




"""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       UpperCAmelCase_    :




    def __init__(       self		:			List[str]      ,					snake_case_		:			Dict      ,					snake_case_		:			Optional[Any]=36_000      ,					snake_case_		:			Any=13      ,					snake_case_		:			Dict=7      ,					snake_case_		:			Optional[Any]=True      ,					snake_case_		:			Any=32      ,					snake_case_		:			Union[str, Any]=42      ,					snake_case_		:			Tuple=5      ,					snake_case_		:			Tuple=2      ,					snake_case_		:			int=4      ,					snake_case_		:			Union[str, Any]=2      ,					snake_case_		:			List[Any]=32      ,					snake_case_		:			Any=80      ,					snake_case_		:			Optional[int]=33      ,					snake_case_		:			int=0.0      ,					snake_case_		:			str=1e-6      ,					snake_case_		:			Tuple=100      ,					snake_case_		:			Union[str, Any]=0.0      ,					) ->				Optional[int]:




     '''simple docstring'''

     A__			=							vocab_size
     A__			=							parent
     A__			=							batch_size
     A__			=							num_contexts
     # For common tests
     A__			=							self.num_contexts
     A__			=							is_training
     A__			=							hidden_size
     A__			=							num_ext_layers
     A__			=							ext_size
     A__			=							num_hidden_layers
     A__			=							num_attention_heads
     A__			=							num_experts
     A__			=							d_ff
     A__			=							d_ext
     A__			=							d_spout
     A__			=							dropout_rate
     A__			=							layer_norm_epsilon
     A__			=							expert_capacity
     A__			=							router_jitter_noise




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




     '''simple docstring'''

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




    def 				__magic_name__				(       self		:			Any ) ->				Tuple:




     '''simple docstring'''

     A__			=							ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size )

     A__			=							self.get_config()

     return (config, input_ids)




    def 				__magic_name__				(       self		:			Dict ) ->				int:




     '''simple docstring'''

     A__			=							ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size )

     A__			=							self.get_config()

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




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				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 				__magic_name__				(       self		:			Any      ,					snake_case_		:			List[str]      ,					snake_case_		:			int      ,					) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							GPTSanJapaneseForConditionalGeneration(config=snake_case_ )
     model.to(snake_case_ )
     model.eval()
     A__			=							model(
         input_ids=snake_case_      ,					)
     self.parent.assertIsNotNone(snake_case_ )






@require_torch
class       UpperCAmelCase_    (		A_,  A_,  unittest.TestCase				):
    lowercase__		   = (GPTSanJapaneseModel,) if is_torch_available() else ()
    lowercase__		   = (
        {
            '''conversational''': GPTSanJapaneseForConditionalGeneration,
            '''feature-extraction''': GPTSanJapaneseForConditionalGeneration,
            '''summarization''': GPTSanJapaneseForConditionalGeneration,
            '''text2text-generation''': GPTSanJapaneseForConditionalGeneration,
            '''translation''': GPTSanJapaneseForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    lowercase__		   = False
    lowercase__		   = False
    lowercase__		   = False
    lowercase__		   = False
    lowercase__		   = False
    lowercase__		   = False
    lowercase__		   = False
    lowercase__		   = False
    # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
    lowercase__		   = [0.8, 0.9]




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




     '''simple docstring'''

     A__			=							GPTSanJapaneseTester(self )
     A__			=							ConfigTester(self      ,					config_class=snake_case_      ,					d_model=37 )




    def 				__magic_name__				(       self		:			Any ) ->				str:




     '''simple docstring'''

     GPTSanJapaneseConfig()




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




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*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 				__magic_name__				(       self		:			Tuple ) ->				Dict:




     '''simple docstring'''

     super().test_model_parallelism()




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




     '''simple docstring'''

     pass






@require_torch
class       UpperCAmelCase_    (		A_,  A_,  unittest.TestCase				):
    lowercase__		   = (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
    lowercase__		   = False
    lowercase__		   = False
    lowercase__		   = False
    lowercase__		   = False
    lowercase__		   = False
    lowercase__		   = False
    # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
    lowercase__		   = [0.8, 0.9]




    def 				__magic_name__				(       self		:			Dict ) ->				Tuple:




     '''simple docstring'''

     A__			=							GPTSanJapaneseTester(self )
     A__			=							ConfigTester(self      ,					config_class=snake_case_      ,					d_model=37 )




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




     '''simple docstring'''

     GPTSanJapaneseConfig()




    def 				__magic_name__				(       self		:			Optional[Any] ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*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 				__magic_name__				(       self		:			Optional[int] ) ->				Tuple:




     '''simple docstring'''

     super().test_model_parallelism()




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




     '''simple docstring'''

     pass




    @slow
    def 				__magic_name__				(       self		:			List[str] ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese" )
     A__			=							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese" )
     A__			=							tokenizer.encode("武田信玄は"      ,					return_tensors="pt" )
     A__			=							model(snake_case_ )
     A__			=							outputs.logits.detach().cpu().numpy()
     # Output of original model created with mesh-tensoflow
     A__			=							[
         # fmt: off
         [-12.037_8398_8952_6367, -12.433_0615_9973_1445, -14.333_8403_7017_8223, -12.450_3459_9304_1992, -11.1_6613_7695_3125,
         -11.930_1376_3427_7344, -10.659_7404_4799_8047, -12.909_5745_0866_6992, -13.241_0430_9082_0312, -13.398_5795_9747_3145,
         -11.107_5248_7182_6172, -12.3_6859_4169_6167, -22.97_9431_1523_4375, -10.481_0676_5747_0703, -12.484_0307_2357_1777,
         -12.807_3606_4910_8887, -14.769_7000_5035_4004, -12.233_5796_3562_0117, -13.428_1454_0863_0371, -22.624_1779_3273_9258],
         [-7.511_1498_8327_0264, -8.281_8517_6849_3652, -7.943_1271_5530_3955, -7.55_0213_3369_4458, -6.49_8699_6650_6958,
         -7.586_7962_8372_1924, -6.978_0859_9472_0459, -7.839_1451_8356_3232, -8.21_9641_6854_8584, -8.695_0912_4755_8594,
         -6.706_9106_1019_8975, -6.6585_7982_6354_9805, -19.565_6986_2365_7227, -5.353_8427_3529_0527, -8.350_6860_7330_3223,
         -8.039_3886_5661_6211, -10.856_5692_9016_1133, -7.75_1544_4755_5542, -8.819_0221_7864_9902, -19.51_5327_4536_1328],
         [-9.73_0662_3458_8623, -10.223_9227_2949_2188, -9.932_9814_9108_8867, -11.857_8367_2332_7637, -7.662_6262_6647_9492,
         -11.13_5291_0995_4834, -7.765_0976_1810_3027, -11.472_9232_7880_8594, -9.543_1499_4812_0117, -11.905_6339_2639_1602,
         -9.366_1642_0745_8496, -11.5_7342_8153_9917, -23.699_0032_1960_4492, -9.429_5902_2521_9727, -10.42_8392_4102_7832,
         -10.585_2403_6407_4707, -10.94_7719_5739_7461, -11.095_4160_6903_0762, -10.390_2406_6925_0488, -23.769_3729_4006_3477],
         [-9.728_2657_6232_9102, -9.859_7126_0070_8008, -10.09_7292_9000_8545, -9.678_5221_0998_5352, -6.879_5199_3942_2607,
         -9.68_4875_4882_8125, -4.2803_4257_8887_9395, -10.018_9142_2271_7285, -9.308_4459_3048_0957, -10.63_3945_4650_8789,
         -8.083_6467_7429_1992, -9.06_3014_9841_3086, -21.904_2663_5742_1875, -8.90_1608_4671_0205, -8.841_8760_2996_8262,
         -11.856_7199_7070_3125, -12.079_3981_5521_2402, -11.233_7532_0434_5703, -10.177_3386_0015_8691, -21.87_2566_2231_4453],
         [-9.669_7645_1873_7793, -9.614_1986_8469_2383, -9.814_5103_4545_8984, -9.996_5019_2260_7422, -11.375_6904_6020_5078,
         -10.113_4052_2766_1133, -10.546_8673_7060_5469, -10.04_3690_6814_5752, -10.907_8092_5750_7324, -10.504_2161_9415_2832,
         -11.129_1990_2801_5137, -10.151_1240_0054_9316, -21.96_5867_9962_1582, -9.086_3494_8730_4688, -11.730_3390_5029_2969,
         -10.460_6676_1016_8457, -10.298_0499_2675_7812, -10.784_1482_1624_7559, -10.840_6934_7381_5918, -22.03_1528_4729_0039],
         # fmt: on
     ]
     A__			=							np.array(snake_case_ ).flatten()
     A__			=							output_logits[0, :, :20].flatten()

     def check(snake_case_		:			Dict      ,					snake_case_		:			int      ,					snake_case_		:			List[str]=5e-4 ):
      return abs(a - b ) < epsilon * max(abs(snake_case_ )      ,					abs(snake_case_ ) )

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




    @slow
    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				List[str]:




     '''simple docstring'''

     A__			=							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese" )
     A__			=							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese" )
     model.to(snake_case_ )

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

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

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

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

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

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

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

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

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

     A__			=							[
         "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(snake_case_      ,					snake_case_ )
     self.assertListEqual(snake_case_      ,					[non_padded_sentence, padded_sentence] )




    @tooslow
    def 				__magic_name__				(       self		:			str ) ->				Dict:




     '''simple docstring'''

     A__			=							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese" )
     A__			=							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese" )
     # Output of original model created with mesh-tensoflow
     A__			=							[
         ("武田信玄は", 35_675),
         ("武田信玄は、", 45),
         ("武田信玄は、この", 29),
         ("武田信玄は、このよう", 30_642),
         ("武田信玄は、このような", 35_680),
         ("武田信玄は、このような「", 8_640),
         ("武田信玄は、このような「武田", 31_617),
         ("武田信玄は、このような「武田家", 30_646),
         ("武田信玄は、このような「武田家の", 31_617),
         ("武田信玄は、このような「武田家の家", 31_381),
     ]
     for input, output in target:
      A__			=							tokenizer.encode(snake_case_      ,					return_tensors="pt" )
      A__			=							model(snake_case_ )
      A__			=							outputs.logits.detach().cpu().numpy()[0]
      A__			=							np.argmax(output_logits[-1] )
      self.assertEqual(snake_case_      ,					snake_case_ )




    @slow
    def 				__magic_name__				(       self		:			int ) ->				List[str]:




     '''simple docstring'''

     A__			=							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese" )
     A__			=							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese" )
     model.to(snake_case_ )

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

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

     # spout from uniform and one-hot
     A__			=							[
         # fmt: off
         [0.8788_2208, 0.3842_6396, 0.3322_0248, 0.4389_0406, 0.1656_2252,
         0.0480_3985, 0.21_1572  , 0.2318_8473, 0.3715_3068, 0.783_6377 ,
         0.0216_0172, 0.3876_1719, 0.7529_0772, 0.9019_8857, 0.3436_5777,
         0.6416_8169, 0.4431_8471, 0.1457_5746, 0.9256_2881, 0.4081_2148,
         0.2901_9122, 0.8886_1599, 0.6552_4846, 0.4356_3456, 0.3817_7187,
         0.7083_2965, 0.8152_7892, 0.6883_2812, 0.3883_3192, 0.456_1522 ,
         0.1482_8817, 0.4724_8213, 0.5435_7335, 0.8200_9566, 0.133_8884 ,
         0.0275_5417, 0.1976_4677, 0.242_2084 , 0.0475_7674, 0.6540_9606,
         0.082_4589 , 0.0330_4383, 0.9438_7689, 0.9876_4509, 0.8243_3901,
         0.2764_6741, 0.6490_7493, 0.7600_9406, 0.3008_7915, 0.1790_4689,
         0.4160_1714, 0.6704_6398, 0.1042_2822, 0.0844_7374, 0.0735_4344,
         0.6142_3565, 0.7028_4866, 0.753_2333 , 0.197_2038 , 0.2957_5659,
         0.9058_3886, 0.2926_5307, 0.5000_0175, 0.7040_7655, 0.88_9363  ,
         0.8190_4418, 0.6682_9128, 0.6446_8815, 0.5656_3723, 0.8560_1875,
         0.9492_4672, 0.0016_6762, 0.2522_0643, 0.7454_0219, 0.6799_3247,
         0.154_9675 , 0.3938_5352, 0.9215_3607, 0.6374_5931, 0.2775_9043,
         0.8470_2295, 0.6590_4271, 0.5867_6614, 0.866_6936 , 0.3960_7438,
         0.7995_4983, 0.4222_0697, 0.3965_0381, 0.784_9864 , 0.5615_0201,
         0.1567_8925, 0.1474_6032, 0.3454_2114, 0.4702_6783, 0.1195_6489,
         0.2542_1435, 0.3378_8901, 0.6893_4842, 0.3642_4685, 0.7173_7898,
         0.3898_3449, 0.9439_3779, 0.3957_5588, 0.3661_6553, 0.8710_4665,
         0.6463_0203, 0.2251_6905, 0.8827_0804, 0.1503_1338, 0.7514_4345,
         0.4645_9025, 0.8539_6454, 0.8635_5643, 0.6513_9851, 0.7026_6061,
         0.3024_1389, 0.8105_6497, 0.8886_5969, 0.3877_3807, 0.7063_5849,
         0.9071_8459, 0.4324_5789, 0.2800_0654, 0.4593_5562, 0.0877_3519,
         0.955_2151 , 0.9390_1511, 0.2248_9288], # 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
     ]

     A__			=							model.generate(
         input_ids=snake_case_      ,					spout=spouts[0]      ,					max_new_tokens=20      ,					generation_config=snake_case_      ,					)

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

     A__			=							model.generate(
         input_ids=snake_case_      ,					spout=snake_case_      ,					max_new_tokens=20      ,					generation_config=snake_case_      ,					)

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

     A__			=							[
         "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(snake_case_      ,					snake_case_ )
     self.assertListEqual(snake_case_      ,					[outa_sentence, outa_sentence] )




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




     '''simple docstring'''

     A__			=							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese" )
     A__			=							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese" )
     model.to(snake_case_ )

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

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

     A__			=							model.generate(
         input_ids=input_tok_a.input_ids.to(snake_case_ )      ,					token_type_ids=input_tok_a.token_type_ids.to(snake_case_ )      ,					max_new_tokens=20      ,					generation_config=snake_case_      ,					)

     A__			=							model.generate(
         input_ids=input_tok_a.input_ids.to(snake_case_ )      ,					token_type_ids=input_tok_a.token_type_ids.to(snake_case_ )      ,					max_new_tokens=20      ,					generation_config=snake_case_      ,					)

     A__			=							model.generate(
         input_ids=input_tok_a.input_ids.to(snake_case_ )      ,					token_type_ids=input_tok_a.token_type_ids.to(snake_case_ )      ,					attention_mask=input_tok_a.attention_mask.to(snake_case_ )      ,					max_new_tokens=20      ,					generation_config=snake_case_      ,					)

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

     A__			=							[
         "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(snake_case_      ,					snake_case_ )
     self.assertListEqual(snake_case_      ,					[outa_sentence, outa_sentence] )











"""simple docstring"""



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							=			{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPRContextEncoderTokenizerFast",
          "DPRQuestionEncoderTokenizerFast",
          "DPRReaderTokenizerFast",
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPRContextEncoder",
          "DPRPretrainedContextEncoder",
          "DPRPreTrainedModel",
          "DPRPretrainedQuestionEncoder",
          "DPRPretrainedReader",
          "DPRQuestionEncoder",
          "DPRReader",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDPRContextEncoder",
          "TFDPRPretrainedContextEncoder",
          "TFDPRPretrainedQuestionEncoder",
          "TFDPRPretrainedReader",
          "TFDPRQuestionEncoder",
          "TFDPRReader",
      ]


if TYPE_CHECKING:
      from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
      from .tokenization_dpr import (
          DPRContextEncoderTokenizer,
          DPRQuestionEncoderTokenizer,
          DPRReaderOutput,
          DPRReaderTokenizer,
      )

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_dpr_fast import (
                DPRContextEncoderTokenizerFast,
                DPRQuestionEncoderTokenizerFast,
                DPRReaderTokenizerFast,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpr import (
                DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPRContextEncoder,
                DPRPretrainedContextEncoder,
                DPRPreTrainedModel,
                DPRPretrainedQuestionEncoder,
                DPRPretrainedReader,
                DPRQuestionEncoder,
                DPRReader,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_dpr import (
                TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDPRContextEncoder,
                TFDPRPretrainedContextEncoder,
                TFDPRPretrainedQuestionEncoder,
                TFDPRPretrainedReader,
                TFDPRQuestionEncoder,
                TFDPRReader,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





