration(config=a_			)
         model.to(a_			)
         model.eval()
         __lowerCamelCase     :			Optional[int]              =	model(
             input_ids=a_    ,  )
         self.parent.assertIsNotNone(a_			)







@require_torch
class      UpperCAmelCase_						(_UpperCAmelCase      ,	_UpperCAmelCase      ,	unittest.TestCase						):





    """simple docstring"""






    lowerCamelCase						:      Any							  =    (GPTSanJapaneseModel,) if is_torch_available() else ()
    lowerCamelCase						:      Union[str, Any]							  =    (
        {
            '''conversational''': GPTSanJapaneseForConditionalGeneration,
            '''feature-extraction''': GPTSanJapaneseForConditionalGeneration,
            '''summarization''': GPTSanJapaneseForConditionalGeneration,
            '''text2text-generation''': GPTSanJapaneseForConditionalGeneration,
            '''translation''': GPTSanJapaneseForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase						:      Optional[int]							  =    False
    lowerCamelCase						:      str							  =    False
    lowerCamelCase						:      Dict							  =    False
    lowerCamelCase						:      Optional[Any]							  =    False
    lowerCamelCase						:      Dict							  =    False
    lowerCamelCase						:      Union[str, Any]							  =    False
    lowerCamelCase						:      int							  =    False
    lowerCamelCase						:      int							  =    False
    # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
    lowerCamelCase						:      Any							  =    [0.8, 0.9]







    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  int:
         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]:
         __lowerCamelCase     :			Any              =	GPTSanJapaneseTester(self			)
         __lowerCamelCase     :			Tuple              =	ConfigTester(self    ,  config_class=a_    ,  d_model=37			)







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







    def    lowercase_       (							self			)       ->  Dict:
         __lowerCamelCase     :			Dict              =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a_			)







    @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			)       ->  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			)       ->  int:
         pass







@require_torch
class      UpperCAmelCase_						(_UpperCAmelCase      ,	_UpperCAmelCase      ,	unittest.TestCase						):





    """simple docstring"""






    lowerCamelCase						:      Optional[int]							  =    (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
    lowerCamelCase						:      Optional[int]							  =    False
    lowerCamelCase						:      Union[str, Any]							  =    False
    lowerCamelCase						:      Optional[int]							  =    False
    lowerCamelCase						:      Any							  =    False
    lowerCamelCase						:      Tuple							  =    False
    lowerCamelCase						:      Union[str, Any]							  =    False
    # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
    lowerCamelCase						:      List[Any]							  =    [0.8, 0.9]







    def    lowercase_       (							self			)       ->  Dict:
         __lowerCamelCase     :			Any              =	GPTSanJapaneseTester(self			)
         __lowerCamelCase     :			str              =	ConfigTester(self    ,  config_class=a_    ,  d_model=37			)







    def    lowercase_       (							self			)       ->  Tuple:
         GPTSanJapaneseConfig()







    def    lowercase_       (							self			)       ->  str:
         __lowerCamelCase     :			Union[str, Any]              =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a_			)







    @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			)       ->  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			)       ->  Tuple:
         pass







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

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

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







    @slow
    def    lowercase_       (							self			)       ->  Optional[Any]:
         __lowerCamelCase     :			Union[str, Any]              =	GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'			)
         __lowerCamelCase     :			Optional[int]              =	GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'			)
         model.to(a_			)

         # set deterministically
         __lowerCamelCase     :			List[str]              =	GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'			)
         __lowerCamelCase     :			Dict              =	1

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

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

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

         __lowerCamelCase     :			Any              =	model.generate(
             input_ids=a_    ,  attention_mask=inputs['attention_mask'].to(a_			)    ,  max_new_tokens=3    ,  generation_config=a_    ,  )

         __lowerCamelCase     :			int              =	tokenizer(sentences[0]    ,  return_tensors='pt'			).input_ids.to(a_			)
         __lowerCamelCase     :			Optional[Any]              =	model.generate(
             input_ids=a_    ,  max_new_tokens=3    ,  generation_config=a_			)

         __lowerCamelCase     :			Optional[Any]              =	tokenizer(sentences[1]    ,  return_tensors='pt'			).input_ids.to(a_			)
         __lowerCamelCase     :			int              =	model.generate(input_ids=a_    ,  max_new_tokens=3    ,  generation_config=a_			)

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

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

         __lowerCamelCase     :			Any              =	[
             '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(a_    ,  a_			)
         self.assertListEqual(a_    ,  [non_padded_sentence, padded_sentence]			)







    @tooslow
    def    lowercase_       (							self			)       ->  List[Any]:
         __lowerCamelCase     :			List[Any]              =	GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'			)
         __lowerCamelCase     :			Optional[Any]              =	GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'			)
         # Output of original model created with mesh-tensoflow
         __lowerCamelCase     :			int              =	[
             ('武田信玄は', 3_56_75),
             ('武田信玄は、', 45),
             ('武田信玄は、この', 29),
             ('武田信玄は、このよう', 3_06_42),
             ('武田信玄は、このような', 3_56_80),
             ('武田信玄は、このような「', 86_40),
             ('武田信玄は、このような「武田', 3_16_17),
             ('武田信玄は、このような「武田家', 3_06_46),
             ('武田信玄は、このような「武田家の', 3_16_17),
             ('武田信玄は、このような「武田家の家', 3_13_81),
         ]
         for input, output in target:
              __lowerCamelCase     :			str              =	tokenizer.encode(a_    ,  return_tensors='pt'			)
              __lowerCamelCase     :			Optional[int]              =	model(a_			)
              __lowerCamelCase     :			str              =	outputs.logits.detach().cpu().numpy()[0]
              __lowerCamelCase     :			Union[str, Any]              =	np.argmax(output_logits[-1]			)
              self.assertEqual(a_    ,  a_			)







    @slow
    def    lowercase_       (							self			)       ->  Dict:
         __lowerCamelCase     :			Optional[int]              =	GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'			)
         __lowerCamelCase     :			List[Any]              =	GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'			)
         model.to(a_			)

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

         __lowerCamelCase     :			Tuple              =	'武田信玄は、'
         __lowerCamelCase     :			List[str]              =	tokenizer(a_    ,  return_tensors='pt'			).input_ids.to(a_			)
         __lowerCamelCase     :			int              =	tokenizer([input_text, input_text]    ,  return_tensors='pt'			).input_ids.to(a_			)

         # spout from uniform and one-hot
         __lowerCamelCase     :			int              =	[
             # fmt: off
             [0.8_7_8_8_2_2_0_8, 0.3_8_4_2_6_3_9_6, 0.3_3_2_2_0_2_4_8, 0.4_3_8_9_0_4_0_6, 0.1_6_5_6_2_2_5_2,
             0.0_4_8_0_3_9_8_5, 0.2_1_1_5_7_2  , 0.2_3_1_8_8_4_7_3, 0.3_7_1_5_3_0_6_8, 0.7_8_3_6_3_7_7 ,
             0.0_2_1_6_0_1_7_2, 0.3_8_7_6_1_7_1_9, 0.7_5_2_9_0_7_7_2, 0.9_0_1_9_8_8_5_7, 0.3_4_3_6_5_7_7_7,
             0.6_4_1_6_8_1_6_9, 0.4_4_3_1_8_4_7_1, 0.1_4_5_7_5_7_4_6, 0.9_2_5_6_2_8_8_1, 0.4_0_8_1_2_1_4_8,
             0.2_9_0_1_9_1_2_2, 0.8_8_8_6_1_5_9_9, 0.6_5_5_2_4_8_4_6, 0.4_3_5_6_3_4_5_6, 0.3_8_1_7_7_1_8_7,
             0.7_0_8_3_2_9_6_5, 0.8_1_5_2_7_8_9_2, 0.6_8_8_3_2_8_1_2, 0.3_8_8_3_3_1_9_2, 0.4_5_6_1_5_2_2 ,
             0.1_4_8_2_8_8_1_7, 0.4_7_2_4_8_2_1_3, 0.5_4_3_5_7_3_3_5, 0.8_2_0_0_9_5_6_6, 0.1_3_3_8_8_8_4 ,
             0.0_2_7_5_5_4_1_7, 0.1_9_7_6_4_6_7_7, 0.2_4_2_2_0_8_4 , 0.0_4_7_5_7_6_7_4, 0.6_5_4_0_9_6_0_6,
             0.0_8_2_4_5_8_9 , 0.0_3_3_0_4_3_8_3, 0.9_4_3_8_7_6_8_9, 0.9_8_7_6_4_5_0_9, 0.8_2_4_3_3_9_0_1,
             0.2_7_6_4_6_7_4_1, 0.6_4_9_0_7_4_9_3, 0.7_6_0_0_9_4_0_6, 0.3_0_0_8_7_9_1_5, 0.1_7_9_0_4_6_8_9,
             0.4_1_6_0_1_7_1_4, 0.6_7_0_4_6_3_9_8, 0.1_0_4_2_2_8_2_2, 0.0_8_4_4_7_3_7_4, 0.0_7_3_5_4_3_4_4,
             0.6_1_4_2_3_5_6_5, 0.7_0_2_8_4_8_6_6, 0.7_5_3_2_3_3_3 , 0.1_9_7_2_0_3_8 , 0.2_9_5_7_5_6_5_9,
             0.9_0_5_8_3_8_8_6, 0.2_9_2_6_5_3_0_7, 0.5_0_0_0_0_1_7_5, 0.7_0_4_0_7_6_5_5, 0.8_8_9_3_6_3  ,
             0.8_1_9_0_4_4_1_8, 0.6_6_8_2_9_1_2_8, 0.6_4_4_6_8_8_1_5, 0.5_6_5_6_3_7_2_3, 0.8_5_6_0_1_8_7_5,
             0.9_4_9_2_4_6_7_2, 0.0_0_1_6_6_7_6_2, 0.2_5_2_2_0_6_4_3, 0.7_4_5_4_0_2_1_9, 0.6_7_9_9_3_2_4_7,
             0.1_5_4_9_6_7_5 , 0.3_9_3_8_5_3_5_2, 0.9_2_1_5_3_6_0_7, 0.6_3_7_4_5_9_3_1, 0.2_7_7_5_9_0_4_3,
             0.8_4_7_0_2_2_9_5, 0.6_5_9_0_4_2_7_1, 0.5_8_6_7_6_6_1_4, 0.8_6_6_6_9_3_6 , 0.3_9_6_0_7_4_3_8,
             0.7_9_9_5_4_9_8_3, 0.4_2_2_2_0_6_9_7, 0.3_9_6_5_0_3_8_1, 0.7_8_4_9_8_6_4 , 0.5_6_1_5_0_2_0_1,
             0.1_5_6_7_8_9_2_5, 0.1_4_7_4_6_0_3_2, 0.3_4_5_4_2_1_1_4, 0.4_7_0_2_6_7_8_3, 0.1_1_9_5_6_4_8_9,
             0.2_5_4_2_1_4_3_5, 0.3_3_7_8_8_9_0_1, 0.6_8_9_3_4_8_4_2, 0.3_6_4_2_4_6_8_5, 0.7_1_7_3_7_8_9_8,
             0.3_8_9_8_3_4_4_9, 0.9_4_3_9_3_7_7_9, 0.3_9_5_7_5_5_8_8, 0.3_6_6_1_6_5_5_3, 0.8_7_1_0_4_6_6_5,
             0.6_4_6_3_0_2_0_3, 0.2_2_5_1_6_9_0_5, 0.8_8_2_7_0_8_0_4, 0.1_5_0_3_1_3_3_8, 0.7_5_1_4_4_3_4_5,
             0.4_6_4_5_9_0_2_5, 0.8_5_3_9_6_4_5_4, 0.8_6_3_5_5_6_4_3, 0.6_5_1_3_9_8_5_1, 0.7_0_2_6_6_0_6_1,
             0.3_0_2_4_1_3_8_9, 0.8_1_0_5_6_4_9_7, 0.8_8_8_6_5_9_6_9, 0.3_8_7_7_3_8_0_7, 0.7_0_6_3_5_8_4_9,
             0.9_0_7_1_8_4_5_9, 0.4_3_2_4_5_7_8_9, 0.2_8_0_0_0_6_5_4, 0.4_5_9_3_5_5_6_2, 0.0_8_7_7_3_5_1_9,
             0.9_5_5_2_1_5_1 , 0.9_3_9_0_1_5_1_1, 0.2_2_4_8_9_2_8_8], # uniform
             [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0.],
             # fmt: on
         ]

         __lowerCamelCase     :			Any              =	model.generate(
             input_ids=a_    ,  spout=spouts[0]    ,  max_new_tokens=20    ,  generation_config=a_    ,  )

         __lowerCamelCase     :			Union[str, Any]              =	model.generate(
             input_ids=a_    ,  spout=spouts[1]    ,  max_new_tokens=20    ,  generation_config=a_    ,  )

         __lowerCamelCase     :			List[Any]              =	model.generate(
             input_ids=a_    ,  spout=a_    ,  max_new_tokens=20    ,  generation_config=a_    ,  )

         __lowerCamelCase     :			Optional[int]              =	tokenizer.decode(outputa[0]			)
         __lowerCamelCase     :			Any              =	tokenizer.decode(outputa[0]			)
         __lowerCamelCase     :			Union[str, Any]              =	tokenizer.batch_decode(a_			)

         __lowerCamelCase     :			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(a_    ,  a_			)
         self.assertListEqual(a_    ,  [outa_sentence, outa_sentence]			)







    @slow
    def    lowercase_       (							self			)       ->  int:
         __lowerCamelCase     :			str              =	GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'			)
         __lowerCamelCase     :			Tuple              =	GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'			)
         model.to(a_			)

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

         __lowerCamelCase     :			Optional[int]              =	'武田信玄'
         __lowerCamelCase     :			str              =	'織田信長'
         __lowerCamelCase     :			Any              =	'は、'
         __lowerCamelCase     :			Dict              =	'が、'
         __lowerCamelCase     :			List[str]              =	tokenizer(a_    ,  prefix_text=a_    ,  return_tensors='pt'			)
         __lowerCamelCase     :			Union[str, Any]              =	tokenizer(a_    ,  prefix_text=a_    ,  return_tensors='pt'			)
         __lowerCamelCase     :			Optional[Any]              =	tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]    ,  return_tensors='pt'			)

         __lowerCamelCase     :			List[Any]              =	model.generate(
             input_ids=input_tok_a.input_ids.to(a_			)    ,  token_type_ids=input_tok_a.token_type_ids.to(a_			)    ,  max_new_tokens=20    ,  generation_config=a_    ,  )

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

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

         __lowerCamelCase     :			Optional[Any]              =	tokenizer.decode(outputa[0]			)
         __lowerCamelCase     :			Union[str, Any]              =	tokenizer.decode(outputa[0]			)
         __lowerCamelCase     :			Union[str, Any]              =	tokenizer.batch_decode(a_			)

         __lowerCamelCase     :			int              =	[
             '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(a_    ,  a_			)
         self.assertListEqual(a_    ,  [outa_sentence, outa_sentence]			)










'''simple docstring'''



A__	:      List[Any]				=						[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
A__	:      Dict				=						3
A__	:      Union[str, Any]				=						5

# Buffer Space (with Parameters Space)
A__	:      list[float]				=						[]
A__	:      list[float]				=						[]

# Machine Time
A__	:      Dict				=						0



def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	str:
     global buffer_space, params_space, machine_time, K, m, t

     # Choosing Dynamical Systems (All)
     for key, value in enumerate(UpperCAmelCase_							):
          # Evolution Parameter
          __lowerCamelCase     :			List[str]              =	float(seed / value							)

          # Control Theory: Orbit Change
          __lowerCamelCase     :			Optional[int]              =	(buffer_space[(key + 1) % m] + e) % 1

          # Control Theory: Trajectory Change
          __lowerCamelCase     :			str              =	(params_space[key] + e) % 1 + 3

          # Modification (Transition Function) - Jumps
          __lowerCamelCase     :			List[Any]              =	round(float(r * value * (1 - value)							)   ,					10							)
          __lowerCamelCase     :			Tuple              =	r  # Saving to Parameters Space

     # Logistic Map
     assert max(UpperCAmelCase_							) < 1
     assert max(UpperCAmelCase_							) < 4

     # Machine Time
     machine_time += 1



def     UpperCAmelCase__      (			)  ->	Optional[int]:
     global buffer_space, params_space, machine_time, K, m, t

     # PRNG (Xorshift by George Marsaglia)
     def xorshift(UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple							):
          x ^= y >> 13
          y ^= x << 17
          x ^= y >> 5
          return x

     # Choosing Dynamical Systems (Increment)
     __lowerCamelCase     :			List[Any]              =	machine_time % m

     # Evolution (Time Length)
     for _ in range(0   ,					UpperCAmelCase_							):
          # Variables (Position + Parameters)
          __lowerCamelCase     :			Union[str, Any]              =	params_space[key]
          __lowerCamelCase     :			Optional[Any]              =	buffer_space[key]

          # Modification (Transition Function) - Flow
          __lowerCamelCase     :			str              =	round(float(r * value * (1 - value)							)   ,					10							)
          __lowerCamelCase     :			Dict              =	(machine_time * 0.01 + r * 1.01) % 1 + 3

     # Choosing Chaotic Data
     __lowerCamelCase     :			List[Any]              =	int(buffer_space[(key + 2) % m] * (10**10)							)
     __lowerCamelCase     :			List[str]              =	int(buffer_space[(key - 2) % m] * (10**10)							)

     # Machine Time
     machine_time += 1

     return xorshift(UpperCAmelCase_   ,					UpperCAmelCase_							) % 0xff_ff_ff_ff



def     UpperCAmelCase__      (			)  ->	Any:
     global buffer_space, params_space, machine_time, K, m, t

     __lowerCamelCase     :			Union[str, Any]              =	K
     __lowerCamelCase     :			Optional[int]              =	[0] * m
     __lowerCamelCase     :			Tuple              =	0


if __name__ == "__main__":
      # Initialization
      reset()

      # Pushing Data (Input)
      import random

      A__	:      Optional[Any]				=						random.sample(range(0XFFFFFFFF), 100)
      for chunk in message:
            push(chunk)

      # for controlling
      A__	:      Tuple				=						""""""

      # Pulling Data (Output)
      while inp in ("e", "E"):
            print(f'''{format(pull(), '#04x')}''')
            print(buffer_space)
            print(params_space)
            A__	:      Optional[Any]				=						input("""(e)exit? """).strip()






