

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 			__magic_name__					:





			def __init__(	self      ,						__snake_case      ,						__snake_case=3_6000      ,						__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      ,						)     ->				List[str]:




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





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




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





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




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

						__a						   =self.get_config()

						return (config, input_ids)





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




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

						__a						   =self.get_config()

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





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




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





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						)     ->				Dict:




						'''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 			__magic_name__					(    lowerCAmelCase_						,					lowerCAmelCase_						,					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 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )     ->				Optional[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 	__magic_name__					(	self      )     ->				Optional[int]:




						'''simple docstring'''
						__a						   =GPTSanJapaneseTester(self      )
						__a						   =ConfigTester(self      ,						config_class=__snake_case      ,						d_model=37      )





			def 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						GPTSanJapaneseConfig()





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




						'''simple docstring'''
						__a						   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__snake_case      )





			@unittest.skip(
			    reason='skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'      )
			def 	__magic_name__					(	self      )     ->				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 	__magic_name__					(	self      )     ->				str:




						'''simple docstring'''
						pass






@require_torch
class 			__magic_name__					(    lowerCAmelCase_						,					lowerCAmelCase_						,					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 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						__a						   =GPTSanJapaneseTester(self      )
						__a						   =ConfigTester(self      ,						config_class=__snake_case      ,						d_model=37      )





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




						'''simple docstring'''
						GPTSanJapaneseConfig()





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




						'''simple docstring'''
						super().test_model_parallelism()





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




						'''simple docstring'''
						pass





			@slow
			def 	__magic_name__					(	self      )     ->				str:




						'''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 	__magic_name__					(	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

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

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

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

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

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

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

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

						__a						   =tokenizer.batch_decode(__snake_case      ,						skip_special_tokens=__snake_case      )
						__a						   =tokenizer.decode(output_non_padded[0]      ,						skip_special_tokens=__snake_case      )
						__a						   =tokenizer.decode(output_padded[0]      ,						skip_special_tokens=__snake_case      )

						__a						   =[
						    '甲斐なら武田と言うほど甲斐の武田',
						    '織田信長は、このような',
						]
						self.assertListEqual(__snake_case      ,						__snake_case      )
						self.assertListEqual(__snake_case      ,						[non_padded_sentence, padded_sentence]      )





			@tooslow
			def 	__magic_name__					(	self      )     ->				Any:




						'''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', 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:
									__a						   =tokenizer.encode(__snake_case      ,						return_tensors='pt'      )
									__a						   =model(__snake_case      )
									__a						   =outputs.logits.detach().cpu().numpy()[0]
									__a						   =np.argmax(output_logits[-1]      )
									self.assertEqual(__snake_case      ,						__snake_case      )





			@slow
			def 	__magic_name__					(	self      )     ->				str:




						'''simple docstring'''
						__a						   =GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'      )
						__a						   =GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'      )
						model.to(__snake_case      )

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

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

						# spout from uniform and one-hot
						__a						   =[
						    # fmt: off
						    [0.8788_2208, 0.3842_6396, 0.3322_0248, 0.4389_0406, 0.1656_2252,
						    0.0480_3985, 0.21_1572  , 0.2318_8473, 0.3715_3068, 0.783_6377 ,
						    0.0216_0172, 0.3876_1719, 0.7529_0772, 0.9019_8857, 0.3436_5777,
						    0.6416_8169, 0.4431_8471, 0.1457_5746, 0.9256_2881, 0.4081_2148,
						    0.2901_9122, 0.8886_1599, 0.6552_4846, 0.4356_3456, 0.3817_7187,
						    0.7083_2965, 0.8152_7892, 0.6883_2812, 0.3883_3192, 0.456_1522 ,
						    0.1482_8817, 0.4724_8213, 0.5435_7335, 0.8200_9566, 0.133_8884 ,
						    0.0275_5417, 0.1976_4677, 0.242_2084 , 0.0475_7674, 0.6540_9606,
						    0.082_4589 , 0.0330_4383, 0.9438_7689, 0.9876_4509, 0.8243_3901,
						    0.2764_6741, 0.6490_7493, 0.7600_9406, 0.3008_7915, 0.1790_4689,
						    0.4160_1714, 0.6704_6398, 0.1042_2822, 0.0844_7374, 0.0735_4344,
						    0.6142_3565, 0.7028_4866, 0.753_2333 , 0.197_2038 , 0.2957_5659,
						    0.9058_3886, 0.2926_5307, 0.5000_0175, 0.7040_7655, 0.88_9363  ,
						    0.8190_4418, 0.6682_9128, 0.6446_8815, 0.5656_3723, 0.8560_1875,
						    0.9492_4672, 0.0016_6762, 0.2522_0643, 0.7454_0219, 0.6799_3247,
						    0.154_9675 , 0.3938_5352, 0.9215_3607, 0.6374_5931, 0.2775_9043,
						    0.8470_2295, 0.6590_4271, 0.5867_6614, 0.866_6936 , 0.3960_7438,
						    0.7995_4983, 0.4222_0697, 0.3965_0381, 0.784_9864 , 0.5615_0201,
						    0.1567_8925, 0.1474_6032, 0.3454_2114, 0.4702_6783, 0.1195_6489,
						    0.2542_1435, 0.3378_8901, 0.6893_4842, 0.3642_4685, 0.7173_7898,
						    0.3898_3449, 0.9439_3779, 0.3957_5588, 0.3661_6553, 0.8710_4665,
						    0.6463_0203, 0.2251_6905, 0.8827_0804, 0.1503_1338, 0.7514_4345,
						    0.4645_9025, 0.8539_6454, 0.8635_5643, 0.6513_9851, 0.7026_6061,
						    0.3024_1389, 0.8105_6497, 0.8886_5969, 0.3877_3807, 0.7063_5849,
						    0.9071_8459, 0.4324_5789, 0.2800_0654, 0.4593_5562, 0.0877_3519,
						    0.955_2151 , 0.9390_1511, 0.2248_9288], # uniform
						    [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
						     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
						     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
						     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
						     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
						     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
						     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
						     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
						     0., 0., 0., 0., 0., 0., 0., 0.],
						    # fmt: on
						]

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

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

						__a						   =model.generate(
						    input_ids=__snake_case      ,						spout=__snake_case      ,						max_new_tokens=20      ,						generation_config=__snake_case      ,						)

						__a						   =tokenizer.decode(outputa[0]      )
						__a						   =tokenizer.decode(outputa[0]      )
						__a						   =tokenizer.batch_decode(__snake_case      )

						__a						   =[
						    '武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である',
						    '武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討',
						]
						self.assertListEqual(__snake_case      ,						__snake_case      )
						self.assertListEqual(__snake_case      ,						[outa_sentence, outa_sentence]      )





			@slow
			def 	__magic_name__					(	self      )     ->				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 			__magic_name__					(    TensorFormatter[Mapping, 'tf.Tensor', Mapping]   ):





			def __init__(	self      ,						__snake_case=None      ,						**__snake_case      )     ->				Union[str, Any]:




						'''simple docstring'''
						super().__init__(features=__snake_case      )
						__a						   =tf_tensor_kwargs
						import tensorflow as tf  # noqa: import tf at initialization





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				int:




						'''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 	__magic_name__					(	self      ,						__snake_case      )     ->				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 	__magic_name__					(	self      ,						__snake_case      )     ->				Dict:




						'''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 	__magic_name__					(	self      ,						__snake_case      )     ->				Optional[Any]:




						'''simple docstring'''
						return map_nested(self._recursive_tensorize      ,						__snake_case      ,						map_list=__snake_case      )





			def 	__magic_name__					(	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 	__magic_name__					(	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 	__magic_name__					(	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






