
import unittest

import numpy as np

from transformers import (
    GPTSanJapaneseConfig,
    GPTSanJapaneseForConditionalGeneration,
    GPTSanJapaneseModel,
    GPTSanJapaneseTokenizer,
    is_torch_available,
)
from transformers.generation import GenerationConfig
from transformers.testing_utils import require_torch, slow, tooslow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin






class 						UpperCAmelCase	:



   def __init__(self	:      Dict					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      Union[str, Any]=3_60_00					,		snake_case__	:      Any=13					,		snake_case__	:      Tuple=7					,		snake_case__	:      str=True					,		snake_case__	:      Dict=32					,		snake_case__	:      Any=42					,		snake_case__	:      str=5					,		snake_case__	:      int=2					,		snake_case__	:      Dict=4					,		snake_case__	:      int=2					,		snake_case__	:      Optional[Any]=32					,		snake_case__	:      List[Any]=80					,		snake_case__	:      Tuple=33					,		snake_case__	:      Tuple=0.0					,		snake_case__	:      Any=1e-6					,		snake_case__	:      Tuple=1_00					,		snake_case__	:      Optional[int]=0.0					,		)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   vocab_size
          snake_case :     Optional[int]           =   parent
          snake_case :     List[Any]           =   batch_size
          snake_case :     Optional[Any]           =   num_contexts
          # For common tests
          snake_case :     int           =   self.num_contexts
          snake_case :     List[Any]           =   is_training
          snake_case :     Tuple           =   hidden_size
          snake_case :     str           =   num_ext_layers
          snake_case :     List[Any]           =   ext_size
          snake_case :     Tuple           =   num_hidden_layers
          snake_case :     str           =   num_attention_heads
          snake_case :     int           =   num_experts
          snake_case :     Dict           =   d_ff
          snake_case :     Any           =   d_ext
          snake_case :     List[Any]           =   d_spout
          snake_case :     Optional[Any]           =   dropout_rate
          snake_case :     str           =   layer_norm_epsilon
          snake_case :     Dict           =   expert_capacity
          snake_case :     Union[str, Any]           =   router_jitter_noise



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			Dict:


          '''simple docstring'''


          return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"				)



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     str           =   ids_tensor([self.batch_size, self.seq_length]					,		self.vocab_size				)

          snake_case :     List[str]           =   self.get_config()

          return (config, input_ids)



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]				)	->			str:


          '''simple docstring'''


          snake_case :     Any           =   ids_tensor([self.batch_size, self.seq_length]					,		self.vocab_size				)

          snake_case :     Tuple           =   self.get_config()

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



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Optional[Any]:


          '''simple docstring'''


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



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]					,		snake_case__	:      Optional[Any]					,		snake_case__	:      Optional[int]					,		)	->			int:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   GPTSanJapaneseForConditionalGeneration(config=snake_case__				)
          model.to(snake_case__				)
          model.eval()
          snake_case :     Dict           =   model(
              input_ids=snake_case__					,		)
          self.parent.assertIsNotNone(snake_case__				)






@require_torch
class 						UpperCAmelCase	(		A_ ,A_ ,unittest.TestCase				):
   A__      :	int		  =  (GPTSanJapaneseModel,) if is_torch_available() else ()
   A__      :	Optional[int]		  =  (
       {
           "conversational": GPTSanJapaneseForConditionalGeneration,
           "feature-extraction": GPTSanJapaneseForConditionalGeneration,
           "summarization": GPTSanJapaneseForConditionalGeneration,
           "text2text-generation": GPTSanJapaneseForConditionalGeneration,
           "translation": GPTSanJapaneseForConditionalGeneration,
       }
       if is_torch_available()
       else {}
   )
   A__      :	Dict		  =  False
   A__      :	Optional[int]		  =  False
   A__      :	Any		  =  False
   A__      :	List[str]		  =  False
   A__      :	Dict		  =  False
   A__      :	Optional[Any]		  =  False
   A__      :	str		  =  False
   A__      :	int		  =  False
   # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
   A__      :	Optional[Any]		  =  [0.8, 0.9]



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      Any					,		snake_case__	:      Optional[int]					,		snake_case__	:      Any				)	->			Any:


          '''simple docstring'''


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

          return False



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   GPTSanJapaneseTester(self				)
          snake_case :     Union[str, Any]           =   ConfigTester(self					,		config_class=snake_case__					,		d_model=37				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			Dict:


          '''simple docstring'''


          GPTSanJapaneseConfig()



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     str           =   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        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			Union[str, Any]:


          '''simple docstring'''


          super().test_model_parallelism()



   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Dict:


          '''simple docstring'''


          pass






@require_torch
class 						UpperCAmelCase	(		A_ ,A_ ,unittest.TestCase				):
   A__      :	Dict		  =  (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
   A__      :	Union[str, Any]		  =  False
   A__      :	Union[str, Any]		  =  False
   A__      :	Optional[Any]		  =  False
   A__      :	Tuple		  =  False
   A__      :	Union[str, Any]		  =  False
   A__      :	Tuple		  =  False
   # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
   A__      :	str		  =  [0.8, 0.9]



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     str           =   GPTSanJapaneseTester(self				)
          snake_case :     List[str]           =   ConfigTester(self					,		config_class=snake_case__					,		d_model=37				)



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			List[Any]:


          '''simple docstring'''


          GPTSanJapaneseConfig()



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]				)	->			Tuple:


          '''simple docstring'''


          snake_case :     int           =   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        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Tuple:


          '''simple docstring'''


          super().test_model_parallelism()



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


          '''simple docstring'''


          pass



   @slow
   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]				)	->			Dict:


          '''simple docstring'''


          snake_case :     Tuple           =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"				)
          snake_case :     Optional[int]           =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"				)
          snake_case :     Any           =   tokenizer.encode("武田信玄は"					,		return_tensors="pt"				)
          snake_case :     Union[str, Any]           =   model(snake_case__				)
          snake_case :     Dict           =   outputs.logits.detach().cpu().numpy()
          # Output of original model created with mesh-tensoflow
          snake_case :     Tuple           =   [
              # 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
          ]
          snake_case :     Union[str, Any]           =   np.array(snake_case__				).flatten()
          snake_case :     Tuple           =   output_logits[0, :, :20].flatten()

          def check(snake_case__	:      Optional[Any]					,		snake_case__	:      List[Any]					,		snake_case__	:      int=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        _SCREAMING_SNAKE_CASE     (self	:      List[Any]				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     List[str]           =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"				)
          snake_case :     List[str]           =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"				)
          model.to(snake_case__				)

          # set deterministically
          snake_case :     int           =   GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"				)
          snake_case :     Dict           =   1

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

          snake_case :     Optional[Any]           =   "left"
          snake_case :     str           =   tokenizer(snake_case__					,		return_tensors="pt"					,		padding=snake_case__				)
          snake_case :     List[Any]           =   inputs["input_ids"].to(snake_case__				)

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

          snake_case :     int           =   model.generate(
              input_ids=snake_case__					,		attention_mask=inputs["attention_mask"].to(snake_case__				)					,		max_new_tokens=3					,		generation_config=snake_case__					,		)

          snake_case :     Union[str, Any]           =   tokenizer(sentences[0]					,		return_tensors="pt"				).input_ids.to(snake_case__				)
          snake_case :     Union[str, Any]           =   model.generate(
              input_ids=snake_case__					,		max_new_tokens=3					,		generation_config=snake_case__				)

          snake_case :     Union[str, Any]           =   tokenizer(sentences[1]					,		return_tensors="pt"				).input_ids.to(snake_case__				)
          snake_case :     List[str]           =   model.generate(input_ids=snake_case__					,		max_new_tokens=3					,		generation_config=snake_case__				)

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

          snake_case :     Union[str, Any]           =   tokenizer.batch_decode(snake_case__					,		skip_special_tokens=snake_case__				)
          snake_case :     Union[str, Any]           =   tokenizer.decode(output_non_padded[0]					,		skip_special_tokens=snake_case__				)
          snake_case :     List[Any]           =   tokenizer.decode(output_padded[0]					,		skip_special_tokens=snake_case__				)

          snake_case :     Dict           =   [
              "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        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     List[Any]           =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"				)
          snake_case :     Optional[int]           =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"				)
          # Output of original model created with mesh-tensoflow
          snake_case :     Optional[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:
                 snake_case :     List[str]           =   tokenizer.encode(snake_case__					,		return_tensors="pt"				)
                 snake_case :     Tuple           =   model(snake_case__				)
                 snake_case :     Dict           =   outputs.logits.detach().cpu().numpy()[0]
                 snake_case :     str           =   np.argmax(output_logits[-1]				)
                 self.assertEqual(snake_case__					,		snake_case__				)



   @slow
   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     Optional[int]           =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"				)
          snake_case :     Optional[int]           =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"				)
          model.to(snake_case__				)

          # set deterministically
          snake_case :     str           =   GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"				)
          snake_case :     Dict           =   1

          snake_case :     Dict           =   "武田信玄は、"
          snake_case :     Any           =   tokenizer(snake_case__					,		return_tensors="pt"				).input_ids.to(snake_case__				)
          snake_case :     Union[str, Any]           =   tokenizer([input_text, input_text]					,		return_tensors="pt"				).input_ids.to(snake_case__				)

          # spout from uniform and one-hot
          snake_case :     List[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
          ]

          snake_case :     str           =   model.generate(
              input_ids=snake_case__					,		spout=spouts[0]					,		max_new_tokens=20					,		generation_config=snake_case__					,		)

          snake_case :     Dict           =   model.generate(
              input_ids=snake_case__					,		spout=spouts[1]					,		max_new_tokens=20					,		generation_config=snake_case__					,		)

          snake_case :     Optional[int]           =   model.generate(
              input_ids=snake_case__					,		spout=snake_case__					,		max_new_tokens=20					,		generation_config=snake_case__					,		)

          snake_case :     Union[str, Any]           =   tokenizer.decode(outputa[0]				)
          snake_case :     str           =   tokenizer.decode(outputa[0]				)
          snake_case :     Any           =   tokenizer.batch_decode(snake_case__				)

          snake_case :     List[str]           =   [
              "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        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"				)
          snake_case :     Optional[int]           =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"				)
          model.to(snake_case__				)

          # set deterministically
          snake_case :     Dict           =   GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"				)
          snake_case :     List[Any]           =   1

          snake_case :     str           =   "武田信玄"
          snake_case :     Optional[int]           =   "織田信長"
          snake_case :     str           =   "は、"
          snake_case :     List[Any]           =   "が、"
          snake_case :     Dict           =   tokenizer(snake_case__					,		prefix_text=snake_case__					,		return_tensors="pt"				)
          snake_case :     int           =   tokenizer(snake_case__					,		prefix_text=snake_case__					,		return_tensors="pt"				)
          snake_case :     str           =   tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]					,		return_tensors="pt"				)

          snake_case :     Dict           =   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__					,		)

          snake_case :     List[Any]           =   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__					,		)

          snake_case :     Optional[Any]           =   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__					,		)

          snake_case :     Optional[int]           =   tokenizer.decode(outputa[0]				)
          snake_case :     Optional[int]           =   tokenizer.decode(outputa[0]				)
          snake_case :     int           =   tokenizer.batch_decode(snake_case__				)

          snake_case :     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(snake_case__					,		snake_case__				)
          self.assertListEqual(snake_case__					,		[outa_sentence, outa_sentence]				)







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class 						UpperCAmelCase	(		A_				):



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]					,		snake_case__	:      Dict					,		snake_case__	:      Tuple					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]				)	->			Any:


          '''simple docstring'''


          return True



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Dict:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"				)
          snake_case :     str           =   "Hugging Face大法好，谁用谁知道。"
          snake_case :     Optional[Any]           =   "Hugging Face大法好,谁用谁知道。<unk>"
          snake_case :     int           =   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

          snake_case :     Union[str, Any]           =   tokenizer.tokenize(snake_case__				)
          self.assertListEqual(snake_case__					,		snake_case__				)

          snake_case :     Tuple           =   tokens + [tokenizer.unk_token]

          snake_case :     int           =   [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)					,		snake_case__				)

          snake_case :     Dict           =   tokenizer.decode(snake_case__				)
          self.assertEqual(snake_case__					,		snake_case__				)





