


"""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:

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=3_60_00				,							SCREAMING_SNAKE_CASE__=13				,							SCREAMING_SNAKE_CASE__=7				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=32				,							SCREAMING_SNAKE_CASE__=42				,							SCREAMING_SNAKE_CASE__=5				,							SCREAMING_SNAKE_CASE__=2				,							SCREAMING_SNAKE_CASE__=4				,							SCREAMING_SNAKE_CASE__=2				,							SCREAMING_SNAKE_CASE__=32				,							SCREAMING_SNAKE_CASE__=80				,							SCREAMING_SNAKE_CASE__=33				,							SCREAMING_SNAKE_CASE__=0.0				,							SCREAMING_SNAKE_CASE__=1E-6				,							SCREAMING_SNAKE_CASE__=1_00				,							SCREAMING_SNAKE_CASE__=0.0				,							)			->  str:

					"""simple docstring"""



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

					"""simple docstring"""



					return GPTSanJapaneseConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



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

					_UpperCamelCase				:int				    =  self.get_config()

					return (config, input_ids)
		def      _UpperCamelCase(   self      )			->  Any:

					"""simple docstring"""



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

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

					return (config, {"input_ids": input_ids})
		def      _UpperCamelCase(   self      )			->  Optional[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      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							)			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Tuple				    =  GPTSanJapaneseForConditionalGeneration(config=SCREAMING_SNAKE_CASE__      )
					model.to(SCREAMING_SNAKE_CASE__      )
					model.eval()
					_UpperCamelCase				:Optional[Any]				    =  model(
					    input_ids=SCREAMING_SNAKE_CASE__				,							)
					self.parent.assertIsNotNone(SCREAMING_SNAKE_CASE__      )






@require_torch
class     A(     lowerCamelCase__							,       lowerCamelCase__							,       unittest.TestCase		):

		"""simple docstring"""





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

					"""simple docstring"""



					_UpperCamelCase				:Union[str, Any]				    =  GPTSanJapaneseTester(self      )
					_UpperCamelCase				:Union[str, Any]				    =  ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE__				,							d_model=37      )
		def      _UpperCamelCase(   self      )			->  Optional[int]:

					"""simple docstring"""



					GPTSanJapaneseConfig()
		def      _UpperCamelCase(   self      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:int				    =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__      )
		@unittest.skip(
		    reason='''skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'''      )
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					super().test_model_parallelism()





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

					"""simple docstring"""



					pass






@require_torch
class     A(     lowerCamelCase__							,       lowerCamelCase__							,       unittest.TestCase		):

		"""simple docstring"""





		A         =       (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
		A         =       False
		A         =       False
		A         =       False
		A         =       False
		A         =       False
		A         =       False
		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
		A         =       [0.8, 0.9]
		def      _UpperCamelCase(   self      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:int				    =  GPTSanJapaneseTester(self      )
					_UpperCamelCase				:Dict				    =  ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE__				,							d_model=37      )
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					GPTSanJapaneseConfig()
		def      _UpperCamelCase(   self      )			->  Tuple:

					"""simple docstring"""



					_UpperCamelCase				:Union[str, Any]				    =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__      )
		@unittest.skip(
		    reason='''skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'''      )
		def      _UpperCamelCase(   self      )			->  Optional[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      _UpperCamelCase(   self      )			->  Any:

					"""simple docstring"""



					pass
		@slow
		def      _UpperCamelCase(   self      )			->  List[Any]:

					"""simple docstring"""



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

					def check(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=5E-4      ):
								return abs(a - b      ) < epsilon * max(abs(SCREAMING_SNAKE_CASE__      )				,							abs(SCREAMING_SNAKE_CASE__      )      )

					self.assertTrue(np.all([check(target[i]				,							predict[i]      ) for i in range(len(SCREAMING_SNAKE_CASE__      )      )]      )      )
		@slow
		def      _UpperCamelCase(   self      )			->  Any:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
					_UpperCamelCase				:Optional[Any]				    =  GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
					model.to(SCREAMING_SNAKE_CASE__      )

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

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

					_UpperCamelCase				:Optional[Any]				    =  '''left'''
					_UpperCamelCase				:int				    =  tokenizer(SCREAMING_SNAKE_CASE__				,							return_tensors='''pt'''				,							padding=SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Dict				    =  inputs['''input_ids'''].to(SCREAMING_SNAKE_CASE__      )

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

					_UpperCamelCase				:List[str]				    =  model.generate(
					    input_ids=SCREAMING_SNAKE_CASE__				,							attention_mask=inputs['''attention_mask'''].to(SCREAMING_SNAKE_CASE__      )				,							max_new_tokens=3				,							generation_config=SCREAMING_SNAKE_CASE__				,							)

					_UpperCamelCase				:Dict				    =  tokenizer(sentences[0]				,							return_tensors='''pt'''      ).input_ids.to(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Optional[int]				    =  model.generate(
					    input_ids=SCREAMING_SNAKE_CASE__				,							max_new_tokens=3				,							generation_config=SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Any				    =  tokenizer(sentences[1]				,							return_tensors='''pt'''      ).input_ids.to(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Tuple				    =  model.generate(input_ids=SCREAMING_SNAKE_CASE__				,							max_new_tokens=3				,							generation_config=SCREAMING_SNAKE_CASE__      )

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

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

					_UpperCamelCase				: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(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							[non_padded_sentence, padded_sentence]      )
		@tooslow
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					_UpperCamelCase				:str				    =  GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
					_UpperCamelCase				:Dict				    =  GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
					# Output of original model created with mesh-tensoflow
					_UpperCamelCase				:str				    =  [
					    ('''武田信玄は''', 3_56_75),
					    ('''武田信玄は、''', 45),
					    ('''武田信玄は、この''', 29),
					    ('''武田信玄は、このよう''', 3_06_42),
					    ('''武田信玄は、このような''', 3_56_80),
					    ('''武田信玄は、このような「''', 86_40),
					    ('''武田信玄は、このような「武田''', 3_16_17),
					    ('''武田信玄は、このような「武田家''', 3_06_46),
					    ('''武田信玄は、このような「武田家の''', 3_16_17),
					    ('''武田信玄は、このような「武田家の家''', 3_13_81),
					]
					for input, output in target:
								_UpperCamelCase				:Any				    =  tokenizer.encode(SCREAMING_SNAKE_CASE__				,							return_tensors='''pt'''      )
								_UpperCamelCase				:Any				    =  model(SCREAMING_SNAKE_CASE__      )
								_UpperCamelCase				:Any				    =  outputs.logits.detach().cpu().numpy()[0]
								_UpperCamelCase				:Optional[int]				    =  np.argmax(output_logits[-1]      )
								self.assertEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
		@slow
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:Tuple				    =  GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
					_UpperCamelCase				:Any				    =  GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
					model.to(SCREAMING_SNAKE_CASE__      )

					# set deterministically
					_UpperCamelCase				:Any				    =  GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
					_UpperCamelCase				:Tuple				    =  1

					_UpperCamelCase				:Tuple				    =  '''武田信玄は、'''
					_UpperCamelCase				:Union[str, Any]				    =  tokenizer(SCREAMING_SNAKE_CASE__				,							return_tensors='''pt'''      ).input_ids.to(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Optional[Any]				    =  tokenizer([input_text, input_text]				,							return_tensors='''pt'''      ).input_ids.to(SCREAMING_SNAKE_CASE__      )

					# spout from uniform and one-hot
					_UpperCamelCase				:str				    =  [
					    # 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
					]

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

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

					_UpperCamelCase				:Optional[Any]				    =  model.generate(
					    input_ids=SCREAMING_SNAKE_CASE__				,							spout=SCREAMING_SNAKE_CASE__				,							max_new_tokens=20				,							generation_config=SCREAMING_SNAKE_CASE__				,							)

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

					_UpperCamelCase				: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(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							[outa_sentence, outa_sentence]      )





		@slow
		def      _UpperCamelCase(   self      )			->  Any:

					"""simple docstring"""



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

					# set deterministically
					_UpperCamelCase				:str				    =  GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''      )
					_UpperCamelCase				:int				    =  1

					_UpperCamelCase				:int				    =  '''武田信玄'''
					_UpperCamelCase				:List[str]				    =  '''織田信長'''
					_UpperCamelCase				:Optional[int]				    =  '''は、'''
					_UpperCamelCase				:Optional[int]				    =  '''が、'''
					_UpperCamelCase				:Tuple				    =  tokenizer(SCREAMING_SNAKE_CASE__				,							prefix_text=SCREAMING_SNAKE_CASE__				,							return_tensors='''pt'''      )
					_UpperCamelCase				:str				    =  tokenizer(SCREAMING_SNAKE_CASE__				,							prefix_text=SCREAMING_SNAKE_CASE__				,							return_tensors='''pt'''      )
					_UpperCamelCase				:List[str]				    =  tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]				,							return_tensors='''pt'''      )

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

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

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

					_UpperCamelCase				:Tuple				    =  tokenizer.decode(outputa[0]      )
					_UpperCamelCase				:int				    =  tokenizer.decode(outputa[0]      )
					_UpperCamelCase				:List[str]				    =  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Optional[int]				    =  [
					    '''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(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							[outa_sentence, outa_sentence]      )





"""simple docstring"""






import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json





@pytest.fixture
def 							A_					(   snake_case__		)				->     Union[str, Any]:
			_UpperCamelCase				:int				    =  tmp_path / '''file.jsonl'''
			_UpperCamelCase				:Dict				    =  textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''		)
			with open(snake_case__					,				'''w'''		) as f:
						f.write(snake_case__		)
			return str(snake_case__		)





@pytest.fixture
def 							A_					(   snake_case__		)				->     Optional[int]:
			_UpperCamelCase				:Dict				    =  tmp_path / '''file_utf16_encoded.jsonl'''
			_UpperCamelCase				:Dict				    =  textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''		)
			with open(snake_case__					,				'''w'''					,				encoding='''utf-16'''		) as f:
						f.write(snake_case__		)
			return str(snake_case__		)





@pytest.fixture
def 							A_					(   snake_case__		)				->     Optional[int]:
			_UpperCamelCase				:Optional[Any]				    =  tmp_path / '''file_with_list_of_dicts.json'''
			_UpperCamelCase				:str				    =  textwrap.dedent(
			    '''\
        [
            {"col_1": -1},
            {"col_1": 1, "col_2": 2},
            {"col_1": 10, "col_2": 20}
        ]
        '''		)
			with open(snake_case__					,				'''w'''		) as f:
						f.write(snake_case__		)
			return str(snake_case__		)





@pytest.fixture
def 							A_					(   snake_case__		)				->     Union[str, Any]:
			_UpperCamelCase				:Dict				    =  tmp_path / '''file_with_list_of_dicts_field.json'''
			_UpperCamelCase				:Optional[int]				    =  textwrap.dedent(
			    '''\
        {
            "field1": 1,
            "field2": "aabb",
            "field3": [
                {"col_1": -1},
                {"col_1": 1, "col_2": 2},
                {"col_1": 10, "col_2": 20}
            ]
        }
        '''		)
			with open(snake_case__					,				'''w'''		) as f:
						f.write(snake_case__		)
			return str(snake_case__		)





@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''					,				[
        ('''jsonl_file''', {}),
        ('''jsonl_file_utf16_encoded''', {'''encoding''': '''utf-16'''}),
        ('''json_file_with_list_of_dicts''', {}),
        ('''json_file_with_list_of_dicts_field''', {'''field''': '''field3'''}),
    ]					,				)
def 							A_					(   snake_case__					,				snake_case__					,				snake_case__		)				->     int:
			_UpperCamelCase				:Any				    =  Json(**snake_case__		)
			_UpperCamelCase				:str				    =  json._generate_tables([[request.getfixturevalue(snake_case__		)]]		)
			_UpperCamelCase				:List[Any]				    =  pa.concat_tables([table for _, table in generator]		)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}





@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''					,				[
        (
            '''jsonl_file''',
            {'''features''': Features({'''col_1''': Value('''int64'''		), '''col_2''': Value('''int64'''		), '''missing_col''': Value('''string'''		)}		)},
        ),
        (
            '''json_file_with_list_of_dicts''',
            {'''features''': Features({'''col_1''': Value('''int64'''		), '''col_2''': Value('''int64'''		), '''missing_col''': Value('''string'''		)}		)},
        ),
        (
            '''json_file_with_list_of_dicts_field''',
            {
                '''field''': '''field3''',
                '''features''': Features(
                    {'''col_1''': Value('''int64'''		), '''col_2''': Value('''int64'''		), '''missing_col''': Value('''string'''		)}		),
            },
        ),
    ]					,				)
def 							A_					(   snake_case__					,				snake_case__					,				snake_case__		)				->     Optional[int]:
			_UpperCamelCase				:Union[str, Any]				    =  Json(**snake_case__		)
			_UpperCamelCase				:List[str]				    =  json._generate_tables([[request.getfixturevalue(snake_case__		)]]		)
			_UpperCamelCase				:str				    =  pa.concat_tables([table for _, table in generator]		)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}

