





import unittest

import numpy as np

from transformers import (
    GPTSanJapaneseConfig,
    GPTSanJapaneseForConditionalGeneration,
    GPTSanJapaneseModel,
    GPTSanJapaneseTokenizer,
    is_torch_available,
)
from transformers.generation import GenerationConfig
from transformers.testing_utils import require_torch, slow, tooslow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin






class 							_a:







		def __init__(     self			,    __snake_case			,    __snake_case=3_6_0_0_0			,    __snake_case=1_3			,    __snake_case=7			,    __snake_case=True			,    __snake_case=3_2			,    __snake_case=4_2			,    __snake_case=5			,    __snake_case=2			,    __snake_case=4			,    __snake_case=2			,    __snake_case=3_2			,    __snake_case=8_0			,    __snake_case=3_3			,    __snake_case=0.0			,    __snake_case=1E-6			,    __snake_case=1_0_0			,    __snake_case=0.0			,    )   ->   str:


							'''simple docstring'''

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







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


							'''simple docstring'''

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







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

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

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

							return (config, input_ids)







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


							'''simple docstring'''

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

							_snake_case							:       Optional[int]												= self.get_config()

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







		def 							lowercase							(     self					)   ->   Tuple:


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


							'''simple docstring'''

							_snake_case							:       Union[str, Any]												= GPTSanJapaneseForConditionalGeneration(config=__snake_case					)
							model.to(__snake_case					)
							model.eval()
							_snake_case							:       Optional[Any]												= model(
							    input_ids=__snake_case			,    )
							self.parent.assertIsNotNone(__snake_case					)






@require_torch
class 							_a(     __A						, __A						, unittest.TestCase     ):
		lowerCamelCase__  :Dict 					=		(GPTSanJapaneseModel,) if is_torch_available() else ()
		lowerCamelCase__  :List[str] 					=		(
		    {
		        'conversational': GPTSanJapaneseForConditionalGeneration,
		        'feature-extraction': GPTSanJapaneseForConditionalGeneration,
		        'summarization': GPTSanJapaneseForConditionalGeneration,
		        'text2text-generation': GPTSanJapaneseForConditionalGeneration,
		        'translation': GPTSanJapaneseForConditionalGeneration,
		    }
		    if is_torch_available()
		    else {}
		)
		lowerCamelCase__  :List[Any] 					=		False
		lowerCamelCase__  :Tuple 					=		False
		lowerCamelCase__  :Optional[Any] 					=		False
		lowerCamelCase__  :Union[str, Any] 					=		False
		lowerCamelCase__  :Optional[int] 					=		False
		lowerCamelCase__  :Any 					=		False
		lowerCamelCase__  :List[Any] 					=		False
		lowerCamelCase__  :Dict 					=		False
		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
		lowerCamelCase__  :Optional[int] 					=		[0.8, 0.9]







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


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= GPTSanJapaneseTester(self					)
							_snake_case							:       List[str]												= ConfigTester(self			,    config_class=__snake_case			,    d_model=3_7					)







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							GPTSanJapaneseConfig()







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


							'''simple docstring'''

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


							'''simple docstring'''

							pass






@require_torch
class 							_a(     __A						, __A						, unittest.TestCase     ):
		lowerCamelCase__  :Tuple 					=		(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
		lowerCamelCase__  :str 					=		False
		lowerCamelCase__  :List[Any] 					=		False
		lowerCamelCase__  :Dict 					=		False
		lowerCamelCase__  :List[Any] 					=		False
		lowerCamelCase__  :int 					=		False
		lowerCamelCase__  :Dict 					=		False
		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
		lowerCamelCase__  :Dict 					=		[0.8, 0.9]







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


							'''simple docstring'''

							_snake_case							:       str												= GPTSanJapaneseTester(self					)
							_snake_case							:       Tuple												= ConfigTester(self			,    config_class=__snake_case			,    d_model=3_7					)







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


							'''simple docstring'''

							GPTSanJapaneseConfig()







		def 							lowercase							(     self					)   ->   Tuple:


							'''simple docstring'''

							_snake_case							:       Any												= 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 							lowercase							(     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 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							pass







		@slow
		def 							lowercase							(     self					)   ->   List[Any]:


							'''simple docstring'''

							_snake_case							:       Dict												= GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"					)
							_snake_case							:       Tuple												= GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"					)
							_snake_case							:       Optional[int]												= tokenizer.encode("武田信玄は"			,    return_tensors="pt"					)
							_snake_case							:       int												= model(__snake_case					)
							_snake_case							:       Dict												= outputs.logits.detach().cpu().numpy()
							# Output of original model created with mesh-tensoflow
							_snake_case							:       Union[str, Any]												= [
							    # fmt: off
							    [-12.0_37_83_98_89_52_63_67, -12.4_33_06_15_99_73_14_45, -14.3_33_84_03_70_17_82_23, -12.4_50_34_59_93_04_19_92, -11.1_66_13_76_95_31_25,
							    -11.9_30_13_76_34_27_73_44, -10.6_59_74_04_47_99_80_47, -12.9_09_57_45_08_66_69_92, -13.2_41_04_30_90_82_03_12, -13.3_98_57_95_97_47_31_45,
							    -11.1_07_52_48_71_82_61_72, -12.3_68_59_41_69_61_67, -22.97_94_31_15_23_43_75, -10.4_81_06_76_57_47_07_03, -12.4_84_03_07_23_57_17_77,
							    -12.8_07_36_06_49_10_88_87, -14.7_69_70_00_50_35_40_04, -12.2_33_57_96_35_62_01_17, -13.4_28_14_54_08_63_03_71, -22.6_24_17_79_32_73_92_58],
							    [-7.5_11_14_98_83_27_02_64, -8.2_81_85_17_68_49_36_52, -7.9_43_12_71_55_30_39_55, -7.55_02_13_33_69_44_58, -6.49_86_99_66_50_69_58,
							    -7.5_86_79_62_83_72_19_24, -6.9_78_08_59_94_72_04_59, -7.8_39_14_51_83_56_32_32, -8.21_96_41_68_54_85_84, -8.6_95_09_12_47_55_85_94,
							    -6.7_06_91_06_10_19_89_75, -6.65_85_79_82_63_54_98_05, -19.5_65_69_86_23_65_72_27, -5.3_53_84_27_35_29_05_27, -8.3_50_68_60_73_30_32_23,
							    -8.0_39_38_86_56_61_62_11, -10.8_56_56_92_90_16_11_33, -7.75_15_44_47_55_55_42, -8.8_19_02_21_78_64_99_02, -19.51_53_27_45_36_13_28],
							    [-9.73_06_62_34_58_86_23, -10.2_23_92_27_29_49_21_88, -9.9_32_98_14_91_08_88_67, -11.8_57_83_67_23_32_76_37, -7.6_62_62_62_66_47_94_92,
							    -11.13_52_91_09_95_48_34, -7.7_65_09_76_18_10_30_27, -11.4_72_92_32_78_80_85_94, -9.5_43_14_99_48_12_01_17, -11.9_05_63_39_26_39_16_02,
							    -9.3_66_16_42_07_45_84_96, -11.5_73_42_81_53_99_17, -23.6_99_00_32_19_60_44_92, -9.4_29_59_02_25_21_97_27, -10.42_83_92_41_02_78_32,
							    -10.5_85_24_03_64_07_47_07, -10.94_77_19_57_39_74_61, -11.0_95_41_60_69_03_07_62, -10.3_90_24_06_69_25_04_88, -23.7_69_37_29_40_06_34_77],
							    [-9.7_28_26_57_62_32_91_02, -9.8_59_71_26_00_70_80_08, -10.09_72_92_90_00_85_45, -9.6_78_52_21_09_98_53_52, -6.8_79_51_99_39_42_26_07,
							    -9.68_48_75_48_82_81_25, -4.28_03_42_57_88_87_93_95, -10.0_18_91_42_22_71_72_85, -9.3_08_44_59_30_48_09_57, -10.63_39_45_46_50_87_89,
							    -8.0_83_64_67_74_29_19_92, -9.06_30_14_98_41_30_86, -21.9_04_26_63_57_42_18_75, -8.90_16_08_46_71_02_05, -8.8_41_87_60_29_96_82_62,
							    -11.8_56_71_99_70_70_31_25, -12.0_79_39_81_55_21_24_02, -11.2_33_75_32_04_34_57_03, -10.1_77_33_86_00_15_86_91, -21.87_25_66_22_31_44_53],
							    [-9.6_69_76_45_18_73_77_93, -9.6_14_19_86_84_69_23_83, -9.8_14_51_03_45_45_89_84, -9.9_96_50_19_22_60_74_22, -11.3_75_69_04_60_20_50_78,
							    -10.1_13_40_52_27_66_11_33, -10.5_46_86_73_70_60_54_69, -10.04_36_90_68_14_57_52, -10.9_07_80_92_57_50_73_24, -10.5_04_21_61_94_15_28_32,
							    -11.1_29_19_90_28_01_51_37, -10.1_51_12_40_00_54_93_16, -21.96_58_67_99_62_15_82, -9.0_86_34_94_87_30_46_88, -11.7_30_33_90_50_29_29_69,
							    -10.4_60_66_76_10_16_84_57, -10.2_98_04_99_26_75_78_12, -10.7_84_14_82_16_24_75_59, -10.8_40_69_34_73_81_59_18, -22.03_15_28_47_29_00_39],
							    # fmt: on
							]
							_snake_case							:       int												= np.array(__snake_case					).flatten()
							_snake_case							:       int												= output_logits[0, :, :2_0].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 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

							_snake_case							:       Dict												= GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"					)
							_snake_case							:       Union[str, Any]												= GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"					)
							model.to(__snake_case					)

							# set deterministically
							_snake_case							:       int												= GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"					)
							_snake_case							:       Any												= 1

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

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

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

							_snake_case							:       Any												= model.generate(
							    input_ids=__snake_case			,    attention_mask=inputs["attention_mask"].to(__snake_case					)			,    max_new_tokens=3			,    generation_config=__snake_case			,    )

							_snake_case							:       List[Any]												= tokenizer(sentences[0]			,    return_tensors="pt"					).input_ids.to(__snake_case					)
							_snake_case							:       Optional[Any]												= model.generate(
							    input_ids=__snake_case			,    max_new_tokens=3			,    generation_config=__snake_case					)

							_snake_case							:       List[Any]												= tokenizer(sentences[1]			,    return_tensors="pt"					).input_ids.to(__snake_case					)
							_snake_case							:       Union[str, Any]												= model.generate(input_ids=__snake_case			,    max_new_tokens=3			,    generation_config=__snake_case					)

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

							_snake_case							:       Optional[Any]												= tokenizer.batch_decode(__snake_case			,    skip_special_tokens=__snake_case					)
							_snake_case							:       int												= tokenizer.decode(output_non_padded[0]			,    skip_special_tokens=__snake_case					)
							_snake_case							:       Any												= tokenizer.decode(output_padded[0]			,    skip_special_tokens=__snake_case					)

							_snake_case							:       Dict												= [
							    "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 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

							_snake_case							:       Tuple												= GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"					)
							_snake_case							:       Tuple												= GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"					)
							# Output of original model created with mesh-tensoflow
							_snake_case							:       Tuple												= [
							    ("武田信玄は", 3_5_6_7_5),
							    ("武田信玄は、", 4_5),
							    ("武田信玄は、この", 2_9),
							    ("武田信玄は、このよう", 3_0_6_4_2),
							    ("武田信玄は、このような", 3_5_6_8_0),
							    ("武田信玄は、このような「", 8_6_4_0),
							    ("武田信玄は、このような「武田", 3_1_6_1_7),
							    ("武田信玄は、このような「武田家", 3_0_6_4_6),
							    ("武田信玄は、このような「武田家の", 3_1_6_1_7),
							    ("武田信玄は、このような「武田家の家", 3_1_3_8_1),
							]
							for input, output in target:
												_snake_case							:       Union[str, Any]												= tokenizer.encode(__snake_case			,    return_tensors="pt"					)
												_snake_case							:       Dict												= model(__snake_case					)
												_snake_case							:       Union[str, Any]												= outputs.logits.detach().cpu().numpy()[0]
												_snake_case							:       Any												= np.argmax(output_logits[-1]					)
												self.assertEqual(__snake_case			,    __snake_case					)







		@slow
		def 							lowercase							(     self					)   ->   str:


							'''simple docstring'''

							_snake_case							:       Union[str, Any]												= GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"					)
							_snake_case							:       Tuple												= GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"					)
							model.to(__snake_case					)

							# set deterministically
							_snake_case							:       Optional[int]												= GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"					)
							_snake_case							:       Union[str, Any]												= 1

							_snake_case							:       str												= "武田信玄は、"
							_snake_case							:       List[Any]												= tokenizer(__snake_case			,    return_tensors="pt"					).input_ids.to(__snake_case					)
							_snake_case							:       Tuple												= tokenizer([input_text, input_text]			,    return_tensors="pt"					).input_ids.to(__snake_case					)

							# spout from uniform and one-hot
							_snake_case							:       Dict												= [
							    # fmt: off
							    [0.87_88_22_08, 0.38_42_63_96, 0.33_22_02_48, 0.43_89_04_06, 0.16_56_22_52,
							    0.04_80_39_85, 0.21_15_72  , 0.23_18_84_73, 0.37_15_30_68, 0.7_83_63_77 ,
							    0.02_16_01_72, 0.38_76_17_19, 0.75_29_07_72, 0.90_19_88_57, 0.34_36_57_77,
							    0.64_16_81_69, 0.44_31_84_71, 0.14_57_57_46, 0.92_56_28_81, 0.40_81_21_48,
							    0.29_01_91_22, 0.88_86_15_99, 0.65_52_48_46, 0.43_56_34_56, 0.38_17_71_87,
							    0.70_83_29_65, 0.81_52_78_92, 0.68_83_28_12, 0.38_83_31_92, 0.4_56_15_22 ,
							    0.14_82_88_17, 0.47_24_82_13, 0.54_35_73_35, 0.82_00_95_66, 0.1_33_88_84 ,
							    0.02_75_54_17, 0.19_76_46_77, 0.2_42_20_84 , 0.04_75_76_74, 0.65_40_96_06,
							    0.0_82_45_89 , 0.03_30_43_83, 0.94_38_76_89, 0.98_76_45_09, 0.82_43_39_01,
							    0.27_64_67_41, 0.64_90_74_93, 0.76_00_94_06, 0.30_08_79_15, 0.17_90_46_89,
							    0.41_60_17_14, 0.67_04_63_98, 0.10_42_28_22, 0.08_44_73_74, 0.07_35_43_44,
							    0.61_42_35_65, 0.70_28_48_66, 0.7_53_23_33 , 0.1_97_20_38 , 0.29_57_56_59,
							    0.90_58_38_86, 0.29_26_53_07, 0.50_00_01_75, 0.70_40_76_55, 0.88_93_63  ,
							    0.81_90_44_18, 0.66_82_91_28, 0.64_46_88_15, 0.56_56_37_23, 0.85_60_18_75,
							    0.94_92_46_72, 0.00_16_67_62, 0.25_22_06_43, 0.74_54_02_19, 0.67_99_32_47,
							    0.1_54_96_75 , 0.39_38_53_52, 0.92_15_36_07, 0.63_74_59_31, 0.27_75_90_43,
							    0.84_70_22_95, 0.65_90_42_71, 0.58_67_66_14, 0.8_66_69_36 , 0.39_60_74_38,
							    0.79_95_49_83, 0.42_22_06_97, 0.39_65_03_81, 0.7_84_98_64 , 0.56_15_02_01,
							    0.15_67_89_25, 0.14_74_60_32, 0.34_54_21_14, 0.47_02_67_83, 0.11_95_64_89,
							    0.25_42_14_35, 0.33_78_89_01, 0.68_93_48_42, 0.36_42_46_85, 0.71_73_78_98,
							    0.38_98_34_49, 0.94_39_37_79, 0.39_57_55_88, 0.36_61_65_53, 0.87_10_46_65,
							    0.64_63_02_03, 0.22_51_69_05, 0.88_27_08_04, 0.15_03_13_38, 0.75_14_43_45,
							    0.46_45_90_25, 0.85_39_64_54, 0.86_35_56_43, 0.65_13_98_51, 0.70_26_60_61,
							    0.30_24_13_89, 0.81_05_64_97, 0.88_86_59_69, 0.38_77_38_07, 0.70_63_58_49,
							    0.90_71_84_59, 0.43_24_57_89, 0.28_00_06_54, 0.45_93_55_62, 0.08_77_35_19,
							    0.9_55_21_51 , 0.93_90_15_11, 0.22_48_92_88], # uniform
							    [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0.],
							    # fmt: on
							]

							_snake_case							:       str												= model.generate(
							    input_ids=__snake_case			,    spout=spouts[0]			,    max_new_tokens=2_0			,    generation_config=__snake_case			,    )

							_snake_case							:       List[str]												= model.generate(
							    input_ids=__snake_case			,    spout=spouts[1]			,    max_new_tokens=2_0			,    generation_config=__snake_case			,    )

							_snake_case							:       List[str]												= model.generate(
							    input_ids=__snake_case			,    spout=__snake_case			,    max_new_tokens=2_0			,    generation_config=__snake_case			,    )

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

							_snake_case							:       Any												= [
							    "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 							lowercase							(     self					)   ->   Optional[int]:


							'''simple docstring'''

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

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

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

							_snake_case							:       int												= 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=2_0			,    generation_config=__snake_case			,    )

							_snake_case							:       List[Any]												= 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=2_0			,    generation_config=__snake_case			,    )

							_snake_case							:       List[Any]												= 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=2_0			,    generation_config=__snake_case			,    )

							_snake_case							:       str												= tokenizer.decode(outputa[0]					)
							_snake_case							:       Dict												= tokenizer.decode(outputa[0]					)
							_snake_case							:       Tuple												= tokenizer.batch_decode(__snake_case					)

							_snake_case							:       Optional[int]												= [
							    "武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して",
							    "織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残",
							]
							self.assertListEqual(__snake_case			,    __snake_case					)
							self.assertListEqual(__snake_case			,    [outa_sentence, outa_sentence]					)






import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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






@dataclass
class 							_a(     __A     ):
		lowerCamelCase__  :torch.FloatTensor
		lowerCamelCase__  :torch.FloatTensor







def  A					(				UpperCAmelCase	,	UpperCAmelCase=0.9_9_9	,	UpperCAmelCase="cosine"	,	):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(UpperCAmelCase					):
															return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(UpperCAmelCase					):
															return math.exp(t * -1_2.0					)

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

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






class 							_a(     __A						, __A     ):
		lowerCamelCase__  :Any 					=		1







		@register_to_config
		def __init__(     self			,    __snake_case = 1_0_0_0			,    __snake_case = 0.00_01			,    __snake_case = 0.02			,    __snake_case = "linear"			,    __snake_case = 0.0			,    __snake_case = None			,    __snake_case = True			,    )   ->   Tuple:


							'''simple docstring'''

							if trained_betas is not None:
												_snake_case							:       Any												= torch.from_numpy(__snake_case					)
							elif beta_schedule == "linear":
												_snake_case							:       List[Any]												= 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.
												_snake_case							:       Tuple												= (
												    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
												_snake_case							:       Optional[int]												= betas_for_alpha_bar(__snake_case					)
							elif beta_schedule == "sigmoid":
												# GeoDiff sigmoid schedule
												_snake_case							:       Union[str, Any]												= torch.linspace(-6			,    6			,    __snake_case					)
												_snake_case							:       Optional[Any]												= torch.sigmoid(__snake_case					) * (beta_end - beta_start) + beta_start
							else:
												raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""					)

							_snake_case							:       Optional[int]												= 1.0 - self.betas
							_snake_case							:       Tuple												= torch.cumprod(self.alphas			,    dim=0					)
							_snake_case							:       List[str]												= torch.tensor(1.0					)

							_snake_case							:       Any												= torch.tensor(1.0					)

							# standard deviation of the initial noise distribution
							_snake_case							:       int												= 1.0

							# setable values
							_snake_case							:       Optional[Any]												= None
							_snake_case							:       str												= torch.from_numpy(np.arange(0			,    __snake_case					)[::-1].copy()					)

							_snake_case							:       Any												= eta







		def 							lowercase							(     self			,    __snake_case			,    __snake_case = None					)   ->   torch.FloatTensor:


							'''simple docstring'''

							return sample







		def 							lowercase							(     self			,    __snake_case			,    __snake_case = 1_0			,    __snake_case = 1_0			,    __snake_case = None			,    )   ->   List[str]:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= min(self.config.num_train_timesteps			,    __snake_case					)
							_snake_case							:       Tuple												= num_inference_steps

							_snake_case							:       List[str]												= []

							_snake_case							:       str												= {}
							for j in range(0			,    num_inference_steps - jump_length			,    __snake_case					):
												_snake_case							:       Tuple												= jump_n_sample - 1

							_snake_case							:       int												= num_inference_steps
							while t >= 1:
												_snake_case							:       Union[str, Any]												= t - 1
												timesteps.append(__snake_case					)

												if jumps.get(__snake_case			,    0					) > 0:
																	_snake_case							:       Any												= jumps[t] - 1
																	for _ in range(__snake_case					):
																						_snake_case							:       List[str]												= t + 1
																						timesteps.append(__snake_case					)

							_snake_case							:       Optional[Any]												= np.array(__snake_case					) * (self.config.num_train_timesteps // self.num_inference_steps)
							_snake_case							:       int												= torch.from_numpy(__snake_case					).to(__snake_case					)







		def 							lowercase							(     self			,    __snake_case					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= t - self.config.num_train_timesteps // self.num_inference_steps

							_snake_case							:       Optional[int]												= self.alphas_cumprod[t]
							_snake_case							:       Union[str, Any]												= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case							:       Optional[Any]												= 1 - alpha_prod_t
							_snake_case							:       str												= 1 - alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from
							# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
							# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
							# variance to pred_sample
							# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
							# without eta.
							# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
							_snake_case							:       Dict												= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance







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


							'''simple docstring'''

							_snake_case							:       List[Any]												= timestep
							_snake_case							:       Optional[int]												= timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							_snake_case							:       Optional[Any]												= self.alphas_cumprod[t]
							_snake_case							:       Any												= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case							:       Dict												= 1 - alpha_prod_t

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							_snake_case							:       List[str]												= (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
												_snake_case							:       str												= torch.clamp(__snake_case			,    -1			,    1					)

							# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
							# substitute formula (7) in the algorithm coming from DDPM paper
							# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
							# DDIM schedule gives the same results as DDPM with eta = 1.0
							# Noise is being reused in 7. and 8., but no impact on quality has
							# been observed.

							# 5. Add noise
							_snake_case							:       Tuple												= model_output.device
							_snake_case							:       Any												= randn_tensor(model_output.shape			,    generator=__snake_case			,    device=__snake_case			,    dtype=model_output.dtype					)
							_snake_case							:       Tuple												= self.eta * self._get_variance(__snake_case					) ** 0.5

							_snake_case							:       str												= 0
							if t > 0 and self.eta > 0:
												_snake_case							:       List[str]												= std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case							:       List[str]												= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case							:       int												= alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

							# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case							:       Tuple												= (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

							# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case							:       int												= mask * prev_known_part + (1.0 - mask) * prev_unknown_part

							if not return_dict:
												return (
												    pred_prev_sample,
												    pred_original_sample,
												)

							return RePaintSchedulerOutput(prev_sample=__snake_case			,    pred_original_sample=__snake_case					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case=None					)   ->   Optional[Any]:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= self.config.num_train_timesteps // self.num_inference_steps

							for i in range(__snake_case					):
												_snake_case							:       List[Any]												= self.betas[timestep + i]
												if sample.device.type == "mps":
																	# randn does not work reproducibly on mps
																	_snake_case							:       List[Any]												= randn_tensor(sample.shape			,    dtype=sample.dtype			,    generator=__snake_case					)
																	_snake_case							:       int												= noise.to(sample.device					)
												else:
																	_snake_case							:       Any												= randn_tensor(sample.shape			,    generator=__snake_case			,    device=sample.device			,    dtype=sample.dtype					)

												# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
												_snake_case							:       List[Any]												= (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample







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


							'''simple docstring'''

							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."					)







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


							'''simple docstring'''

							return self.config.num_train_timesteps