



'''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  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     int      ,     _a				:     int      ,     _a				:     str=3_6000      ,     _a				:     Dict=13      ,     _a				:     str=7      ,     _a				:     Optional[Any]=True      ,     _a				:     Optional[int]=32      ,     _a				:     Any=42      ,     _a				:     Tuple=5      ,     _a				:     Optional[Any]=2      ,     _a				:     List[str]=4      ,     _a				:     List[Any]=2      ,     _a				:     List[str]=32      ,     _a				:     Any=80      ,     _a				:     Union[str, Any]=33      ,     _a				:     Optional[Any]=0.0      ,     _a				:     Any=1e-6      ,     _a				:     Tuple=100      ,     _a				:     List[Any]=0.0      ,     ) -> Union[str, Any]:
    __lowerCamelCase	:	Tuple   	=			vocab_size
    __lowerCamelCase	:	str   	=			parent
    __lowerCamelCase	:	Dict   	=			batch_size
    __lowerCamelCase	:	Dict   	=			num_contexts
    # For common tests
    __lowerCamelCase	:	Any   	=			self.num_contexts
    __lowerCamelCase	:	Any   	=			is_training
    __lowerCamelCase	:	Optional[int]   	=			hidden_size
    __lowerCamelCase	:	int   	=			num_ext_layers
    __lowerCamelCase	:	List[str]   	=			ext_size
    __lowerCamelCase	:	Optional[int]   	=			num_hidden_layers
    __lowerCamelCase	:	Any   	=			num_attention_heads
    __lowerCamelCase	:	Dict   	=			num_experts
    __lowerCamelCase	:	Dict   	=			d_ff
    __lowerCamelCase	:	Optional[Any]   	=			d_ext
    __lowerCamelCase	:	List[Any]   	=			d_spout
    __lowerCamelCase	:	Union[str, Any]   	=			dropout_rate
    __lowerCamelCase	:	Any   	=			layer_norm_epsilon
    __lowerCamelCase	:	Any   	=			expert_capacity
    __lowerCamelCase	:	List[Any]   	=			router_jitter_noise




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




  def 						_lowercase					(			self				:     Tuple       ) -> List[str]:
    __lowerCamelCase	:	Optional[Any]   	=			ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size       )

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

    return (config, input_ids)




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

    __lowerCamelCase	:	int   	=			self.get_config()

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




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






@require_torch
class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       unittest.TestCase					):
  """simple docstring"""




  a_							 =(GPTSanJapaneseModel,) if is_torch_available() else ()
  a_							 =(
      {
          """conversational""": GPTSanJapaneseForConditionalGeneration,
          """feature-extraction""": GPTSanJapaneseForConditionalGeneration,
          """summarization""": GPTSanJapaneseForConditionalGeneration,
          """text2text-generation""": GPTSanJapaneseForConditionalGeneration,
          """translation""": GPTSanJapaneseForConditionalGeneration,
      }
      if is_torch_available()
      else {}
  )
  a_							 =False
  a_							 =False
  a_							 =False
  a_							 =False
  a_							 =False
  a_							 =False
  a_							 =False
  a_							 =False
  # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  a_							 =[0.8, 0.9]




  def 						_lowercase					(			self				:     List[Any]      ,     _a				:     List[str]      ,     _a				:     Any      ,     _a				:     Optional[int]      ,     _a				:     List[Any]      ,     _a				:     int       ) -> Tuple:
    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				:     Any       ) -> Any:
    __lowerCamelCase	:	Dict   	=			GPTSanJapaneseTester(self       )
    __lowerCamelCase	:	int   	=			ConfigTester(self      ,     config_class=_a      ,     d_model=37       )




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




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




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






@require_torch
class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       unittest.TestCase					):
  """simple docstring"""




  a_							 =(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
  a_							 =False
  a_							 =False
  a_							 =False
  a_							 =False
  a_							 =False
  a_							 =False
  # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  a_							 =[0.8, 0.9]




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




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




  def 						_lowercase					(			self				:     Optional[Any]       ) -> List[str]:
    __lowerCamelCase	:	str   	=			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				:     Optional[Any]       ) -> Optional[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				:     Any       ) -> int:
    pass




  @slow
  def 						_lowercase					(			self				:     Dict       ) -> Dict:
    __lowerCamelCase	:	Dict   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	Any   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	str   	=			tokenizer.encode('武田信玄は'      ,     return_tensors='pt'       )
    __lowerCamelCase	:	Optional[int]   	=			model(_a       )
    __lowerCamelCase	:	Optional[Any]   	=			outputs.logits.detach().cpu().numpy()
    # Output of original model created with mesh-tensoflow
    __lowerCamelCase	:	List[str]   	=			[
        # fmt: off
        [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
        -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
        -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
        -12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
        [-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
        -7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
        -6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
        -8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
        [-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
        -11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
        -9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
        -10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
        [-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
        -9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
        -8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
        -11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
        [-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
        -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
        -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
        -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
        # fmt: on
    ]
    __lowerCamelCase	:	Tuple   	=			np.array(_a       ).flatten()
    __lowerCamelCase	:	Tuple   	=			output_logits[0, :, :20].flatten()

    def check(_a				:     Optional[Any]      ,     _a				:     str      ,     _a				:     int=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]       ) -> List[Any]:
    __lowerCamelCase	:	List[str]   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	List[str]   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    model.to(_a       )

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

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

    __lowerCamelCase	:	Optional[Any]   	=			'left'
    __lowerCamelCase	:	str   	=			tokenizer(_a      ,     return_tensors='pt'      ,     padding=_a       )
    __lowerCamelCase	:	Dict   	=			inputs['input_ids'].to(_a       )

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

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

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

    __lowerCamelCase	:	str   	=			tokenizer(sentences[1]      ,     return_tensors='pt'       ).input_ids.to(_a       )
    __lowerCamelCase	:	Tuple   	=			model.generate(input_ids=_a      ,     max_new_tokens=3      ,     generation_config=_a       )

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

    __lowerCamelCase	:	Union[str, Any]   	=			tokenizer.batch_decode(_a      ,     skip_special_tokens=_a       )
    __lowerCamelCase	:	int   	=			tokenizer.decode(output_non_padded[0]      ,     skip_special_tokens=_a       )
    __lowerCamelCase	:	int   	=			tokenizer.decode(output_padded[0]      ,     skip_special_tokens=_a       )

    __lowerCamelCase	:	str   	=			[
        '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				:     int       ) -> Tuple:
    __lowerCamelCase	:	Any   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	Optional[int]   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    # Output of original model created with mesh-tensoflow
    __lowerCamelCase	:	Union[str, Any]   	=			[
        ('武田信玄は', 3_5675),
        ('武田信玄は、', 45),
        ('武田信玄は、この', 29),
        ('武田信玄は、このよう', 3_0642),
        ('武田信玄は、このような', 3_5680),
        ('武田信玄は、このような「', 8640),
        ('武田信玄は、このような「武田', 3_1617),
        ('武田信玄は、このような「武田家', 3_0646),
        ('武田信玄は、このような「武田家の', 3_1617),
        ('武田信玄は、このような「武田家の家', 3_1381),
    ]
    for input, output in target:
      __lowerCamelCase	:	Dict   	=			tokenizer.encode(_a      ,     return_tensors='pt'       )
      __lowerCamelCase	:	Dict   	=			model(_a       )
      __lowerCamelCase	:	Optional[int]   	=			outputs.logits.detach().cpu().numpy()[0]
      __lowerCamelCase	:	List[str]   	=			np.argmax(output_logits[-1]       )
      self.assertEqual(_a      ,     _a       )




  @slow
  def 						_lowercase					(			self				:     Optional[int]       ) -> List[str]:
    __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	:	Tuple   	=			GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	Dict   	=			1

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

    # spout from uniform and one-hot
    __lowerCamelCase	:	Optional[Any]   	=			[
        # fmt: off
        [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
        0.04803985, 0.211572  , 0.23188473, 0.37153068, 0.7836377 ,
        0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
        0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
        0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
        0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
        0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
        0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
        0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
        0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
        0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
        0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
        0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363  ,
        0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
        0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
        0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
        0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
        0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
        0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
        0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
        0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
        0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
        0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
        0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
        0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
        0.9552151 , 0.93901511, 0.22489288], # 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	:	Dict   	=			model.generate(
        input_ids=_a      ,     spout=spouts[0]      ,     max_new_tokens=20      ,     generation_config=_a      ,     )

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

    __lowerCamelCase	:	Any   	=			[
        '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				:     List[Any]       ) -> Union[str, Any]:
    __lowerCamelCase	:	int   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	int   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    model.to(_a       )

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

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

    __lowerCamelCase	:	Tuple   	=			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	:	Tuple   	=			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	:	Tuple   	=			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	:	Tuple   	=			tokenizer.decode(outputa[0]       )
    __lowerCamelCase	:	Dict   	=			tokenizer.decode(outputa[0]       )
    __lowerCamelCase	:	Optional[int]   	=			tokenizer.batch_decode(_a       )

    __lowerCamelCase	:	str   	=			[
        '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'''





from __future__ import annotations

from collections.abc import Iterable






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     int      ,     _a				:     Dict      ,     _a				:     Optional[Any]       ) -> str:
    __lowerCamelCase	,__lowerCamelCase	:	List[Any]   	=			float(_a       ), float(_a       )




  def __eq__(			self				:     Tuple      ,     _a				:     List[Any]       ) -> Optional[int]:
    return self.x == other.x and self.y == other.y




  def __ne__(			self				:     Tuple      ,     _a				:     Optional[int]       ) -> List[str]:
    return not self == other




  def __gt__(			self				:     Tuple      ,     _a				:     str       ) -> str:
    if self.x > other.x:
      return True
    elif self.x == other.x:
      return self.y > other.y
    return False




  def __lt__(			self				:     Union[str, Any]      ,     _a				:     List[Any]       ) -> Optional[Any]:
    return not self > other




  def __ge__(			self				:     Tuple      ,     _a				:     Optional[Any]       ) -> Optional[int]:
    if self.x > other.x:
      return True
    elif self.x == other.x:
      return self.y >= other.y
    return False




  def __le__(			self				:     Any      ,     _a				:     List[str]       ) -> Optional[int]:
    if self.x < other.x:
      return True
    elif self.x == other.x:
      return self.y <= other.y
    return False




  def __repr__(			self				:     int       ) -> Optional[int]:
    return f'({self.x}, {self.y})'




  def __hash__(			self				:     Optional[Any]       ) -> Union[str, Any]:
    return hash(self.x       )




def 	a_					(							_lowerCAmelCase	,)				->				list[Point]:
  __lowerCamelCase	:	list[Point]   	=			[]
  if list_of_tuples:
    for p in list_of_tuples:
      if isinstance(_lowerCAmelCase	,_lowerCAmelCase	):
        points.append(_lowerCAmelCase	)
      else:
        try:
          points.append(Point(p[0]	,p[1]	)	)
        except (IndexError, TypeError):
          print(
              F'Ignoring deformed point {p}. All points'
              ' must have at least 2 coordinates.'	)
  return points

def 	a_					(							_lowerCAmelCase	)				->				list[Point]:

  if not hasattr(_lowerCAmelCase	,'__iter__'	):
    __lowerCamelCase	:	str   	=			F'Expecting an iterable object but got an non-iterable type {points}'
    raise ValueError(_lowerCAmelCase	)

  if not points:
    __lowerCamelCase	:	Any   	=			F'Expecting a list of points but got {points}'
    raise ValueError(_lowerCAmelCase	)

  return _construct_points(_lowerCAmelCase	)

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				float:
  __lowerCamelCase	:	Optional[int]   	=			(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
  return det

def 	a_					(							_lowerCAmelCase	)				->				list[Point]:
  __lowerCamelCase	:	Optional[Any]   	=			sorted(_validate_input(_lowerCAmelCase	)	)
  __lowerCamelCase	:	Optional[int]   	=			len(_lowerCAmelCase	)
  __lowerCamelCase	:	Union[str, Any]   	=			set()

  for i in range(n - 1	):
    for j in range(i + 1	,_lowerCAmelCase	):
      __lowerCamelCase	:	List[Any]   	=			False
      __lowerCamelCase	:	Union[str, Any]   	=			True
      for k in range(_lowerCAmelCase	):
        if k != i and k != j:
          __lowerCamelCase	:	Any   	=			_det(points[i]	,points[j]	,points[k]	)

          if det_k > 0:
            __lowerCamelCase	:	List[Any]   	=			True
          elif det_k < 0:
            __lowerCamelCase	:	Any   	=			True
          else:
            # point[i], point[j], point[k] all lie on a straight line
            # if point[k] is to the left of point[i] or it's to the
            # right of point[j], then point[i], point[j] cannot be
            # part of the convex hull of A
            if points[k] < points[i] or points[k] > points[j]:
              __lowerCamelCase	:	Dict   	=			False
              break

        if points_left_of_ij and points_right_of_ij:
          __lowerCamelCase	:	int   	=			False
          break

      if ij_part_of_convex_hull:
        convex_set.update([points[i], points[j]]	)

  return sorted(_lowerCAmelCase	)

def 	a_					(							_lowerCAmelCase	)				->				list[Point]:
  __lowerCamelCase	:	Dict   	=			sorted(_validate_input(_lowerCAmelCase	)	)
  __lowerCamelCase	:	Optional[Any]   	=			len(_lowerCAmelCase	)

  # divide all the points into an upper hull and a lower hull
  # the left most point and the right most point are definitely
  # members of the convex hull by definition.
  # use these two anchors to divide all the points into two hulls,
  # an upper hull and a lower hull.

  # all points to the left (above) the line joining the extreme points belong to the
  # upper hull
  # all points to the right (below) the line joining the extreme points below to the
  # lower hull
  # ignore all points on the line joining the extreme points since they cannot be
  # part of the convex hull

  __lowerCamelCase	:	str   	=			points[0]
  __lowerCamelCase	:	Optional[Any]   	=			points[n - 1]

  __lowerCamelCase	:	Tuple   	=			{left_most_point, right_most_point}
  __lowerCamelCase	:	int   	=			[]
  __lowerCamelCase	:	Tuple   	=			[]

  for i in range(1	,n - 1	):
    __lowerCamelCase	:	Dict   	=			_det(_lowerCAmelCase	,_lowerCAmelCase	,points[i]	)

    if det > 0:
      upper_hull.append(points[i]	)
    elif det < 0:
      lower_hull.append(points[i]	)

  _construct_hull(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)
  _construct_hull(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)

  return sorted(_lowerCAmelCase	)

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				None:
  if points:
    __lowerCamelCase	:	Optional[int]   	=			None
    __lowerCamelCase	:	Optional[Any]   	=			float('-inf'	)
    __lowerCamelCase	:	Optional[int]   	=			[]

    for p in points:
      __lowerCamelCase	:	List[str]   	=			_det(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)

      if det > 0:
        candidate_points.append(_lowerCAmelCase	)

        if det > extreme_point_distance:
          __lowerCamelCase	:	Union[str, Any]   	=			det
          __lowerCamelCase	:	Dict   	=			p

    if extreme_point:
      _construct_hull(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)
      convex_set.add(_lowerCAmelCase	)
      _construct_hull(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)

def 	a_					(							_lowerCAmelCase	)				->				list[Point]:
  __lowerCamelCase	:	Optional[int]   	=			sorted(_validate_input(_lowerCAmelCase	)	)
  __lowerCamelCase	:	List[Any]   	=			len(_lowerCAmelCase	)

  __lowerCamelCase	:	Tuple   	=			points[:2]
  for i in range(2	,_lowerCAmelCase	):
    __lowerCamelCase	:	Tuple   	=			_det(convex_hull[1]	,convex_hull[0]	,points[i]	)
    if det > 0:
      convex_hull.insert(0	,points[i]	)
      break
    elif det < 0:
      convex_hull.append(points[i]	)
      break
    else:
      __lowerCamelCase	:	Optional[int]   	=			points[i]
  i += 1

  for j in range(_lowerCAmelCase	,_lowerCAmelCase	):
    if (
        _det(convex_hull[0]	,convex_hull[-1]	,points[j]	) > 0
        and _det(convex_hull[-1]	,convex_hull[0]	,points[1]	) < 0
    ):
      # The point lies within the convex hull
      continue

    convex_hull.insert(0	,points[j]	)
    convex_hull.append(points[j]	)
    while _det(convex_hull[0]	,convex_hull[1]	,convex_hull[2]	) >= 0:
      del convex_hull[1]
    while _det(convex_hull[-1]	,convex_hull[-2]	,convex_hull[-3]	) <= 0:
      del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
  return sorted(convex_hull[1:] if len(_lowerCAmelCase	) > 3 else convex_hull	)

def 	a_					(							)				->				Optional[int]:
  __lowerCamelCase	:	List[Any]   	=			[
      (0, 3),
      (2, 2),
      (1, 1),
      (2, 1),
      (3, 0),
      (0, 0),
      (3, 3),
      (2, -1),
      (2, -4),
      (1, -3),
  ]
  # the convex set of points is
  # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
  __lowerCamelCase	:	Any   	=			convex_hull_bf(_lowerCAmelCase	)

  __lowerCamelCase	:	Optional[int]   	=			convex_hull_recursive(_lowerCAmelCase	)
  assert results_bf == results_recursive

  __lowerCamelCase	:	int   	=			convex_hull_melkman(_lowerCAmelCase	)
  assert results_bf == results_melkman

  print(_lowerCAmelCase	)


if __name__ == "__main__":
   main()






