




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	: Dict						,	A__	: List[Any]						,	A__	: List[Any]=3_6_0_0_0						,	A__	: Optional[Any]=1_3						,	A__	: List[Any]=7						,	A__	: List[str]=True						,	A__	: Optional[Any]=3_2						,	A__	: Dict=4_2						,	A__	: List[str]=5						,	A__	: List[str]=2						,	A__	: int=4						,	A__	: Dict=2						,	A__	: Union[str, Any]=3_2						,	A__	: Any=8_0						,	A__	: Union[str, Any]=3_3						,	A__	: int=0.0						,	A__	: int=1E-6						,	A__	: Union[str, Any]=1_0_0						,	A__	: str=0.0						,	)					->							Tuple:
           lowerCamelCase__   	=					vocab_size
           lowerCamelCase__   	=					parent
           lowerCamelCase__   	=					batch_size
           lowerCamelCase__   	=					num_contexts
           # For common tests
           lowerCamelCase__   	=					self.num_contexts
           lowerCamelCase__   	=					is_training
           lowerCamelCase__   	=					hidden_size
           lowerCamelCase__   	=					num_ext_layers
           lowerCamelCase__   	=					ext_size
           lowerCamelCase__   	=					num_hidden_layers
           lowerCamelCase__   	=					num_attention_heads
           lowerCamelCase__   	=					num_experts
           lowerCamelCase__   	=					d_ff
           lowerCamelCase__   	=					d_ext
           lowerCamelCase__   	=					d_spout
           lowerCamelCase__   	=					dropout_rate
           lowerCamelCase__   	=					layer_norm_epsilon
           lowerCamelCase__   	=					expert_capacity
           lowerCamelCase__   	=					router_jitter_noise




     def 			__snake_case (     self	: Optional[int]      )					->							str:
           return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese'      )




     def 			__snake_case (     self	: Optional[int]      )					->							str:
           lowerCamelCase__   	=					ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size      )

           lowerCamelCase__   	=					self.get_config()

           return (config, input_ids)




     def 			__snake_case (     self	: List[Any]      )					->							Union[str, Any]:
           lowerCamelCase__   	=					ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size      )

           lowerCamelCase__   	=					self.get_config()

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




     def 			__snake_case (     self	: int      )					->							str:
           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 			__snake_case (     self	: Optional[Any]						,	A__	: Dict						,	A__	: Tuple						,	)					->							List[Any]:
           lowerCamelCase__   	=					GPTSanJapaneseForConditionalGeneration(config=A__      )
           model.to(A__      )
           model.eval()
           lowerCamelCase__   	=					model(
               input_ids=A__						,	)
           self.parent.assertIsNotNone(A__      )






@require_torch
class       A      (		_lowerCAmelCase      ,    _lowerCAmelCase      ,    unittest.TestCase		):
     """simple docstring"""

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




     def 			__snake_case (     self	: Optional[Any]						,	A__	: List[Any]						,	A__	: Any						,	A__	: Optional[Any]						,	A__	: List[Any]						,	A__	: int      )					->							Optional[int]:
           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 			__snake_case (     self	: str      )					->							str:
           lowerCamelCase__   	=					GPTSanJapaneseTester(self      )
           lowerCamelCase__   	=					ConfigTester(self						,	config_class=A__						,	d_model=3_7      )




     def 			__snake_case (     self	: Dict      )					->							Optional[int]:
           GPTSanJapaneseConfig()




     def 			__snake_case (     self	: Tuple      )					->							List[str]:
           lowerCamelCase__   	=					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 			__snake_case (     self	: Union[str, Any]      )					->							List[str]:
           super().test_model_parallelism()




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






@require_torch
class       A      (		_lowerCAmelCase      ,    _lowerCAmelCase      ,    unittest.TestCase		):
     """simple docstring"""

     _snake_case:						int  =(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
     _snake_case:						str  =False
     _snake_case:						Union[str, Any]  =False
     _snake_case:						Dict  =False
     _snake_case:						List[str]  =False
     _snake_case:						Union[str, Any]  =False
     _snake_case:						Any  =False
     # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
     _snake_case:						Tuple  =[0.8, 0.9]




     def 			__snake_case (     self	: int      )					->							Optional[Any]:
           lowerCamelCase__   	=					GPTSanJapaneseTester(self      )
           lowerCamelCase__   	=					ConfigTester(self						,	config_class=A__						,	d_model=3_7      )




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




     def 			__snake_case (     self	: Optional[int]      )					->							str:
           lowerCamelCase__   	=					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 			__snake_case (     self	: Optional[Any]      )					->							Optional[Any]:
           super().test_model_parallelism()




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




     @slow
     def 			__snake_case (     self	: Dict      )					->							Tuple:
           lowerCamelCase__   	=					GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'      )
           lowerCamelCase__   	=					GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'      )
           lowerCamelCase__   	=					tokenizer.encode('武田信玄は'						,	return_tensors='pt'      )
           lowerCamelCase__   	=					model(A__      )
           lowerCamelCase__   	=					outputs.logits.detach().cpu().numpy()
           # Output of original model created with mesh-tensoflow
           lowerCamelCase__   	=					[
               # fmt: off
               [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
               -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
               -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
               -12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
               [-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
               -7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
               -6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
               -8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
               [-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
               -11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
               -9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
               -10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
               [-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
               -9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
               -8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
               -11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
               [-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
               -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
               -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
               -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
               # fmt: on
           ]
           lowerCamelCase__   	=					np.array(A__      ).flatten()
           lowerCamelCase__   	=					output_logits[0, :, :2_0].flatten()

           def check(A__	: int						,	A__	: List[str]						,	A__	: Union[str, Any]=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 			__snake_case (     self	: Optional[Any]      )					->							List[Any]:
           lowerCamelCase__   	=					GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'      )
           lowerCamelCase__   	=					GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'      )
           model.to(A__      )

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

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

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

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

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

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

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

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

           lowerCamelCase__   	=					tokenizer.batch_decode(A__						,	skip_special_tokens=A__      )
           lowerCamelCase__   	=					tokenizer.decode(output_non_padded[0]						,	skip_special_tokens=A__      )
           lowerCamelCase__   	=					tokenizer.decode(output_padded[0]						,	skip_special_tokens=A__      )

           lowerCamelCase__   	=					[
               '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 			__snake_case (     self	: str      )					->							str:
           lowerCamelCase__   	=					GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'      )
           lowerCamelCase__   	=					GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'      )
           # Output of original model created with mesh-tensoflow
           lowerCamelCase__   	=					[
               ('武田信玄は', 3_5_6_7_5),
               ('武田信玄は、', 4_5),
               ('武田信玄は、この', 2_9),
               ('武田信玄は、このよう', 3_0_6_4_2),
               ('武田信玄は、このような', 3_5_6_8_0),
               ('武田信玄は、このような「', 8_6_4_0),
               ('武田信玄は、このような「武田', 3_1_6_1_7),
               ('武田信玄は、このような「武田家', 3_0_6_4_6),
               ('武田信玄は、このような「武田家の', 3_1_6_1_7),
               ('武田信玄は、このような「武田家の家', 3_1_3_8_1),
           ]
           for input, output in target:
                 lowerCamelCase__   	=					tokenizer.encode(A__						,	return_tensors='pt'      )
                 lowerCamelCase__   	=					model(A__      )
                 lowerCamelCase__   	=					outputs.logits.detach().cpu().numpy()[0]
                 lowerCamelCase__   	=					np.argmax(output_logits[-1]      )
                 self.assertEqual(A__						,	A__      )




     @slow
     def 			__snake_case (     self	: List[Any]      )					->							Dict:
           lowerCamelCase__   	=					GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'      )
           lowerCamelCase__   	=					GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'      )
           model.to(A__      )

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

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

           # spout from uniform and one-hot
           lowerCamelCase__   	=					[
               # fmt: off
               [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
               0.04803985, 0.211572  , 0.23188473, 0.37153068, 0.7836377 ,
               0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
               0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
               0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
               0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
               0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
               0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
               0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
               0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
               0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
               0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
               0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363  ,
               0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
               0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
               0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
               0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
               0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
               0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
               0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
               0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
               0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
               0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
               0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
               0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
               0.9552151 , 0.93901511, 0.22489288], # uniform
               [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0.],
               # fmt: on
           ]

           lowerCamelCase__   	=					model.generate(
               input_ids=A__						,	spout=spouts[0]						,	max_new_tokens=2_0						,	generation_config=A__						,	)

           lowerCamelCase__   	=					model.generate(
               input_ids=A__						,	spout=spouts[1]						,	max_new_tokens=2_0						,	generation_config=A__						,	)

           lowerCamelCase__   	=					model.generate(
               input_ids=A__						,	spout=A__						,	max_new_tokens=2_0						,	generation_config=A__						,	)

           lowerCamelCase__   	=					tokenizer.decode(outputa[0]      )
           lowerCamelCase__   	=					tokenizer.decode(outputa[0]      )
           lowerCamelCase__   	=					tokenizer.batch_decode(A__      )

           lowerCamelCase__   	=					[
               '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 			__snake_case (     self	: str      )					->							Union[str, Any]:
           lowerCamelCase__   	=					GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'      )
           lowerCamelCase__   	=					GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'      )
           model.to(A__      )

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

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

           lowerCamelCase__   	=					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=2_0						,	generation_config=A__						,	)

           lowerCamelCase__   	=					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=2_0						,	generation_config=A__						,	)

           lowerCamelCase__   	=					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=2_0						,	generation_config=A__						,	)

           lowerCamelCase__   	=					tokenizer.decode(outputa[0]      )
           lowerCamelCase__   	=					tokenizer.decode(outputa[0]      )
           lowerCamelCase__   	=					tokenizer.batch_decode(A__      )

           lowerCamelCase__   	=					[
               '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]      )





def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					53_81
      for x in s:
            lowerCamelCase__   	=					((hash_value << 5) + hash_value) + ord(lowerCamelCase    )
      return hash_value & 0Xffff_ffff