

import unittest

import numpy as np

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

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



class 					__snake_case     :

  def __init__(    self  ,						_A  ,						_A=36000  ,						_A=13  ,						_A=7  ,						_A=True  ,						_A=32  ,						_A=42  ,						_A=5  ,						_A=2  ,						_A=4  ,						_A=2  ,						_A=32  ,						_A=80  ,						_A=33  ,						_A=0.0  ,						_A=1E-6  ,						_A=100  ,						_A=0.0  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  parent
      SCREAMING_SNAKE_CASE_													=  batch_size
      SCREAMING_SNAKE_CASE_													=  num_contexts
      # For common tests
      SCREAMING_SNAKE_CASE_													=  self.num_contexts
      SCREAMING_SNAKE_CASE_													=  is_training
      SCREAMING_SNAKE_CASE_													=  hidden_size
      SCREAMING_SNAKE_CASE_													=  num_ext_layers
      SCREAMING_SNAKE_CASE_													=  ext_size
      SCREAMING_SNAKE_CASE_													=  num_hidden_layers
      SCREAMING_SNAKE_CASE_													=  num_attention_heads
      SCREAMING_SNAKE_CASE_													=  num_experts
      SCREAMING_SNAKE_CASE_													=  d_ff
      SCREAMING_SNAKE_CASE_													=  d_ext
      SCREAMING_SNAKE_CASE_													=  d_spout
      SCREAMING_SNAKE_CASE_													=  dropout_rate
      SCREAMING_SNAKE_CASE_													=  layer_norm_epsilon
      SCREAMING_SNAKE_CASE_													=  expert_capacity
      SCREAMING_SNAKE_CASE_													=  router_jitter_noise

  def   lowerCAmelCase__   (    self):
      return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese')

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

      SCREAMING_SNAKE_CASE_													=  self.get_config()

      return (config, input_ids)

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

      SCREAMING_SNAKE_CASE_													=  self.get_config()

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

  def   lowerCAmelCase__   (    self):
      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  ,						_A  ,						_A  ,						):
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseForConditionalGeneration(config=_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE_													=  model(
          input_ids=_A  ,						)
      self.parent.assertIsNotNone(_A)






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

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A):
      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):
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseTester(self)
      SCREAMING_SNAKE_CASE_													=  ConfigTester(self  ,						config_class=_A  ,						d_model=37)

  def   lowerCAmelCase__   (    self):
      GPTSanJapaneseConfig()

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__   (    self):
      super().test_model_parallelism()


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






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

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseTester(self)
      SCREAMING_SNAKE_CASE_													=  ConfigTester(self  ,						config_class=_A  ,						d_model=37)

  def   lowerCAmelCase__   (    self):
      GPTSanJapaneseConfig()

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__   (    self):
      super().test_model_parallelism()

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

  @slow
  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese')
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese')
      SCREAMING_SNAKE_CASE_													=  tokenizer.encode('武田信玄は'  ,						return_tensors='pt')
      SCREAMING_SNAKE_CASE_													=  model(_A)
      SCREAMING_SNAKE_CASE_													=  outputs.logits.detach().cpu().numpy()
      # Output of original model created with mesh-tensoflow
      SCREAMING_SNAKE_CASE_													=  [
          # 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_													=  np.array(_A).flatten()
      SCREAMING_SNAKE_CASE_													=  output_logits[0, :, :20].flatten()

      def check(_A  ,						_A  ,						_A=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   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese')
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese')
      model.to(_A)

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

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

      SCREAMING_SNAKE_CASE_													=  'left'
      SCREAMING_SNAKE_CASE_													=  tokenizer(_A  ,						return_tensors='pt'  ,						padding=_A)
      SCREAMING_SNAKE_CASE_													=  inputs['input_ids'].to(_A)

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

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

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

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

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

      SCREAMING_SNAKE_CASE_													=  tokenizer.batch_decode(_A  ,						skip_special_tokens=_A)
      SCREAMING_SNAKE_CASE_													=  tokenizer.decode(output_non_padded[0]  ,						skip_special_tokens=_A)
      SCREAMING_SNAKE_CASE_													=  tokenizer.decode(output_padded[0]  ,						skip_special_tokens=_A)

      SCREAMING_SNAKE_CASE_													=  [
          '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   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese')
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese')
      # Output of original model created with mesh-tensoflow
      SCREAMING_SNAKE_CASE_													=  [
          ('武田信玄は', 35675),
          ('武田信玄は、', 45),
          ('武田信玄は、この', 29),
          ('武田信玄は、このよう', 30642),
          ('武田信玄は、このような', 35680),
          ('武田信玄は、このような「', 8640),
          ('武田信玄は、このような「武田', 31617),
          ('武田信玄は、このような「武田家', 30646),
          ('武田信玄は、このような「武田家の', 31617),
          ('武田信玄は、このような「武田家の家', 31381),
      ]
      for input, output in target:
          SCREAMING_SNAKE_CASE_													=  tokenizer.encode(_A  ,						return_tensors='pt')
          SCREAMING_SNAKE_CASE_													=  model(_A)
          SCREAMING_SNAKE_CASE_													=  outputs.logits.detach().cpu().numpy()[0]
          SCREAMING_SNAKE_CASE_													=  np.argmax(output_logits[-1])
          self.assertEqual(_A  ,						_A)

  @slow
  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese')
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese')
      model.to(_A)

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

      SCREAMING_SNAKE_CASE_													=  '武田信玄は、'
      SCREAMING_SNAKE_CASE_													=  tokenizer(_A  ,						return_tensors='pt').input_ids.to(_A)
      SCREAMING_SNAKE_CASE_													=  tokenizer([input_text, input_text]  ,						return_tensors='pt').input_ids.to(_A)

      # spout from uniform and one-hot
      SCREAMING_SNAKE_CASE_													=  [
          # 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_													=  model.generate(
          input_ids=_A  ,						spout=spouts[0]  ,						max_new_tokens=20  ,						generation_config=_A  ,						)

      SCREAMING_SNAKE_CASE_													=  model.generate(
          input_ids=_A  ,						spout=spouts[1]  ,						max_new_tokens=20  ,						generation_config=_A  ,						)

      SCREAMING_SNAKE_CASE_													=  model.generate(
          input_ids=_A  ,						spout=_A  ,						max_new_tokens=20  ,						generation_config=_A  ,						)

      SCREAMING_SNAKE_CASE_													=  tokenizer.decode(outputa[0])
      SCREAMING_SNAKE_CASE_													=  tokenizer.decode(outputa[0])
      SCREAMING_SNAKE_CASE_													=  tokenizer.batch_decode(_A)

      SCREAMING_SNAKE_CASE_													=  [
          '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   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese')
      SCREAMING_SNAKE_CASE_													=  GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese')
      model.to(_A)

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

      SCREAMING_SNAKE_CASE_													=  '武田信玄'
      SCREAMING_SNAKE_CASE_													=  '織田信長'
      SCREAMING_SNAKE_CASE_													=  'は、'
      SCREAMING_SNAKE_CASE_													=  'が、'
      SCREAMING_SNAKE_CASE_													=  tokenizer(_A  ,						prefix_text=_A  ,						return_tensors='pt')
      SCREAMING_SNAKE_CASE_													=  tokenizer(_A  ,						prefix_text=_A  ,						return_tensors='pt')
      SCREAMING_SNAKE_CASE_													=  tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]  ,						return_tensors='pt')

      SCREAMING_SNAKE_CASE_													=  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  ,						)

      SCREAMING_SNAKE_CASE_													=  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  ,						)

      SCREAMING_SNAKE_CASE_													=  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  ,						)

      SCREAMING_SNAKE_CASE_													=  tokenizer.decode(outputa[0])
      SCREAMING_SNAKE_CASE_													=  tokenizer.decode(outputa[0])
      SCREAMING_SNAKE_CASE_													=  tokenizer.batch_decode(_A)

      SCREAMING_SNAKE_CASE_													=  [
          '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])





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__     :					int								=			{
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					str								=			["CamembertTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Any								=			["CamembertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Union[str, Any]								=			[
           "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "CamembertForCausalLM",
           "CamembertForMaskedLM",
           "CamembertForMultipleChoice",
           "CamembertForQuestionAnswering",
           "CamembertForSequenceClassification",
           "CamembertForTokenClassification",
           "CamembertModel",
           "CamembertPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Optional[Any]								=			[
           "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFCamembertForCausalLM",
           "TFCamembertForMaskedLM",
           "TFCamembertForMultipleChoice",
           "TFCamembertForQuestionAnswering",
           "TFCamembertForSequenceClassification",
           "TFCamembertForTokenClassification",
           "TFCamembertModel",
           "TFCamembertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__     :					List[str]								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


