


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 	_lowercase				:






				'''simple docstring'''



				def __init__(	self      :Tuple      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Tuple=36_000      ,      lowerCAmelCase__      :List[str]=13      ,      lowerCAmelCase__      :List[Any]=7      ,      lowerCAmelCase__      :List[str]=True      ,      lowerCAmelCase__      :List[str]=32      ,      lowerCAmelCase__      :Any=42      ,      lowerCAmelCase__      :List[str]=5      ,      lowerCAmelCase__      :Optional[Any]=2      ,      lowerCAmelCase__      :List[str]=4      ,      lowerCAmelCase__      :List[Any]=2      ,      lowerCAmelCase__      :Optional[int]=32      ,      lowerCAmelCase__      :int=80      ,      lowerCAmelCase__      :Tuple=33      ,      lowerCAmelCase__      :List[Any]=0.0      ,      lowerCAmelCase__      :Any=1E-6      ,      lowerCAmelCase__      :str=100      ,      lowerCAmelCase__      :Tuple=0.0      ,      )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		vocab_size
										__SCREAMING_SNAKE_CASE						:					str		      =		parent
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		batch_size
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		num_contexts
										# For common tests
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.num_contexts
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		is_training
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		hidden_size
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		num_ext_layers
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		ext_size
										__SCREAMING_SNAKE_CASE						:					str		      =		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:					Any		      =		num_attention_heads
										__SCREAMING_SNAKE_CASE						:					str		      =		num_experts
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		d_ff
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		d_ext
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		d_spout
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		dropout_rate
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		layer_norm_epsilon
										__SCREAMING_SNAKE_CASE						:					Any		      =		expert_capacity
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		router_jitter_noise


				def 		__magic_name__(	self      :Dict     )		->      List[str]:
										return GPTSanJapaneseConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''     )


				def 		__magic_name__(	self      :Optional[int]     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					Any		      =		ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size     )

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

										return (config, input_ids)


				def 		__magic_name__(	self      :Optional[int]     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.get_config()

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


				def 		__magic_name__(	self      :Optional[Any]     )		->      Any:
										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      :int      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :int      ,      )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					str		      =		GPTSanJapaneseForConditionalGeneration(config=lowercase__     )
										model.to(lowercase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		model(
										    input_ids=lowercase__      ,      )
										self.parent.assertIsNotNone(lowercase__     )




@require_torch
class 	_lowercase				(      _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		(GPTSanJapaneseModel,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE__    :      int      =		(
				    {
				        '''conversational''': GPTSanJapaneseForConditionalGeneration,
				        '''feature-extraction''': GPTSanJapaneseForConditionalGeneration,
				        '''summarization''': GPTSanJapaneseForConditionalGeneration,
				        '''text2text-generation''': GPTSanJapaneseForConditionalGeneration,
				        '''translation''': GPTSanJapaneseForConditionalGeneration,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE__    :      Dict      =		False
				SCREAMING_SNAKE_CASE__    :      Dict      =		False
				SCREAMING_SNAKE_CASE__    :      Optional[int]      =		False
				SCREAMING_SNAKE_CASE__    :      Dict      =		False
				SCREAMING_SNAKE_CASE__    :      List[str]      =		False
				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		False
				SCREAMING_SNAKE_CASE__    :      Optional[int]      =		False
				SCREAMING_SNAKE_CASE__    :      List[str]      =		False
				# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		[0.8, 0.9]


				def 		__magic_name__(	self      :Optional[Any]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :Dict     )		->      List[Any]:






										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      :str     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		GPTSanJapaneseTester(self     )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		ConfigTester(self      ,      config_class=lowercase__      ,      d_model=37     )


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


				def 		__magic_name__(	self      :Union[str, Any]     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					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 		__magic_name__(	self      :Tuple     )		->      List[str]:
										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      :List[str]     )		->      int:
										pass




@require_torch
class 	_lowercase				(      _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      Union[str, Any]      =		(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE__    :      Optional[int]      =		False
				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		False
				SCREAMING_SNAKE_CASE__    :      Any      =		False
				SCREAMING_SNAKE_CASE__    :      Optional[int]      =		False
				SCREAMING_SNAKE_CASE__    :      List[str]      =		False
				SCREAMING_SNAKE_CASE__    :      Any      =		False
				# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
				SCREAMING_SNAKE_CASE__    :      Tuple      =		[0.8, 0.9]


				def 		__magic_name__(	self      :List[Any]     )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					int		      =		GPTSanJapaneseTester(self     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		ConfigTester(self      ,      config_class=lowercase__      ,      d_model=37     )


				def 		__magic_name__(	self      :Any     )		->      str:
										GPTSanJapaneseConfig()


				def 		__magic_name__(	self      :Optional[Any]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					List[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 		__magic_name__(	self      :Tuple     )		->      Union[str, Any]:
										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      :Tuple     )		->      Tuple:
										pass


				@slow
				def 		__magic_name__(	self      :Dict     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''     )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		tokenizer.encode('''武田信玄は'''      ,      return_tensors='''pt'''     )
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model(lowercase__     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		outputs.logits.detach().cpu().numpy()
										# Output of original model created with mesh-tensoflow
										__SCREAMING_SNAKE_CASE						:					Dict		      =		[
										    # 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
										]
										__SCREAMING_SNAKE_CASE						:					str		      =		np.array(lowercase__     ).flatten()
										__SCREAMING_SNAKE_CASE						:					Dict		      =		output_logits[0, :, :20].flatten()

										def check(lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Dict=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 		__magic_name__(	self      :Tuple     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''     )
										model.to(lowercase__     )

										# set deterministically
										__SCREAMING_SNAKE_CASE						:					str		      =		GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		1

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

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		"left"
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		tokenizer(lowercase__      ,      return_tensors='''pt'''      ,      padding=lowercase__     )
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		inputs["input_ids"].to(lowercase__     )

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

										__SCREAMING_SNAKE_CASE						:					Any		      =		model.generate(
										    input_ids=lowercase__      ,      attention_mask=inputs['''attention_mask'''].to(lowercase__     )      ,      max_new_tokens=3      ,      generation_config=lowercase__      ,      )

										__SCREAMING_SNAKE_CASE						:					str		      =		tokenizer(sentences[0]      ,      return_tensors='''pt'''     ).input_ids.to(lowercase__     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		model.generate(
										    input_ids=lowercase__      ,      max_new_tokens=3      ,      generation_config=lowercase__     )

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		tokenizer(sentences[1]      ,      return_tensors='''pt'''     ).input_ids.to(lowercase__     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		model.generate(input_ids=lowercase__      ,      max_new_tokens=3      ,      generation_config=lowercase__     )

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

										__SCREAMING_SNAKE_CASE						:					Dict		      =		tokenizer.batch_decode(lowercase__      ,      skip_special_tokens=lowercase__     )
										__SCREAMING_SNAKE_CASE						:					str		      =		tokenizer.decode(output_non_padded[0]      ,      skip_special_tokens=lowercase__     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		tokenizer.decode(output_padded[0]      ,      skip_special_tokens=lowercase__     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		[
										    "甲斐なら武田と言うほど甲斐の武田",
										    "織田信長は、このような",
										]
										self.assertListEqual(lowercase__      ,      lowercase__     )
										self.assertListEqual(lowercase__      ,      [non_padded_sentence, padded_sentence]     )


				@tooslow
				def 		__magic_name__(	self      :Optional[int]     )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					str		      =		GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''     )
										__SCREAMING_SNAKE_CASE						:					str		      =		GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''     )
										# Output of original model created with mesh-tensoflow
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		[
										    ("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:
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		tokenizer.encode(lowercase__      ,      return_tensors='''pt'''     )
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		model(lowercase__     )
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		outputs.logits.detach().cpu().numpy()[0]
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		np.argmax(output_logits[-1]     )
																self.assertEqual(lowercase__      ,      lowercase__     )


				@slow
				def 		__magic_name__(	self      :Dict     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					Any		      =		GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''     )
										model.to(lowercase__     )

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

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		"武田信玄は、"
										__SCREAMING_SNAKE_CASE						:					int		      =		tokenizer(lowercase__      ,      return_tensors='''pt'''     ).input_ids.to(lowercase__     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		tokenizer([input_text, input_text]      ,      return_tensors='''pt'''     ).input_ids.to(lowercase__     )

										# spout from uniform and one-hot
										__SCREAMING_SNAKE_CASE						:					Any		      =		[
										    # 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
										]

										__SCREAMING_SNAKE_CASE						:					int		      =		model.generate(
										    input_ids=lowercase__      ,      spout=spouts[0]      ,      max_new_tokens=20      ,      generation_config=lowercase__      ,      )

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

										__SCREAMING_SNAKE_CASE						:					Dict		      =		model.generate(
										    input_ids=lowercase__      ,      spout=lowercase__      ,      max_new_tokens=20      ,      generation_config=lowercase__      ,      )

										__SCREAMING_SNAKE_CASE						:					str		      =		tokenizer.decode(outputa[0]     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		tokenizer.decode(outputa[0]     )
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		tokenizer.batch_decode(lowercase__     )

										__SCREAMING_SNAKE_CASE						:					List[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 		__magic_name__(	self      :str     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''     )
										model.to(lowercase__     )

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

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

										__SCREAMING_SNAKE_CASE						:					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__      ,      )

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

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		model.generate(
										    input_ids=input_tok_a.input_ids.to(lowercase__     )      ,      token_type_ids=input_tok_a.token_type_ids.to(lowercase__     )      ,      attention_mask=input_tok_a.attention_mask.to(lowercase__     )      ,      max_new_tokens=20      ,      generation_config=lowercase__      ,      )

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		tokenizer.decode(outputa[0]     )
										__SCREAMING_SNAKE_CASE						:					str		      =		tokenizer.decode(outputa[0]     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		tokenizer.batch_decode(lowercase__     )

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		[
										    "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 math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


__lowerCAmelCase     :			str      =logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      torch.FloatTensor
				SCREAMING_SNAKE_CASE__    :      Optional[torch.FloatTensor]      =		None




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__=0.999   ,	lowercase__="cosine"   ,	):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowercase__				):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

						elif alpha_transform_type == "exp":

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

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

						__SCREAMING_SNAKE_CASE						:					List[str]		      =		[]
						for i in range(lowercase__				):
												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		i / num_diffusion_timesteps
												__SCREAMING_SNAKE_CASE						:					Dict		      =		(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(lowercase__				) / alpha_bar_fn(lowercase__				)   ,	lowercase__				)				)
						return torch.tensor(lowercase__   ,	dtype=torch.floataa				)
class 	_lowercase				(      A__						,      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		[e.name for e in KarrasDiffusionSchedulers]
				SCREAMING_SNAKE_CASE__    :      Optional[int]      =		1


				@register_to_config
				def __init__(	self      :Tuple      ,      lowerCAmelCase__      :int = 1_000      ,      lowerCAmelCase__      :float = 0.0001      ,      lowerCAmelCase__      :float = 0.02      ,      lowerCAmelCase__      :str = "linear"      ,      lowerCAmelCase__      :Optional[Union[np.ndarray, List[float]]] = None      ,      lowerCAmelCase__      :str = "epsilon"      ,      lowerCAmelCase__      :str = "linear"      ,      lowerCAmelCase__      :Optional[bool] = False      ,      lowerCAmelCase__      :str = "linspace"      ,      lowerCAmelCase__      :int = 0      ,      )		->      Optional[int]:
										if trained_betas is not None:
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		torch.tensor(lowerCAmelCase__      ,      dtype=torch.floataa     )
										elif beta_schedule == "linear":
																__SCREAMING_SNAKE_CASE						:					str		      =		torch.linspace(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      dtype=torch.floataa     )
										elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		(
																    torch.linspace(beta_start**0.5      ,      beta_end**0.5      ,      lowerCAmelCase__      ,      dtype=torch.floataa     ) ** 2
																)
										elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																__SCREAMING_SNAKE_CASE						:					Dict		      =		betas_for_alpha_bar(lowerCAmelCase__     )
										else:
																raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''     )

										__SCREAMING_SNAKE_CASE						:					Dict		      =		1.0 - self.betas
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.cumprod(self.alphas      ,      dim=0     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		np.concatenate([sigmas[::-1], [0.0]]     ).astype(np.floataa     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.from_numpy(lowerCAmelCase__     )

										# setable values
										__SCREAMING_SNAKE_CASE						:					str		      =		None
										__SCREAMING_SNAKE_CASE						:					int		      =		np.linspace(0      ,      num_train_timesteps - 1      ,      lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )[::-1].copy()
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.from_numpy(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					str		      =		False
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		use_karras_sigmas


				@property
				def 		__magic_name__(	self      :int     )		->      Union[str, Any]:
										# 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      :Dict      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :Union[float, torch.FloatTensor]     )		->      torch.FloatTensor:
										if isinstance(lowerCAmelCase__      ,      torch.Tensor     ):
																__SCREAMING_SNAKE_CASE						:					Any		      =		timestep.to(self.timesteps.device     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		(self.timesteps == timestep).nonzero().item()
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.sigmas[step_index]

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		sample / ((sigma**2 + 1) ** 0.5)

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		True
										return sample


				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Union[str, torch.device] = None     )		->      int:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		num_inference_steps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		np.linspace(0      ,      self.config.num_train_timesteps - 1      ,      lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )[
																    ::-1
																].copy()
										elif self.config.timestep_spacing == "leading":
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.config.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
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		(np.arange(0      ,      lowerCAmelCase__     ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__     )
																timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.config.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
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		(np.arange(self.config.num_train_timesteps      ,      0      ,      -step_ratio     )).round().copy().astype(lowerCAmelCase__     )
																timesteps -= 1
										else:
																raise ValueError(
																    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
										__SCREAMING_SNAKE_CASE						:					int		      =		np.log(lowerCAmelCase__     )

										if self.config.interpolation_type == "linear":
																__SCREAMING_SNAKE_CASE						:					str		      =		np.interp(lowerCAmelCase__      ,      np.arange(0      ,      len(lowerCAmelCase__     )     )      ,      lowerCAmelCase__     )
										elif self.config.interpolation_type == "log_linear":
																__SCREAMING_SNAKE_CASE						:					Any		      =		torch.linspace(np.log(sigmas[-1]     )      ,      np.log(sigmas[0]     )      ,      num_inference_steps + 1     ).exp()
										else:
																raise ValueError(
																    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
																    ''' \'linear\' or \'log_linear\''''     )

										if self.use_karras_sigmas:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		self._convert_to_karras(in_sigmas=lowerCAmelCase__      ,      num_inference_steps=self.num_inference_steps     )
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		np.array([self._sigma_to_t(lowerCAmelCase__      ,      lowerCAmelCase__     ) for sigma in sigmas]     )

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__     )
										if str(lowerCAmelCase__     ).startswith('''mps'''     ):
																# mps does not support float64
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.from_numpy(lowerCAmelCase__     ).to(lowerCAmelCase__      ,      dtype=torch.floataa     )
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      int:
										# get log sigma
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		np.log(lowerCAmelCase__     )

										# get distribution
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										__SCREAMING_SNAKE_CASE						:					int		      =		np.cumsum((dists >= 0)      ,      axis=0     ).argmax(axis=0     ).clip(max=log_sigmas.shape[0] - 2     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		low_idx + 1

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		log_sigmas[low_idx]
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		log_sigmas[high_idx]

										# interpolate sigmas
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		(low - log_sigma) / (low - high)
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		np.clip(lowerCAmelCase__      ,      0      ,      1     )

										# transform interpolation to time range
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		(1 - w) * low_idx + w * high_idx
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		t.reshape(sigma.shape     )
										return t


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :Union[str, Any]     )		->      torch.FloatTensor:
										__SCREAMING_SNAKE_CASE						:					float		      =		in_sigmas[-1].item()
										__SCREAMING_SNAKE_CASE						:					float		      =		in_sigmas[0].item()

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		7.0  # 7.0 is the value used in the paper
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		np.linspace(0      ,      1      ,      lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					str		      =		sigma_min ** (1 / rho)
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		sigma_max ** (1 / rho)
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas


				def 		__magic_name__(	self      :Union[str, Any]      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :Union[float, torch.FloatTensor]      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :float = 0.0      ,      lowerCAmelCase__      :float = 0.0      ,      lowerCAmelCase__      :float = float('''inf'''     )      ,      lowerCAmelCase__      :float = 1.0      ,      lowerCAmelCase__      :Optional[torch.Generator] = None      ,      lowerCAmelCase__      :bool = True      ,      )		->      Union[EulerDiscreteSchedulerOutput, Tuple]:

										if (
										    isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     )
										    or isinstance(lowerCAmelCase__      ,      torch.IntTensor     )
										    or isinstance(lowerCAmelCase__      ,      torch.LongTensor     )
										):
																raise ValueError(
																    (
																        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
																        ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
																        ''' one of the `scheduler.timesteps` as a timestep.'''
																    )      ,      )

										if not self.is_scale_input_called:
																logger.warning(
																    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
																    '''See `StableDiffusionPipeline` for a usage example.'''     )

										if isinstance(lowerCAmelCase__      ,      torch.Tensor     ):
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		timestep.to(self.timesteps.device     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		(self.timesteps == timestep).nonzero().item()
										__SCREAMING_SNAKE_CASE						:					str		      =		self.sigmas[step_index]

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		min(s_churn / (len(self.sigmas     ) - 1)      ,      2**0.5 - 1     ) if s_tmin <= sigma <= s_tmax else 0.0

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		randn_tensor(
										    model_output.shape      ,      dtype=model_output.dtype      ,      device=model_output.device      ,      generator=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		noise * s_noise
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		sigma * (gamma + 1)

										if gamma > 0:
																__SCREAMING_SNAKE_CASE						:					str		      =		sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										# NOTE: "original_sample" should not be an expected prediction_type but is left in for
										# backwards compatibility
										if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																__SCREAMING_SNAKE_CASE						:					Any		      =		model_output
										elif self.config.prediction_type == "epsilon":
																__SCREAMING_SNAKE_CASE						:					Any		      =		sample - sigma_hat * model_output
										elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										else:
																raise ValueError(
																    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''     )

										# 2. Convert to an ODE derivative
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		(sample - pred_original_sample) / sigma_hat

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.sigmas[step_index + 1] - sigma_hat

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		sample + derivative * dt

										if not return_dict:
																return (prev_sample,)

										return EulerDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__      ,      pred_original_sample=lowerCAmelCase__     )


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :torch.FloatTensor      ,      )		->      torch.FloatTensor:
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.sigmas.to(device=original_samples.device      ,      dtype=original_samples.dtype     )
										if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__     ):
																# mps does not support float64
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.timesteps.to(original_samples.device      ,      dtype=torch.floataa     )
																__SCREAMING_SNAKE_CASE						:					int		      =		timesteps.to(original_samples.device      ,      dtype=torch.floataa     )
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.timesteps.to(original_samples.device     )
																__SCREAMING_SNAKE_CASE						:					Dict		      =		timesteps.to(original_samples.device     )

										__SCREAMING_SNAKE_CASE						:					Dict		      =		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		sigmas[step_indices].flatten()
										while len(sigma.shape     ) < len(original_samples.shape     ):
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		sigma.unsqueeze(-1     )

										__SCREAMING_SNAKE_CASE						:					Any		      =		original_samples + noise * sigma
										return noisy_samples



				def __len__(	self      :Optional[Any]     )		->      Union[str, Any]:
										return self.config.num_train_timesteps
