



"""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    a		:



  def __init__(  self	:   Union[str, Any]    ,						lowerCAmelCase	:   Any    ,						lowerCAmelCase	:   Tuple=3_6000    ,						lowerCAmelCase	:   List[Any]=13    ,						lowerCAmelCase	:   Dict=7    ,						lowerCAmelCase	:   str=True    ,						lowerCAmelCase	:   str=32    ,						lowerCAmelCase	:   str=42    ,						lowerCAmelCase	:   Tuple=5    ,						lowerCAmelCase	:   Dict=2    ,						lowerCAmelCase	:   Dict=4    ,						lowerCAmelCase	:   Union[str, Any]=2    ,						lowerCAmelCase	:   int=32    ,						lowerCAmelCase	:   Any=80    ,						lowerCAmelCase	:   Tuple=33    ,						lowerCAmelCase	:   Tuple=0.0    ,						lowerCAmelCase	:   Union[str, Any]=1E-6    ,						lowerCAmelCase	:   Union[str, Any]=100    ,						lowerCAmelCase	:   Dict=0.0    ,						)       ->   List[Any]:


     '''simple docstring'''




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



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


     '''simple docstring'''




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



  def 		lowerCamelCase__    (  self	:   int						)       ->   Any:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.get_config()

     return (config, input_ids)



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size						)

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

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



  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   int:


     '''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 		lowerCamelCase__    (  self	:   int    ,						lowerCAmelCase	:   Dict    ,						lowerCAmelCase	:   int    ,						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =GPTSanJapaneseForConditionalGeneration(config=lowercase__						)
     model.to(lowercase__						)
     model.eval()
     SCREAMING_SNAKE_CASE_:	Optional[int]            =model(
         input_ids=lowercase__    ,						)
     self.parent.assertIsNotNone(lowercase__						)







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



  def 		lowerCamelCase__    (  self	:   int    ,						lowerCAmelCase	:   Optional[Any]    ,						lowerCAmelCase	:   Optional[int]    ,						lowerCAmelCase	:   List[str]    ,						lowerCAmelCase	:   Union[str, Any]    ,						lowerCAmelCase	:   Tuple						)       ->   Dict:


     '''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 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =GPTSanJapaneseTester(self						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =ConfigTester(self    ,						config_class=lowercase__    ,						d_model=37						)



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


     '''simple docstring'''




     GPTSanJapaneseConfig()



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =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 		lowerCamelCase__    (  self	:   Dict						)       ->   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 		lowerCamelCase__    (  self	:   str						)       ->   Dict:


     '''simple docstring'''




     pass







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



  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =GPTSanJapaneseTester(self						)
     SCREAMING_SNAKE_CASE_:	List[str]            =ConfigTester(self    ,						config_class=lowercase__    ,						d_model=37						)



  def 		lowerCamelCase__    (  self	:   List[Any]						)       ->   List[Any]:


     '''simple docstring'''




     GPTSanJapaneseConfig()



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[Any]            =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 		lowerCamelCase__    (  self	:   Any						)       ->   int:


     '''simple docstring'''




     super().test_model_parallelism()



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


     '''simple docstring'''




     pass



  @slow
  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   List[Any]:


     '''simple docstring'''




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

     def check(lowerCAmelCase	:   Optional[int]    ,						lowerCAmelCase	:   Union[str, Any]    ,						lowerCAmelCase	:   List[Any]=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 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""						)
     model.to(lowercase__						)

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

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

     SCREAMING_SNAKE_CASE_:	List[Any]            ="left"
     SCREAMING_SNAKE_CASE_:	Optional[int]            =tokenizer(lowercase__    ,						return_tensors="""pt"""    ,						padding=lowercase__						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =inputs["input_ids"].to(lowercase__						)

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

     SCREAMING_SNAKE_CASE_:	Any            =model.generate(
         input_ids=lowercase__    ,						attention_mask=inputs["""attention_mask"""].to(lowercase__						)    ,						max_new_tokens=3    ,						generation_config=lowercase__    ,						)

     SCREAMING_SNAKE_CASE_:	str            =tokenizer(sentences[0]    ,						return_tensors="""pt"""						).input_ids.to(lowercase__						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =model.generate(
         input_ids=lowercase__    ,						max_new_tokens=3    ,						generation_config=lowercase__						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =tokenizer(sentences[1]    ,						return_tensors="""pt"""						).input_ids.to(lowercase__						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =model.generate(input_ids=lowercase__    ,						max_new_tokens=3    ,						generation_config=lowercase__						)

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

     SCREAMING_SNAKE_CASE_:	Dict            =tokenizer.batch_decode(lowercase__    ,						skip_special_tokens=lowercase__						)
     SCREAMING_SNAKE_CASE_:	str            =tokenizer.decode(output_non_padded[0]    ,						skip_special_tokens=lowercase__						)
     SCREAMING_SNAKE_CASE_:	Any            =tokenizer.decode(output_padded[0]    ,						skip_special_tokens=lowercase__						)

     SCREAMING_SNAKE_CASE_:	Union[str, 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(lowercase__    ,						lowercase__						)
     self.assertListEqual(lowercase__    ,						[non_padded_sentence, padded_sentence]						)



  @tooslow
  def 		lowerCamelCase__    (  self	:   Any						)       ->   List[str]:


     '''simple docstring'''




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



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""						)
     model.to(lowercase__						)

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

     SCREAMING_SNAKE_CASE_:	Tuple            ="武田信玄は、"
     SCREAMING_SNAKE_CASE_:	int            =tokenizer(lowercase__    ,						return_tensors="""pt"""						).input_ids.to(lowercase__						)
     SCREAMING_SNAKE_CASE_:	Any            =tokenizer([input_text, input_text]    ,						return_tensors="""pt"""						).input_ids.to(lowercase__						)

     # spout from uniform and one-hot
     SCREAMING_SNAKE_CASE_:	Any            =[
         # 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
     ]

     SCREAMING_SNAKE_CASE_:	int            =model.generate(
         input_ids=lowercase__    ,						spout=spouts[0]    ,						max_new_tokens=20    ,						generation_config=lowercase__    ,						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =model.generate(
         input_ids=lowercase__    ,						spout=spouts[1]    ,						max_new_tokens=20    ,						generation_config=lowercase__    ,						)

     SCREAMING_SNAKE_CASE_:	Dict            =model.generate(
         input_ids=lowercase__    ,						spout=lowercase__    ,						max_new_tokens=20    ,						generation_config=lowercase__    ,						)

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

     SCREAMING_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(lowercase__    ,						lowercase__						)
     self.assertListEqual(lowercase__    ,						[outa_sentence, outa_sentence]						)





  @slow
  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""						)
     SCREAMING_SNAKE_CASE_:	List[str]            =GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""						)
     model.to(lowercase__						)

     # set deterministically
     SCREAMING_SNAKE_CASE_:	List[str]            =GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =1

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

     SCREAMING_SNAKE_CASE_:	Any            =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=20    ,						generation_config=lowercase__    ,						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =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=20    ,						generation_config=lowercase__    ,						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =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=20    ,						generation_config=lowercase__    ,						)

     SCREAMING_SNAKE_CASE_:	List[Any]            =tokenizer.decode(outputa[0]						)
     SCREAMING_SNAKE_CASE_:	str            =tokenizer.decode(outputa[0]						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =tokenizer.batch_decode(lowercase__						)

     SCREAMING_SNAKE_CASE_:	Tuple            =[
         "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"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase       =     logging.get_logger(__name__)


_UpperCAmelCase       =     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
_UpperCAmelCase       =     {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
}

_UpperCAmelCase       =     {
    """RUCAIBox/mvp""": 1_0_2_4,
}





@lru_cache()
def        __magic_name__			(							):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =(
       list(range(ord("""!"""	)							,     ord("""~"""	) + 1	)	) + list(range(ord("""¡"""	)							,     ord("""¬"""	) + 1	)	) + list(range(ord("""®"""	)							,     ord("""ÿ"""	) + 1	)	)
   )
   SCREAMING_SNAKE_CASE_:	Any            =bs[:]
   SCREAMING_SNAKE_CASE_:	str            =0
   for b in range(2**8	):
      if b not in bs:
         bs.append(lowercase	)
         cs.append(2**8 + n	)
         n += 1
   SCREAMING_SNAKE_CASE_:	List[Any]            =[chr(lowercase	) for n in cs]
   return dict(zip(lowercase							,     lowercase	)	)





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[int]            =set()
   SCREAMING_SNAKE_CASE_:	Tuple            =word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)	)
      SCREAMING_SNAKE_CASE_:	Dict            =char
   return pairs
class    a		(					UpperCAmelCase__    ):
  UpperCamelCase		:			str         =   VOCAB_FILES_NAMES
  UpperCamelCase		:			List[Any]         =   PRETRAINED_VOCAB_FILES_MAP
  UpperCamelCase		:			Optional[Any]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCamelCase		:			Optional[int]         =   ['input_ids', 'attention_mask']



  def __init__(  self	:   Tuple    ,						lowerCAmelCase	:   Dict    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   List[str]="replace"    ,						lowerCAmelCase	:   List[Any]="<s>"    ,						lowerCAmelCase	:   str="</s>"    ,						lowerCAmelCase	:   Dict="</s>"    ,						lowerCAmelCase	:   Optional[int]="<s>"    ,						lowerCAmelCase	:   int="<unk>"    ,						lowerCAmelCase	:   Optional[int]="<pad>"    ,						lowerCAmelCase	:   str="<mask>"    ,						lowerCAmelCase	:   Dict=False    ,						**lowerCAmelCase	:   Any    ,						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else bos_token
     SCREAMING_SNAKE_CASE_:	int            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else eos_token
     SCREAMING_SNAKE_CASE_:	int            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else sep_token
     SCREAMING_SNAKE_CASE_:	Dict            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else cls_token
     SCREAMING_SNAKE_CASE_:	str            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else unk_token
     SCREAMING_SNAKE_CASE_:	int            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     SCREAMING_SNAKE_CASE_:	Optional[int]            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else mask_token

     super().__init__(
         errors=lowerCAmelCase    ,						bos_token=lowerCAmelCase    ,						eos_token=lowerCAmelCase    ,						unk_token=lowerCAmelCase    ,						sep_token=lowerCAmelCase    ,						cls_token=lowerCAmelCase    ,						pad_token=lowerCAmelCase    ,						mask_token=lowerCAmelCase    ,						add_prefix_space=lowerCAmelCase    ,						**lowerCAmelCase    ,						)
     with open(lowerCAmelCase    ,						encoding="""utf-8"""						) as vocab_handle:
        SCREAMING_SNAKE_CASE_:	Any            =json.load(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Dict            ={v: k for k, v in self.encoder.items()}
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =errors  # how to handle errors in decoding
     SCREAMING_SNAKE_CASE_:	Any            =bytes_to_unicode()
     SCREAMING_SNAKE_CASE_:	str            ={v: k for k, v in self.byte_encoder.items()}
     with open(lowerCAmelCase    ,						encoding="""utf-8"""						) as merges_handle:
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =merges_handle.read().split("""\n"""						)[1:-1]
     SCREAMING_SNAKE_CASE_:	Dict            =[tuple(merge.split()						) for merge in bpe_merges]
     SCREAMING_SNAKE_CASE_:	Any            =dict(zip(lowerCAmelCase    ,						range(len(lowerCAmelCase						)						)						)						)
     SCREAMING_SNAKE_CASE_:	int            ={}
     SCREAMING_SNAKE_CASE_:	Optional[int]            =add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     SCREAMING_SNAKE_CASE_:	Tuple            =re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""						)



  @property
  def 		lowerCamelCase__    (  self	:   int						)       ->   List[Any]:


     '''simple docstring'''




     return len(self.encoder						)



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


     '''simple docstring'''




     return dict(self.encoder    ,						**self.added_tokens_encoder						)



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


     '''simple docstring'''




     if token in self.cache:
        return self.cache[token]
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =tuple(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =get_pairs(lowerCAmelCase						)

     if not pairs:
        return token

     while True:
        SCREAMING_SNAKE_CASE_:	int            =min(lowerCAmelCase    ,						key=lambda lowerCAmelCase						: self.bpe_ranks.get(lowerCAmelCase    ,						float("""inf"""						)						)						)
        if bigram not in self.bpe_ranks:
           break
        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[Any]            =bigram
        SCREAMING_SNAKE_CASE_:	List[Any]            =[]
        SCREAMING_SNAKE_CASE_:	int            =0
        while i < len(lowerCAmelCase						):
           try:
              SCREAMING_SNAKE_CASE_:	List[Any]            =word.index(lowerCAmelCase    ,						lowerCAmelCase						)
           except ValueError:
              new_word.extend(word[i:]						)
              break
           else:
              new_word.extend(word[i:j]						)
              SCREAMING_SNAKE_CASE_:	str            =j

           if word[i] == first and i < len(lowerCAmelCase						) - 1 and word[i + 1] == second:
              new_word.append(first + second						)
              i += 2
           else:
              new_word.append(word[i]						)
              i += 1
        SCREAMING_SNAKE_CASE_:	Any            =tuple(lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:	str            =new_word
        if len(lowerCAmelCase						) == 1:
           break
        else:
           SCREAMING_SNAKE_CASE_:	List[str]            =get_pairs(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =""" """.join(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =word
     return word



  def 		lowerCamelCase__    (  self	:   Optional[int]    ,						lowerCAmelCase	:   Optional[int]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =[]
     for token in re.findall(self.pat    ,						lowerCAmelCase						):
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            ="""""".join(
            self.byte_encoder[b] for b in token.encode("""utf-8"""						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase						).split(""" """						)						)
     return bpe_tokens



  def 		lowerCamelCase__    (  self	:   List[str]    ,						lowerCAmelCase	:   str						)       ->   str:


     '''simple docstring'''




     return self.encoder.get(lowerCAmelCase    ,						self.encoder.get(self.unk_token						)						)



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   Optional[Any]						)       ->   Optional[int]:


     '''simple docstring'''




     return self.decoder.get(lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Dict    ,						lowerCAmelCase	:   Optional[Any]						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            ="""""".join(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Dict            =bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""    ,						errors=self.errors						)
     return text



  def 		lowerCamelCase__    (  self	:   Optional[Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   Optional[str] = None						)       ->   Tuple[str]:


     '''simple docstring'''




     if not os.path.isdir(lowerCAmelCase						):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
        return
     SCREAMING_SNAKE_CASE_:	Any            =os.path.join(
         lowerCAmelCase    ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =os.path.join(
         lowerCAmelCase    ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

     with open(lowerCAmelCase    ,						"""w"""    ,						encoding="""utf-8"""						) as f:
        f.write(json.dumps(self.encoder    ,						indent=2    ,						sort_keys=lowerCAmelCase    ,						ensure_ascii=lowerCAmelCase						) + """\n"""						)

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =0
     with open(lowerCAmelCase    ,						"""w"""    ,						encoding="""utf-8"""						) as writer:
        writer.write("""#version: 0.2\n"""						)
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,						key=lambda lowerCAmelCase						: kv[1]						):
           if index != token_index:
              logger.warning(
                  f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                  """ Please check that the tokenizer is not corrupted!"""						)
              SCREAMING_SNAKE_CASE_:	Union[str, Any]            =token_index
           writer.write(""" """.join(lowerCAmelCase						) + """\n"""						)
           index += 1

     return vocab_file, merge_file



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   List[int]    ,						lowerCAmelCase	:   Optional[List[int]] = None						)       ->   List[int]:


     '''simple docstring'''




     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     SCREAMING_SNAKE_CASE_:	List[str]            =[self.cls_token_id]
     SCREAMING_SNAKE_CASE_:	List[str]            =[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep



  def 		lowerCamelCase__    (  self	:   List[str]    ,						lowerCAmelCase	:   List[int]    ,						lowerCAmelCase	:   Optional[List[int]] = None    ,						lowerCAmelCase	:   bool = False						)       ->   List[int]:


     '''simple docstring'''




     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=lowerCAmelCase    ,						token_ids_a=lowerCAmelCase    ,						already_has_special_tokens=lowerCAmelCase						)

     if token_ids_a is None:
        return [1] + ([0] * len(lowerCAmelCase						)) + [1]
     return [1] + ([0] * len(lowerCAmelCase						)) + [1, 1] + ([0] * len(lowerCAmelCase						)) + [1]



  def 		lowerCamelCase__    (  self	:   Dict    ,						lowerCAmelCase	:   List[int]    ,						lowerCAmelCase	:   Optional[List[int]] = None						)       ->   List[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =[self.sep_token_id]
     SCREAMING_SNAKE_CASE_:	str            =[self.cls_token_id]

     if token_ids_a is None:
        return len(cls + token_ids_a + sep						) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]





  def 		lowerCamelCase__    (  self	:   Optional[int]    ,						lowerCAmelCase	:   Optional[int]    ,						lowerCAmelCase	:   Union[str, Any]=False    ,						**lowerCAmelCase	:   str						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =kwargs.pop("""add_prefix_space"""    ,						self.add_prefix_space						)
     if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase						) > 0 and not text[0].isspace()):
        SCREAMING_SNAKE_CASE_:	List[Any]            =""" """ + text
     return (text, kwargs)



