
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	,  _snake_case	,  _snake_case=36_000	,  _snake_case=13	,  _snake_case=7	,  _snake_case=True	,  _snake_case=32	,  _snake_case=42	,  _snake_case=5	,  _snake_case=2	,  _snake_case=4	,  _snake_case=2	,  _snake_case=32	,  _snake_case=80	,  _snake_case=33	,  _snake_case=0.0	,  _snake_case=1E-6	,  _snake_case=100	,  _snake_case=0.0	,  )					->   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        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




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

								__a    							=					self.get_config()

								return (config, input_ids)




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   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        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict:


								'''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        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  )					->   List[str]:


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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case       )					->   int:


								'''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        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								__a    							=					GPTSanJapaneseTester(self       )
								__a    							=					ConfigTester(self	,  config_class=_snake_case	,  d_model=37       )




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


								'''simple docstring'''




								GPTSanJapaneseConfig()




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


								'''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        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''simple docstring'''




								super().test_model_parallelism()




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''       )
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								pass







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




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


								'''simple docstring'''




								__a    							=					GPTSanJapaneseTester(self       )
								__a    							=					ConfigTester(self	,  config_class=_snake_case	,  d_model=37       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								GPTSanJapaneseConfig()




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''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        SCREAMING_SNAKE_CASE_	(  self       )					->   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        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''simple docstring'''




								pass




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


								'''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	,  _snake_case	,  _snake_case=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        SCREAMING_SNAKE_CASE_	(  self       )					->   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        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''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        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''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        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[int]:


								'''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]       )

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import tensorflow as tf







class     __A(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]    ):




				def __init__(  self	,  _snake_case=None	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




								super().__init__(features=_snake_case       )
								__a    							=					tf_tensor_kwargs
								import tensorflow as tf  # noqa: import tf at initialization




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if isinstance(_snake_case	,  _snake_case       ) and column:
												if all(
												    isinstance(_snake_case	,  tf.Tensor       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
																return tf.stack(_snake_case       )
												elif all(
												    isinstance(_snake_case	,  (tf.Tensor, tf.RaggedTensor)       ) and x.ndim == 1 and x.dtype == column[0].dtype
												    for x in column       ):
																# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																return tf.ragged.stack(_snake_case       )

								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if value is None:
												return value

								__a    							=					{}

								if isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.integer       ):
												__a    							=					{'''dtype''': tf.intaa}
								elif isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.floating       ):
												__a    							=					{'''dtype''': tf.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(_snake_case	,  PIL.Image.Image       ):
																__a    							=					np.asarray(_snake_case       )

								return tf.convert_to_tensor(_snake_case	,  **{**default_dtype, **self.tf_tensor_kwargs}       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


								'''simple docstring'''




								import tensorflow as tf

								# support for torch, tf, jax etc.
								if config.TORCH_AVAILABLE and "torch" in sys.modules:
												import torch

												if isinstance(_snake_case	,  torch.Tensor       ):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
								if hasattr(_snake_case	,  '''__array__'''       ) and not isinstance(_snake_case	,  tf.Tensor       ):
												__a    							=					data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(_snake_case	,  np.ndarray       ):
												if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								elif isinstance(_snake_case	,  (list, tuple)       ):
												return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								return self._tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




								return map_nested(self._recursive_tensorize	,  _snake_case	,  map_list=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_row(_snake_case       )
								__a    							=					self.python_features_decoder.decode_row(_snake_case       )
								return self.recursive_tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   "tf.Tensor":


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_column(_snake_case       )
								__a    							=					self.python_features_decoder.decode_column(_snake_case	,  pa_table.column_names[0]       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								__a    							=					self._consolidate(_snake_case       )
								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_batch(_snake_case       )
								__a    							=					self.python_features_decoder.decode_batch(_snake_case       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								for column_name in batch:
												__a    							=					self._consolidate(batch[column_name]       )
								return batch