
'''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			,							lowercase_			,							lowercase_=3_60_00			,							lowercase_=13			,							lowercase_=7			,							lowercase_=True			,							lowercase_=32			,							lowercase_=42			,							lowercase_=5			,							lowercase_=2			,							lowercase_=4			,							lowercase_=2			,							lowercase_=32			,							lowercase_=80			,							lowercase_=33			,							lowercase_=0.0			,							lowercase_=1E-6			,							lowercase_=1_00			,							lowercase_=0.0			,							):
     snake_case_			:     Dict        =    vocab_size
     snake_case_			:     str        =    parent
     snake_case_			:     List[Any]        =    batch_size
     snake_case_			:     Dict        =    num_contexts
     # For common tests
     snake_case_			:     Optional[int]        =    self.num_contexts
     snake_case_			:     Optional[int]        =    is_training
     snake_case_			:     int        =    hidden_size
     snake_case_			:     Union[str, Any]        =    num_ext_layers
     snake_case_			:     Tuple        =    ext_size
     snake_case_			:     Optional[Any]        =    num_hidden_layers
     snake_case_			:     Optional[Any]        =    num_attention_heads
     snake_case_			:     Optional[Any]        =    num_experts
     snake_case_			:     str        =    d_ff
     snake_case_			:     Dict        =    d_ext
     snake_case_			:     Tuple        =    d_spout
     snake_case_			:     Tuple        =    dropout_rate
     snake_case_			:     List[str]        =    layer_norm_epsilon
     snake_case_			:     Tuple        =    expert_capacity
     snake_case_			:     int        =    router_jitter_noise





 def 							snake_case__		(     self):
     return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese")





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

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

     return (config, input_ids)





 def 							snake_case__		(     self):
     snake_case_			:     Any        =    ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size)

     snake_case_			:     str        =    self.get_config()

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





 def 							snake_case__		(     self):
     return GPTSanJapaneseConfig(
         vocab_size=self.vocab_size			,							num_contexts=self.seq_length			,							d_model=self.hidden_size			,							d_ff=self.d_ff			,							d_ext=self.d_ext			,							d_spout=self.d_spout			,							num_switch_layers=self.num_hidden_layers - self.num_ext_layers			,							num_ext_layers=self.num_ext_layers			,							num_heads=self.num_attention_heads			,							num_experts=self.num_experts			,							expert_capacity=self.expert_capacity			,							dropout_rate=self.dropout_rate			,							layer_norm_epsilon=self.layer_norm_epsilon			,							router_jitter_noise=self.router_jitter_noise			,							)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							):
     snake_case_			:     List[Any]        =    GPTSanJapaneseForConditionalGeneration(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_			:     Optional[Any]        =    model(
         input_ids=lowercase_			,							)
     self.parent.assertIsNotNone(lowercase_)






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





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_):
     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):
     snake_case_			:     Tuple        =    GPTSanJapaneseTester(self)
     snake_case_			:     List[Any]        =    ConfigTester(self			,							config_class=lowercase_			,							d_model=37)





 def 							snake_case__		(     self):
     GPTSanJapaneseConfig()





 def 							snake_case__		(     self):
     snake_case_			:     Optional[int]        =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_)





 @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):
     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):
     pass






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





 def 							snake_case__		(     self):
     snake_case_			:     Union[str, Any]        =    GPTSanJapaneseTester(self)
     snake_case_			:     Dict        =    ConfigTester(self			,							config_class=lowercase_			,							d_model=37)





 def 							snake_case__		(     self):
     GPTSanJapaneseConfig()





 def 							snake_case__		(     self):
     snake_case_			:     List[str]        =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_)





 @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):
     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):
     pass





 @slow
 def 							snake_case__		(     self):
     snake_case_			:     str        =    GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
     snake_case_			:     List[Any]        =    GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
     snake_case_			:     List[str]        =    tokenizer.encode("武田信玄は"			,							return_tensors="pt")
     snake_case_			:     Optional[int]        =    model(lowercase_)
     snake_case_			:     List[str]        =    outputs.logits.detach().cpu().numpy()
     # Output of original model created with mesh-tensoflow
     snake_case_			:     List[Any]        =    [
         # fmt: off
         [-12.037_839_889_526_367, -12.433_061_599_731_445, -14.333_840_370_178_223, -12.450_345_993_041_992, -11.1_661_376_953_125,
         -11.930_137_634_277_344, -10.659_740_447_998_047, -12.909_574_508_666_992, -13.241_043_090_820_312, -13.398_579_597_473_145,
         -11.107_524_871_826_172, -12.3_685_941_696_167, -22.97_943_115_234_375, -10.481_067_657_470_703, -12.484_030_723_571_777,
         -12.807_360_649_108_887, -14.769_700_050_354_004, -12.233_579_635_620_117, -13.428_145_408_630_371, -22.624_177_932_739_258],
         [-7.511_149_883_270_264, -8.281_851_768_493_652, -7.943_127_155_303_955, -7.55_021_333_694_458, -6.49_869_966_506_958,
         -7.586_796_283_721_924, -6.978_085_994_720_459, -7.839_145_183_563_232, -8.21_964_168_548_584, -8.695_091_247_558_594,
         -6.706_910_610_198_975, -6.6_585_798_263_549_805, -19.565_698_623_657_227, -5.353_842_735_290_527, -8.350_686_073_303_223,
         -8.039_388_656_616_211, -10.856_569_290_161_133, -7.75_154_447_555_542, -8.819_022_178_649_902, -19.51_532_745_361_328],
         [-9.73_066_234_588_623, -10.223_922_729_492_188, -9.932_981_491_088_867, -11.857_836_723_327_637, -7.662_626_266_479_492,
         -11.13_529_109_954_834, -7.765_097_618_103_027, -11.472_923_278_808_594, -9.543_149_948_120_117, -11.905_633_926_391_602,
         -9.366_164_207_458_496, -11.5_734_281_539_917, -23.699_003_219_604_492, -9.429_590_225_219_727, -10.42_839_241_027_832,
         -10.585_240_364_074_707, -10.94_771_957_397_461, -11.095_416_069_030_762, -10.390_240_669_250_488, -23.769_372_940_063_477],
         [-9.728_265_762_329_102, -9.859_712_600_708_008, -10.09_729_290_008_545, -9.678_522_109_985_352, -6.879_519_939_422_607,
         -9.68_487_548_828_125, -4.2_803_425_788_879_395, -10.018_914_222_717_285, -9.308_445_930_480_957, -10.63_394_546_508_789,
         -8.083_646_774_291_992, -9.06_301_498_413_086, -21.904_266_357_421_875, -8.90_160_846_710_205, -8.841_876_029_968_262,
         -11.856_719_970_703_125, -12.079_398_155_212_402, -11.233_753_204_345_703, -10.177_338_600_158_691, -21.87_256_622_314_453],
         [-9.669_764_518_737_793, -9.614_198_684_692_383, -9.814_510_345_458_984, -9.996_501_922_607_422, -11.375_690_460_205_078,
         -10.113_405_227_661_133, -10.546_867_370_605_469, -10.04_369_068_145_752, -10.907_809_257_507_324, -10.504_216_194_152_832,
         -11.129_199_028_015_137, -10.151_124_000_549_316, -21.96_586_799_621_582, -9.086_349_487_304_688, -11.730_339_050_292_969,
         -10.460_667_610_168_457, -10.298_049_926_757_812, -10.784_148_216_247_559, -10.840_693_473_815_918, -22.03_152_847_290_039],
         # fmt: on
     ]
     snake_case_			:     int        =    np.array(lowercase_).flatten()
     snake_case_			:     List[Any]        =    output_logits[0, :, :20].flatten()

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

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





 @slow
 def 							snake_case__		(     self):
     snake_case_			:     Tuple        =    GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
     snake_case_			:     int        =    GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
     model.to(lowercase_)

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

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

     snake_case_			:     Optional[Any]        =    "left"
     snake_case_			:     str        =    tokenizer(lowercase_			,							return_tensors="pt"			,							padding=lowercase_)
     snake_case_			:     List[str]        =    inputs["input_ids"].to(lowercase_)

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

     snake_case_			:     List[str]        =    model.generate(
         input_ids=lowercase_			,							attention_mask=inputs["attention_mask"].to(lowercase_)			,							max_new_tokens=3			,							generation_config=lowercase_			,							)

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

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

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

     snake_case_			:     str        =    tokenizer.batch_decode(lowercase_			,							skip_special_tokens=lowercase_)
     snake_case_			:     Optional[int]        =    tokenizer.decode(output_non_padded[0]			,							skip_special_tokens=lowercase_)
     snake_case_			:     str        =    tokenizer.decode(output_padded[0]			,							skip_special_tokens=lowercase_)

     snake_case_			:     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(lowercase_			,							lowercase_)
     self.assertListEqual(lowercase_			,							[non_padded_sentence, padded_sentence])





 @tooslow
 def 							snake_case__		(     self):
     snake_case_			:     Any        =    GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
     snake_case_			:     Dict        =    GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
     # Output of original model created with mesh-tensoflow
     snake_case_			:     List[Any]        =    [
         ("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:
         snake_case_			:     str        =    tokenizer.encode(lowercase_			,							return_tensors="pt")
         snake_case_			:     List[Any]        =    model(lowercase_)
         snake_case_			:     Dict        =    outputs.logits.detach().cpu().numpy()[0]
         snake_case_			:     Union[str, Any]        =    np.argmax(output_logits[-1])
         self.assertEqual(lowercase_			,							lowercase_)





 @slow
 def 							snake_case__		(     self):
     snake_case_			:     str        =    GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
     snake_case_			:     List[str]        =    GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
     model.to(lowercase_)

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

     snake_case_			:     Union[str, Any]        =    "武田信玄は、"
     snake_case_			:     int        =    tokenizer(lowercase_			,							return_tensors="pt").input_ids.to(lowercase_)
     snake_case_			:     str        =    tokenizer([input_text, input_text]			,							return_tensors="pt").input_ids.to(lowercase_)

     # spout from uniform and one-hot
     snake_case_			:     int        =    [
         # fmt: off
         [0.87_882_208, 0.38_426_396, 0.33_220_248, 0.43_890_406, 0.16_562_252,
         0.04_803_985, 0.211_572  , 0.23_188_473, 0.37_153_068, 0.7_836_377 ,
         0.02_160_172, 0.38_761_719, 0.75_290_772, 0.90_198_857, 0.34_365_777,
         0.64_168_169, 0.44_318_471, 0.14_575_746, 0.92_562_881, 0.40_812_148,
         0.29_019_122, 0.88_861_599, 0.65_524_846, 0.43_563_456, 0.38_177_187,
         0.70_832_965, 0.81_527_892, 0.68_832_812, 0.38_833_192, 0.4_561_522 ,
         0.14_828_817, 0.47_248_213, 0.54_357_335, 0.82_009_566, 0.1_338_884 ,
         0.02_755_417, 0.19_764_677, 0.2_422_084 , 0.04_757_674, 0.65_409_606,
         0.0_824_589 , 0.03_304_383, 0.94_387_689, 0.98_764_509, 0.82_433_901,
         0.27_646_741, 0.64_907_493, 0.76_009_406, 0.30_087_915, 0.17_904_689,
         0.41_601_714, 0.67_046_398, 0.10_422_822, 0.08_447_374, 0.07_354_344,
         0.61_423_565, 0.70_284_866, 0.7_532_333 , 0.1_972_038 , 0.29_575_659,
         0.90_583_886, 0.29_265_307, 0.50_000_175, 0.70_407_655, 0.889_363  ,
         0.81_904_418, 0.66_829_128, 0.64_468_815, 0.56_563_723, 0.85_601_875,
         0.94_924_672, 0.00_166_762, 0.25_220_643, 0.74_540_219, 0.67_993_247,
         0.1_549_675 , 0.39_385_352, 0.92_153_607, 0.63_745_931, 0.27_759_043,
         0.84_702_295, 0.65_904_271, 0.58_676_614, 0.8_666_936 , 0.39_607_438,
         0.79_954_983, 0.42_220_697, 0.39_650_381, 0.7_849_864 , 0.56_150_201,
         0.15_678_925, 0.14_746_032, 0.34_542_114, 0.47_026_783, 0.11_956_489,
         0.25_421_435, 0.33_788_901, 0.68_934_842, 0.36_424_685, 0.71_737_898,
         0.38_983_449, 0.94_393_779, 0.39_575_588, 0.36_616_553, 0.87_104_665,
         0.64_630_203, 0.22_516_905, 0.88_270_804, 0.15_031_338, 0.75_144_345,
         0.46_459_025, 0.85_396_454, 0.86_355_643, 0.65_139_851, 0.70_266_061,
         0.30_241_389, 0.81_056_497, 0.88_865_969, 0.38_773_807, 0.70_635_849,
         0.90_718_459, 0.43_245_789, 0.28_000_654, 0.45_935_562, 0.08_773_519,
         0.9_552_151 , 0.93_901_511, 0.22_489_288], # 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
     ]

     snake_case_			:     List[str]        =    model.generate(
         input_ids=lowercase_			,							spout=spouts[0]			,							max_new_tokens=20			,							generation_config=lowercase_			,							)

     snake_case_			:     Optional[int]        =    model.generate(
         input_ids=lowercase_			,							spout=spouts[1]			,							max_new_tokens=20			,							generation_config=lowercase_			,							)

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

     snake_case_			:     Optional[int]        =    tokenizer.decode(outputa[0])
     snake_case_			:     Any        =    tokenizer.decode(outputa[0])
     snake_case_			:     Union[str, Any]        =    tokenizer.batch_decode(lowercase_)

     snake_case_			:     List[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(lowercase_			,							lowercase_)
     self.assertListEqual(lowercase_			,							[outa_sentence, outa_sentence])





 @slow
 def 							snake_case__		(     self):
     snake_case_			:     str        =    GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
     snake_case_			:     str        =    GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
     model.to(lowercase_)

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

     snake_case_			:     Union[str, Any]        =    "武田信玄"
     snake_case_			:     Dict        =    "織田信長"
     snake_case_			:     int        =    "は、"
     snake_case_			:     List[Any]        =    "が、"
     snake_case_			:     List[str]        =    tokenizer(lowercase_			,							prefix_text=lowercase_			,							return_tensors="pt")
     snake_case_			:     List[str]        =    tokenizer(lowercase_			,							prefix_text=lowercase_			,							return_tensors="pt")
     snake_case_			:     Any        =    tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]			,							return_tensors="pt")

     snake_case_			:     Dict        =    model.generate(
         input_ids=input_tok_a.input_ids.to(lowercase_)			,							token_type_ids=input_tok_a.token_type_ids.to(lowercase_)			,							max_new_tokens=20			,							generation_config=lowercase_			,							)

     snake_case_			:     Optional[int]        =    model.generate(
         input_ids=input_tok_a.input_ids.to(lowercase_)			,							token_type_ids=input_tok_a.token_type_ids.to(lowercase_)			,							max_new_tokens=20			,							generation_config=lowercase_			,							)

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

     snake_case_			:     List[Any]        =    tokenizer.decode(outputa[0])
     snake_case_			:     Optional[Any]        =    tokenizer.decode(outputa[0])
     snake_case_			:     Any        =    tokenizer.batch_decode(lowercase_)

     snake_case_			:     str        =    [
         "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(lowercase_			,							lowercase_)
     self.assertListEqual(lowercase_			,							[outa_sentence, outa_sentence])






'''simple docstring'''
from typing import TYPE_CHECKING

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


a_           =    {
    "configuration_sam": [
        "SAM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SamConfig",
        "SamMaskDecoderConfig",
        "SamPromptEncoderConfig",
        "SamVisionConfig",
    ],
    "processing_sam": ["SamProcessor"],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    [
      "SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SamModel",
      "SamPreTrainedModel",
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    [
      "TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSamModel",
      "TFSamPreTrainedModel",
  ]
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    ["SamImageProcessor"]


if TYPE_CHECKING:
  from .configuration_sam import (
      SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SamConfig,
      SamMaskDecoderConfig,
      SamPromptEncoderConfig,
      SamVisionConfig,
  )
  from .processing_sam import SamProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_sam import SamImageProcessor

else:
  import sys

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




