





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    :



      def __init__(					self     ,  lowercase_     ,  lowercase_=3_6000     ,  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_=100     ,  lowercase_=0.0     ,  )      ->   Any:



             '''simple docstring'''

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



      def    __a (					self					)      ->   int:



             '''simple docstring'''

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



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



             '''simple docstring'''

             _snake_case      :							Dict     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

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

             return (config, input_ids)



      def    __a (					self					)      ->   str:



             '''simple docstring'''

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

             _snake_case      :							int     				=						self.get_config()

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



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



             '''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    __a (					self     ,  lowercase_     ,  lowercase_     ,  )      ->   str:



             '''simple docstring'''

             _snake_case      :							int     				=						GPTSanJapaneseForConditionalGeneration(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _snake_case      :							str     				=						model(
                 input_ids=lowercase_     ,  )
             self.parent.assertIsNotNone(lowercase_					)


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



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   List[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    __a (					self					)      ->   List[str]:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						GPTSanJapaneseTester(self					)
             _snake_case      :							str     				=						ConfigTester(self     ,  config_class=lowercase_     ,  d_model=37					)



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             GPTSanJapaneseConfig()



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							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    __a (					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    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             pass


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



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						GPTSanJapaneseTester(self					)
             _snake_case      :							str     				=						ConfigTester(self     ,  config_class=lowercase_     ,  d_model=37					)



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             GPTSanJapaneseConfig()



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



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						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    __a (					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    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             pass



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



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
             _snake_case      :							List[str]     				=						GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
             _snake_case      :							Optional[int]     				=						tokenizer.encode('''武田信玄は'''     ,  return_tensors='''pt'''					)
             _snake_case      :							List[str]     				=						model(lowercase_					)
             _snake_case      :							List[Any]     				=						outputs.logits.detach().cpu().numpy()
             # Output of original model created with mesh-tensoflow
             _snake_case      :							List[str]     				=						[
                 # 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
             ]
             _snake_case      :							List[str]     				=						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    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

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

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

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

             _snake_case      :							str     				=						'''left'''
             _snake_case      :							Optional[int]     				=						tokenizer(lowercase_     ,  return_tensors='''pt'''     ,  padding=lowercase_					)
             _snake_case      :							Optional[int]     				=						inputs['''input_ids'''].to(lowercase_					)

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

             _snake_case      :							Optional[Any]     				=						model.generate(
                 input_ids=lowercase_     ,  attention_mask=inputs['''attention_mask'''].to(lowercase_					)     ,  max_new_tokens=3     ,  generation_config=lowercase_     ,  )

             _snake_case      :							Dict     				=						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[int]     				=						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      :							List[str]     				=						tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
             _snake_case      :							Any     				=						tokenizer.decode(output_non_padded[0]     ,  skip_special_tokens=lowercase_					)
             _snake_case      :							Union[str, Any]     				=						tokenizer.decode(output_padded[0]     ,  skip_special_tokens=lowercase_					)

             _snake_case      :							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    __a (					self					)      ->   Any:



             '''simple docstring'''

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



      @slow
      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

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

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

             _snake_case      :							List[str]     				=						'''武田信玄は、'''
             _snake_case      :							int     				=						tokenizer(lowercase_     ,  return_tensors='''pt'''					).input_ids.to(lowercase_					)
             _snake_case      :							Optional[Any]     				=						tokenizer([input_text, input_text]     ,  return_tensors='''pt'''					).input_ids.to(lowercase_					)

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

             _snake_case      :							Dict     				=						model.generate(
                 input_ids=lowercase_     ,  spout=spouts[0]     ,  max_new_tokens=20     ,  generation_config=lowercase_     ,  )

             _snake_case      :							Tuple     				=						model.generate(
                 input_ids=lowercase_     ,  spout=spouts[1]     ,  max_new_tokens=20     ,  generation_config=lowercase_     ,  )

             _snake_case      :							str     				=						model.generate(
                 input_ids=lowercase_     ,  spout=lowercase_     ,  max_new_tokens=20     ,  generation_config=lowercase_     ,  )

             _snake_case      :							Dict     				=						tokenizer.decode(outputa[0]					)
             _snake_case      :							Tuple     				=						tokenizer.decode(outputa[0]					)
             _snake_case      :							Optional[Any]     				=						tokenizer.batch_decode(lowercase_					)

             _snake_case      :							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(lowercase_     ,  lowercase_					)
             self.assertListEqual(lowercase_     ,  [outa_sentence, outa_sentence]					)



      @slow
      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							Any     				=						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
             _snake_case      :							Optional[int]     				=						GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
             model.to(lowercase_					)

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

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

             _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_					)     ,  max_new_tokens=20     ,  generation_config=lowercase_     ,  )

             _snake_case      :							List[Any]     				=						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      :							List[str]     				=						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      :							Optional[int]     				=						tokenizer.decode(outputa[0]					)
             _snake_case      :							Tuple     				=						tokenizer.decode(outputa[0]					)
             _snake_case      :							List[str]     				=						tokenizer.batch_decode(lowercase_					)

             _snake_case      :							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(lowercase_     ,  lowercase_					)
             self.assertListEqual(lowercase_     ,  [outa_sentence, outa_sentence]					)













import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCAmelCase_   =   logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowerCAmelCase_   =   {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCAmelCase_   =   {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCAmelCase_   =   sorted(arg_to_scheduler.keys())
lowerCAmelCase_   =   "{" + ", ".join(arg_to_scheduler_choices) + "}"

class 				A    (pl.LightningModule				):



      def __init__(					self     ,  lowercase_     ,  lowercase_=None     ,  lowercase_="base"     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  **lowercase_     ,  )      ->   Optional[int]:



             '''simple docstring'''

             super().__init__()
             # TODO: move to self.save_hyperparameters()
             # self.save_hyperparameters()
             # can also expand arguments into trainer signature for easier reading

             self.save_hyperparameters(lowercase_					)
             _snake_case      :							Union[str, Any]     				=						0
             _snake_case      :							Any     				=						Path(self.hparams.output_dir					)
             _snake_case      :							Optional[Any]     				=						self.hparams.cache_dir if self.hparams.cache_dir else None
             if config is None:
                    _snake_case      :							List[Any]     				=						AutoConfig.from_pretrained(
                        self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,  **({'''num_labels''': num_labels} if num_labels is not None else {})     ,  cache_dir=lowercase_     ,  **lowercase_     ,  )
             else:
                    _snake_case      :							PretrainedConfig     				=						config

             _snake_case      :							Optional[Any]     				=						('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
             for p in extra_model_params:
                    if getattr(self.hparams     ,  lowercase_     ,  lowercase_					):
                           assert hasattr(self.config     ,  lowercase_					), F'''model config doesn\'t have a `{p}` attribute'''
                           setattr(self.config     ,  lowercase_     ,  getattr(self.hparams     ,  lowercase_					)					)

             if tokenizer is None:
                    _snake_case      :							List[Any]     				=						AutoTokenizer.from_pretrained(
                        self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,  cache_dir=lowercase_     ,  )
             else:
                    _snake_case      :							PreTrainedTokenizer     				=						tokenizer
             _snake_case      :							str     				=						MODEL_MODES[mode]
             if model is None:
                    _snake_case      :							Optional[Any]     				=						self.model_type.from_pretrained(
                        self.hparams.model_name_or_path     ,  from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path					)     ,  config=self.config     ,  cache_dir=lowercase_     ,  )
             else:
                    _snake_case      :							Optional[int]     				=						model



      def    __a (					self     ,  *lowercase_     ,  **lowercase_					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.model_type.from_pretrained(*lowercase_     ,  **lowercase_					)



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						arg_to_scheduler[self.hparams.lr_scheduler]
             _snake_case      :							Optional[int]     				=						get_schedule_func(
                 self.opt     ,  num_warmup_steps=self.hparams.warmup_steps     ,  num_training_steps=self.total_steps()					)
             _snake_case      :							int     				=						{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
             return scheduler



      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.model
             _snake_case      :							List[Any]     				=						['''bias''', '''LayerNorm.weight''']
             _snake_case      :							Optional[Any]     				=						[
                 {
                     '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
                     '''weight_decay''': self.hparams.weight_decay,
                 },
                 {
                     '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)],
                     '''weight_decay''': 0.0,
                 },
             ]
             if self.hparams.adafactor:
                    _snake_case      :							Optional[int]     				=						Adafactor(
                        lowercase_     ,  lr=self.hparams.learning_rate     ,  scale_parameter=lowercase_     ,  relative_step=lowercase_					)

             else:
                    _snake_case      :							Optional[Any]     				=						AdamW(
                        lowercase_     ,  lr=self.hparams.learning_rate     ,  eps=self.hparams.adam_epsilon					)
             _snake_case      :							Optional[int]     				=						optimizer

             _snake_case      :							Tuple     				=						self.get_lr_scheduler()

             return [optimizer], [scheduler]



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Any:



             '''simple docstring'''

             return self.validation_step(lowercase_     ,  lowercase_					)



      def    __a (					self     ,  lowercase_					)      ->   Optional[Any]:



             '''simple docstring'''

             return self.validation_end(lowercase_					)



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						max(1     ,  self.hparams.gpus					)  # TODO: consider num_tpu_cores
             _snake_case      :							Dict     				=						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
             return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



      def    __a (					self     ,  lowercase_					)      ->   int:



             '''simple docstring'''

             if stage == "test":
                    _snake_case      :							Optional[Any]     				=						len(self.test_dataloader().dataset					)
             else:
                    _snake_case      :							Any     				=						self.get_dataloader('''train'''     ,  self.hparams.train_batch_size     ,  shuffle=lowercase_					)
                    _snake_case      :							Union[str, Any]     				=						len(self.train_dataloader().dataset					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = False					)      ->   Union[str, Any]:



             '''simple docstring'''

             raise NotImplementedError('''You must implement this for your task'''					)



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             return self.train_loader



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             return self.get_dataloader('''dev'''     ,  self.hparams.eval_batch_size     ,  shuffle=lowercase_					)



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



             '''simple docstring'''

             return self.get_dataloader('''test'''     ,  self.hparams.eval_batch_size     ,  shuffle=lowercase_					)



      def    __a (					self     ,  lowercase_					)      ->   Dict:



             '''simple docstring'''

             return os.path.join(
                 self.hparams.data_dir     ,  '''cached_{}_{}_{}'''.format(
                     lowercase_     ,  list(filter(lowercase_     ,  self.hparams.model_name_or_path.split('''/'''					)					)					).pop()     ,  str(self.hparams.max_seq_length					)     ,  )     ,  )



      @pl.utilities.rank_zero_only
      def    __a (					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.output_dir.joinpath('''best_tfmr'''					)
             _snake_case      :							str     				=						self.step_count
             self.model.save_pretrained(lowercase_					)
             self.tokenizer.save_pretrained(lowercase_					)



      @staticmethod
      def    __a (					lowercase_     ,  lowercase_					)      ->   Dict:



             '''simple docstring'''

             parser.add_argument(
                 '''--model_name_or_path'''     ,  default=lowercase_     ,  type=lowercase_     ,  required=lowercase_     ,  help='''Path to pretrained model or model identifier from huggingface.co/models'''     ,  )
             parser.add_argument(
                 '''--config_name'''     ,  default=''''''     ,  type=lowercase_     ,  help='''Pretrained config name or path if not the same as model_name'''					)
             parser.add_argument(
                 '''--tokenizer_name'''     ,  default=lowercase_     ,  type=lowercase_     ,  help='''Pretrained tokenizer name or path if not the same as model_name'''     ,  )
             parser.add_argument(
                 '''--cache_dir'''     ,  default=''''''     ,  type=lowercase_     ,  help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''     ,  )
             parser.add_argument(
                 '''--encoder_layerdrop'''     ,  type=lowercase_     ,  help='''Encoder layer dropout probability (Optional). Goes into model.config'''     ,  )
             parser.add_argument(
                 '''--decoder_layerdrop'''     ,  type=lowercase_     ,  help='''Decoder layer dropout probability (Optional). Goes into model.config'''     ,  )
             parser.add_argument(
                 '''--dropout'''     ,  type=lowercase_     ,  help='''Dropout probability (Optional). Goes into model.config'''     ,  )
             parser.add_argument(
                 '''--attention_dropout'''     ,  type=lowercase_     ,  help='''Attention dropout probability (Optional). Goes into model.config'''     ,  )
             parser.add_argument('''--learning_rate'''     ,  default=5E-5     ,  type=lowercase_     ,  help='''The initial learning rate for Adam.'''					)
             parser.add_argument(
                 '''--lr_scheduler'''     ,  default='''linear'''     ,  choices=lowercase_     ,  metavar=lowercase_     ,  type=lowercase_     ,  help='''Learning rate scheduler'''     ,  )
             parser.add_argument('''--weight_decay'''     ,  default=0.0     ,  type=lowercase_     ,  help='''Weight decay if we apply some.'''					)
             parser.add_argument('''--adam_epsilon'''     ,  default=1E-8     ,  type=lowercase_     ,  help='''Epsilon for Adam optimizer.'''					)
             parser.add_argument('''--warmup_steps'''     ,  default=0     ,  type=lowercase_     ,  help='''Linear warmup over warmup_steps.'''					)
             parser.add_argument('''--num_workers'''     ,  default=4     ,  type=lowercase_     ,  help='''kwarg passed to DataLoader'''					)
             parser.add_argument('''--num_train_epochs'''     ,  dest='''max_epochs'''     ,  default=3     ,  type=lowercase_					)
             parser.add_argument('''--train_batch_size'''     ,  default=32     ,  type=lowercase_					)
             parser.add_argument('''--eval_batch_size'''     ,  default=32     ,  type=lowercase_					)
             parser.add_argument('''--adafactor'''     ,  action='''store_true'''					)


class 				A    (pl.Callback				):



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   List[Any]:



             '''simple docstring'''

             if (
                 trainer.is_global_zero and trainer.global_rank == 0
             ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
                    pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.


class 				A    (pl.Callback				):



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						trainer.lr_schedulers[0]['''scheduler''']
             _snake_case      :							Any     				=						{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
             pl_module.logger.log_metrics(lowercase_					)



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[int]:



             '''simple docstring'''

             rank_zero_info('''***** Validation results *****'''					)
             _snake_case      :							Tuple     				=						trainer.callback_metrics
             # Log results
             for key in sorted(lowercase_					):
                    if key not in ["log", "progress_bar"]:
                           rank_zero_info('''{} = {}\n'''.format(lowercase_     ,  str(metrics[key]					)					)					)



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[Any]:



             '''simple docstring'''

             rank_zero_info('''***** Test results *****'''					)
             _snake_case      :							int     				=						trainer.callback_metrics
             # Log and save results to file
             _snake_case      :							Dict     				=						os.path.join(pl_module.hparams.output_dir     ,  '''test_results.txt'''					)
             with open(lowercase_     ,  '''w'''					) as writer:
                    for key in sorted(lowercase_					):
                           if key not in ["log", "progress_bar"]:
                                  rank_zero_info('''{} = {}\n'''.format(lowercase_     ,  str(metrics[key]					)					)					)
                                  writer.write('''{} = {}\n'''.format(lowercase_     ,  str(metrics[key]					)					)					)




def 						A_			(  lowercase_      ,		lowercase_  )			->						None:
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           '''--output_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		help='''The output directory where the model predictions and checkpoints will be written.'''      ,		)
       parser.add_argument(
           '''--fp16'''      ,		action='''store_true'''      ,		help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''      ,		)

       parser.add_argument(
           '''--fp16_opt_level'''      ,		type=lowercase_      ,		default='''O2'''      ,		help=(
               '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
               '''See details at https://nvidia.github.io/apex/amp.html'''
           )      ,		)
       parser.add_argument('''--n_tpu_cores'''      ,		dest='''tpu_cores'''      ,		type=lowercase_  )
       parser.add_argument('''--max_grad_norm'''      ,		dest='''gradient_clip_val'''      ,		default=1.0      ,		type=lowercase_      ,		help='''Max gradient norm'''  )
       parser.add_argument('''--do_train'''      ,		action='''store_true'''      ,		help='''Whether to run training.'''  )
       parser.add_argument('''--do_predict'''      ,		action='''store_true'''      ,		help='''Whether to run predictions on the test set.'''  )
       parser.add_argument(
           '''--gradient_accumulation_steps'''      ,		dest='''accumulate_grad_batches'''      ,		type=lowercase_      ,		default=1      ,		help='''Number of updates steps to accumulate before performing a backward/update pass.'''      ,		)
       parser.add_argument('''--seed'''      ,		type=lowercase_      ,		default=42      ,		help='''random seed for initialization'''  )
       parser.add_argument(
           '''--data_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''      ,		)




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=True      ,		lowercase_=None      ,		lowercase_=[]      ,		lowercase_=None      ,		lowercase_=None      ,		**lowercase_      ,		)			->						Optional[Any]:
       pl.seed_everything(args.seed  )

       # init model
       _snake_case      :							int     				=						Path(model.hparams.output_dir  )
       odir.mkdir(exist_ok=lowercase_  )

       # add custom checkpoints
       if checkpoint_callback is None:
              _snake_case      :							Union[str, Any]     				=						pl.callbacks.ModelCheckpoint(
                  filepath=args.output_dir      ,		prefix='''checkpoint'''      ,		monitor='''val_loss'''      ,		mode='''min'''      ,		save_top_k=1  )
       if early_stopping_callback:
              extra_callbacks.append(lowercase_  )
       if logging_callback is None:
              _snake_case      :							Optional[int]     				=						LoggingCallback()

       _snake_case      :							Union[str, Any]     				=						{}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              _snake_case      :							Any     				=						16
              # train_params["amp_level"] = args.fp16_opt_level

       if args.gpus > 1:
              _snake_case      :							List[str]     				=						'''auto'''  # "ddp"
              _snake_case      :							Dict     				=						'''ddp'''

       _snake_case      :							Optional[int]     				=						args.accumulate_grad_batches
       _snake_case      :							Optional[Any]     				=						None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
       _snake_case      :							Tuple     				=						'''auto'''

       _snake_case      :							int     				=						pl.Trainer.from_argparse_args(
           lowercase_      ,		weights_summary=lowercase_      ,		callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]      ,		logger=lowercase_      ,		**lowercase_      ,		)

       if args.do_train:
              trainer.fit(lowercase_  )

       return trainer






