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						:





      '''simple docstring'''






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


          """simple docstring"""


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



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


          """simple docstring"""


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



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


          """simple docstring"""


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

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

          return (config, input_ids)



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


          """simple docstring"""


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

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

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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->Tuple:


          """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[str]     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					int     ,	)					->str:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    GPTSanJapaneseForConditionalGeneration(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase  :		List[str]			    =    model(
              input_ids=_UpperCamelCase     ,	)
          self.parent.assertIsNotNone(_UpperCamelCase)





@require_torch
class 						__snake_case						(			__lowerCAmelCase			,	__lowerCAmelCase			,	unittest.TestCase		):





      '''simple docstring'''






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



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


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    GPTSanJapaneseTester(self)
          _lowerCamelCase  :		List[Any]			    =    ConfigTester(self     ,	config_class=_UpperCamelCase     ,	d_model=37)



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


          """simple docstring"""


          GPTSanJapaneseConfig()



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


          """simple docstring"""


          _lowerCamelCase  :		int			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCamelCase)



      @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  :					Any)					->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  :					Optional[int])					->Tuple:


          """simple docstring"""


          pass





@require_torch
class 						__snake_case						(			__lowerCAmelCase			,	__lowerCAmelCase			,	unittest.TestCase		):





      '''simple docstring'''






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



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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    GPTSanJapaneseTester(self)
          _lowerCamelCase  :		List[Any]			    =    ConfigTester(self     ,	config_class=_UpperCamelCase     ,	d_model=37)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->Tuple:


          """simple docstring"""


          GPTSanJapaneseConfig()



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCamelCase)



      @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  :					Any)					->List[str]:


          """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  :					Any)					->Optional[Any]:


          """simple docstring"""


          pass



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


          """simple docstring"""


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

          def check(_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					int=5E-4):
              return abs(a - b) < epsilon * max(abs(_UpperCamelCase)     ,	abs(_UpperCamelCase))

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



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


          """simple docstring"""


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

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

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

          _lowerCamelCase  :		List[str]			    =    """left"""
          _lowerCamelCase  :		Tuple			    =    tokenizer(_UpperCamelCase     ,	return_tensors="""pt"""     ,	padding=_UpperCamelCase)
          _lowerCamelCase  :		str			    =    inputs["""input_ids"""].to(_UpperCamelCase)

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

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

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

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

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

          _lowerCamelCase  :		List[str]			    =    tokenizer.batch_decode(_UpperCamelCase     ,	skip_special_tokens=_UpperCamelCase)
          _lowerCamelCase  :		Optional[Any]			    =    tokenizer.decode(output_non_padded[0]     ,	skip_special_tokens=_UpperCamelCase)
          _lowerCamelCase  :		Union[str, Any]			    =    tokenizer.decode(output_padded[0]     ,	skip_special_tokens=_UpperCamelCase)

          _lowerCamelCase  :		List[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(_UpperCamelCase     ,	_UpperCamelCase)
          self.assertListEqual(_UpperCamelCase     ,	[non_padded_sentence, padded_sentence])



      @tooslow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Dict:


          """simple docstring"""


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



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


          """simple docstring"""


          _lowerCamelCase  :		int			    =    GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese""")
          _lowerCamelCase  :		List[Any]			    =    GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese""")
          model.to(_UpperCamelCase)

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

          _lowerCamelCase  :		Optional[int]			    =    """武田信玄は、"""
          _lowerCamelCase  :		Dict			    =    tokenizer(_UpperCamelCase     ,	return_tensors="""pt""").input_ids.to(_UpperCamelCase)
          _lowerCamelCase  :		str			    =    tokenizer([input_text, input_text]     ,	return_tensors="""pt""").input_ids.to(_UpperCamelCase)

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

          _lowerCamelCase  :		Optional[int]			    =    model.generate(
              input_ids=_UpperCamelCase     ,	spout=spouts[0]     ,	max_new_tokens=20     ,	generation_config=_UpperCamelCase     ,	)

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

          _lowerCamelCase  :		int			    =    model.generate(
              input_ids=_UpperCamelCase     ,	spout=_UpperCamelCase     ,	max_new_tokens=20     ,	generation_config=_UpperCamelCase     ,	)

          _lowerCamelCase  :		List[Any]			    =    tokenizer.decode(outputa[0])
          _lowerCamelCase  :		Tuple			    =    tokenizer.decode(outputa[0])
          _lowerCamelCase  :		Any			    =    tokenizer.batch_decode(_UpperCamelCase)

          _lowerCamelCase  :		Union[str, Any]			    =    [
              """武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である""",
              """武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討""",
          ]
          self.assertListEqual(_UpperCamelCase     ,	_UpperCamelCase)
          self.assertListEqual(_UpperCamelCase     ,	[outa_sentence, outa_sentence])




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


          """simple docstring"""


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

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

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

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

          _lowerCamelCase  :		List[str]			    =    model.generate(
              input_ids=input_tok_a.input_ids.to(_UpperCamelCase)     ,	token_type_ids=input_tok_a.token_type_ids.to(_UpperCamelCase)     ,	max_new_tokens=20     ,	generation_config=_UpperCamelCase     ,	)

          _lowerCamelCase  :		Optional[int]			    =    model.generate(
              input_ids=input_tok_a.input_ids.to(_UpperCamelCase)     ,	token_type_ids=input_tok_a.token_type_ids.to(_UpperCamelCase)     ,	attention_mask=input_tok_a.attention_mask.to(_UpperCamelCase)     ,	max_new_tokens=20     ,	generation_config=_UpperCamelCase     ,	)

          _lowerCamelCase  :		Dict			    =    tokenizer.decode(outputa[0])
          _lowerCamelCase  :		Optional[Any]			    =    tokenizer.decode(outputa[0])
          _lowerCamelCase  :		int			    =    tokenizer.batch_decode(_UpperCamelCase)

          _lowerCamelCase  :		Union[str, Any]			    =    [
              """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(_UpperCamelCase     ,	_UpperCamelCase)
          self.assertListEqual(_UpperCamelCase     ,	[outa_sentence, outa_sentence])





lowerCAmelCase      :				Union[str, Any]     			=1000







def     A__	(       __A						):







    '''simple docstring'''
    return x * x







def     A__	(       __A ,			__A ,			__A ,			__A = 4						):







    '''simple docstring'''
    assert callable(
        __A						), F"""the function(object) passed should be callable your input : {function}"""
    assert isinstance(__A ,			(float, int)						), F"""a should be float or integer your input : {a}"""
    assert isinstance(function(__A						) ,			(float, int)						), (
        "the function should return integer or float return type of your function, "
        F"""{type(__A						)}"""
    )
    assert isinstance(__A ,			(float, int)						), F"""b should be float or integer your input : {b}"""
    assert (
        isinstance(__A ,			__A						) and precision > 0
    ), F"""precision should be positive integer your input : {precision}"""

    # just applying the formula of simpson for approximate integration written in
    # mentioned article in first comment of this file and above this function

    _lowerCamelCase  :		Optional[Any]			    =    (b - a) / N_STEPS
    _lowerCamelCase  :		str			    =    function(__A						) + function(__A						)

    for i in range(1 ,			__A						):
        _lowerCamelCase  :		str			    =    a + h * i
        result += function(__A						) * (4 if i % 2 else 2)

    result *= h / 3
    return round(__A ,			__A						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




