

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]      )









import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def  UpperCamelCase_(							_snake_case     :						List[Any] ,  _snake_case     :						Optional[int]=0.999 ,  _snake_case     :						List[Any]="cosine" ,  ):




			"""simple docstring"""






			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_snake_case     :						Union[str, Any]			):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(_snake_case     :						Dict			):
									return math.exp(t * -12.0			)

			else:
						raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'			)

			__a						   =[]
			for i in range(_snake_case			):
						__a						   =i / num_diffusion_timesteps
						__a						   =(i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(_snake_case			) / alpha_bar_fn(_snake_case			) ,  _snake_case			)			)
			return torch.tensor(_snake_case ,  dtype=torch.floataa			)






class 			__magic_name__					(    lowerCAmelCase_						,					lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		[e.name for e in KarrasDiffusionSchedulers]
			SCREAMING_SNAKE_CASE							     =		2





			@register_to_config
			def __init__(	self      ,						__snake_case = 1000      ,						__snake_case = 0.0_0085      ,						__snake_case = 0.012      ,						__snake_case = "linear"      ,						__snake_case = None      ,						__snake_case = "epsilon"      ,						__snake_case = "linspace"      ,						__snake_case = 0      ,						)     ->				Union[str, Any]:




						'''simple docstring'''
						if trained_betas is not None:
									__a						   =torch.tensor(__snake_case      ,						dtype=torch.floataa      )
						elif beta_schedule == "linear":
									__a						   =torch.linspace(__snake_case      ,						__snake_case      ,						__snake_case      ,						dtype=torch.floataa      )
						elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									__a						   =(
									    torch.linspace(beta_start**0.5      ,						beta_end**0.5      ,						__snake_case      ,						dtype=torch.floataa      ) ** 2
									)
						elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__a						   =betas_for_alpha_bar(__snake_case      )
						else:
									raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'      )

						__a						   =1.0 - self.betas
						__a						   =torch.cumprod(self.alphas      ,						dim=0      )

						#  set all values
						self.set_timesteps(__snake_case      ,						__snake_case      ,						__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case=None      )     ->				Any:




						'''simple docstring'''
						if schedule_timesteps is None:
									__a						   =self.timesteps

						__a						   =(schedule_timesteps == timestep).nonzero()

						# The sigma index that is taken for the **very** first `step`
						# is always the second index (or the last index if there is only 1)
						# This way we can ensure we don't accidentally skip a sigma in
						# case we start in the middle of the denoising schedule (e.g. for image-to-image)
						if len(self._index_counter      ) == 0:
									__a						   =1 if len(__snake_case      ) > 1 else 0
						else:
									__a						   =timestep.cpu().item() if torch.is_tensor(__snake_case      ) else timestep
									__a						   =self._index_counter[timestep_int]

						return indices[pos].item()





			@property
			def 	__magic_name__					(	self      )     ->				Any:




						'''simple docstring'''
						# standard deviation of the initial noise distribution
						if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						)     ->				torch.FloatTensor:




						'''simple docstring'''
						__a						   =self.index_for_timestep(__snake_case      )

						if self.state_in_first_order:
									__a						   =self.sigmas[step_index]
						else:
									__a						   =self.sigmas_interpol[step_index - 1]

						__a						   =sample / ((sigma**2 + 1) ** 0.5)
						return sample





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case = None      ,						__snake_case = None      ,						)     ->				List[str]:




						'''simple docstring'''
						__a						   =num_inference_steps

						__a						   =num_train_timesteps or self.config.num_train_timesteps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
									__a						   =np.linspace(0      ,						num_train_timesteps - 1      ,						__snake_case      ,						dtype=__snake_case      )[::-1].copy()
						elif self.config.timestep_spacing == "leading":
									__a						   =num_train_timesteps // self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									__a						   =(np.arange(0      ,						__snake_case      ) * step_ratio).round()[::-1].copy().astype(__snake_case      )
									timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
									__a						   =num_train_timesteps / self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									__a						   =(np.arange(__snake_case      ,						0      ,						-step_ratio      )).round().copy().astype(__snake_case      )
									timesteps -= 1
						else:
									raise ValueError(
									    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'      )

						__a						   =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
						__a						   =torch.from_numpy(np.log(__snake_case      )      ).to(__snake_case      )

						__a						   =np.interp(__snake_case      ,						np.arange(0      ,						len(__snake_case      )      )      ,						__snake_case      )
						__a						   =np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
						__a						   =torch.from_numpy(__snake_case      ).to(device=__snake_case      )

						# compute up and down sigmas
						__a						   =sigmas.roll(-1      )
						__a						   =0.0
						__a						   =(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
						__a						   =(sigmas_next**2 - sigmas_up**2) ** 0.5
						__a						   =0.0

						# compute interpolated sigmas
						__a						   =sigmas.log().lerp(sigmas_down.log()      ,						0.5      ).exp()
						__a						   =0.0

						# set sigmas
						__a						   =torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
						__a						   =torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
						__a						   =torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
						__a						   =torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

						if str(__snake_case      ).startswith('mps'      ):
									# mps does not support float64
									__a						   =torch.from_numpy(__snake_case      ).to(__snake_case      ,						dtype=torch.floataa      )
						else:
									__a						   =torch.from_numpy(__snake_case      ).to(__snake_case      )

						__a						   =self.sigma_to_t(__snake_case      ).to(__snake_case      ,						dtype=timesteps.dtype      )
						__a						   =torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])      ,						dim=-1      ).flatten()

						__a						   =torch.cat([timesteps[:1], interleaved_timesteps]      )

						__a						   =None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						__a						   =defaultdict(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				int:




						'''simple docstring'''
						# get log sigma
						__a						   =sigma.log()

						# get distribution
						__a						   =log_sigma - self.log_sigmas[:, None]

						# get sigmas range
						__a						   =dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
						__a						   =low_idx + 1

						__a						   =self.log_sigmas[low_idx]
						__a						   =self.log_sigmas[high_idx]

						# interpolate sigmas
						__a						   =(low - log_sigma) / (low - high)
						__a						   =w.clamp(0      ,						1      )

						# transform interpolation to time range
						__a						   =(1 - w) * low_idx + w * high_idx
						__a						   =t.view(sigma.shape      )
						return t





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




						'''simple docstring'''
						return self.sample is None





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case = None      ,						__snake_case = True      ,						)     ->				Union[SchedulerOutput, Tuple]:




						'''simple docstring'''
						__a						   =self.index_for_timestep(__snake_case      )

						# advance index counter by 1
						__a						   =timestep.cpu().item() if torch.is_tensor(__snake_case      ) else timestep
						self._index_counter[timestep_int] += 1

						if self.state_in_first_order:
									__a						   =self.sigmas[step_index]
									__a						   =self.sigmas_interpol[step_index]
									__a						   =self.sigmas_up[step_index]
									__a						   =self.sigmas_down[step_index - 1]
						else:
									# 2nd order / KPDM2's method
									__a						   =self.sigmas[step_index - 1]
									__a						   =self.sigmas_interpol[step_index - 1]
									__a						   =self.sigmas_up[step_index - 1]
									__a						   =self.sigmas_down[step_index - 1]

						# currently only gamma=0 is supported. This usually works best anyways.
						# We can support gamma in the future but then need to scale the timestep before
						# passing it to the model which requires a change in API
						__a						   =0
						__a						   =sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

						__a						   =model_output.device
						__a						   =randn_tensor(model_output.shape      ,						dtype=model_output.dtype      ,						device=__snake_case      ,						generator=__snake_case      )

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
									__a						   =sigma_hat if self.state_in_first_order else sigma_interpol
									__a						   =sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
									__a						   =sigma_hat if self.state_in_first_order else sigma_interpol
									__a						   =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
									    sample / (sigma_input**2 + 1)
									)
						elif self.config.prediction_type == "sample":
									raise NotImplementedError('prediction_type not implemented yet: sample'      )
						else:
									raise ValueError(
									    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'      )

						if self.state_in_first_order:
									# 2. Convert to an ODE derivative for 1st order
									__a						   =(sample - pred_original_sample) / sigma_hat
									# 3. delta timestep
									__a						   =sigma_interpol - sigma_hat

									# store for 2nd order step
									__a						   =sample
									__a						   =dt
									__a						   =sample + derivative * dt
						else:
									# DPM-Solver-2
									# 2. Convert to an ODE derivative for 2nd order
									__a						   =(sample - pred_original_sample) / sigma_interpol
									# 3. delta timestep
									__a						   =sigma_down - sigma_hat

									__a						   =self.sample
									__a						   =None

									__a						   =sample + derivative * dt
									__a						   =prev_sample + noise * sigma_up

						if not return_dict:
									return (prev_sample,)

						return SchedulerOutput(prev_sample=__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						)     ->				torch.FloatTensor:




						'''simple docstring'''
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						__a						   =self.sigmas.to(device=original_samples.device      ,						dtype=original_samples.dtype      )
						if original_samples.device.type == "mps" and torch.is_floating_point(__snake_case      ):
									# mps does not support float64
									__a						   =self.timesteps.to(original_samples.device      ,						dtype=torch.floataa      )
									__a						   =timesteps.to(original_samples.device      ,						dtype=torch.floataa      )
						else:
									__a						   =self.timesteps.to(original_samples.device      )
									__a						   =timesteps.to(original_samples.device      )

						__a						   =[self.index_for_timestep(__snake_case      ,						__snake_case      ) for t in timesteps]

						__a						   =sigmas[step_indices].flatten()
						while len(sigma.shape      ) < len(original_samples.shape      ):
									__a						   =sigma.unsqueeze(-1      )

						__a						   =original_samples + noise * sigma
						return noisy_samples





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




						'''simple docstring'''
						return self.config.num_train_timesteps






