
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 		__lowerCamelCase     :



							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	,				)						->     List[str]:






								"""simple docstring"""



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



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






								"""simple docstring"""



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



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






								"""simple docstring"""



								UpperCAmelCase:					str 						=     ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size							)

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

								return (config, input_ids)



							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size							)

								UpperCAmelCase:					List[Any] 						=     self.get_config()

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



							def     A__ (     self							)						->     Any:






								"""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     A__ (     self	,				__snake_case	,				__snake_case	,				)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     GPTSanJapaneseForConditionalGeneration(config=__snake_case							)
								model.to(__snake_case							)
								model.eval()
								UpperCAmelCase:					Any 						=     model(
								    input_ids=__snake_case	,				)
								self.parent.assertIsNotNone(__snake_case							)





@require_torch
class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				int      =							(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__:				Optional[Any]      =							False
							lowerCamelCase__:				Dict      =							False
							lowerCamelCase__:				Optional[Any]      =							False
							lowerCamelCase__:				List[str]      =							False
							lowerCamelCase__:				Optional[int]      =							False
							lowerCamelCase__:				List[str]      =							False
							lowerCamelCase__:				Dict      =							False
							lowerCamelCase__:				int      =							False
							# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
							lowerCamelCase__:				Any      =							[0.8, 0.9]



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Optional[Any]:






								"""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     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     GPTSanJapaneseTester(self							)
								UpperCAmelCase:					Any 						=     ConfigTester(self	,				config_class=__snake_case	,				d_model=3_7							)



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






								"""simple docstring"""



								GPTSanJapaneseConfig()



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     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     A__ (     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     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								pass





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



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






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     GPTSanJapaneseTester(self							)
								UpperCAmelCase:					Dict 						=     ConfigTester(self	,				config_class=__snake_case	,				d_model=3_7							)



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






								"""simple docstring"""



								GPTSanJapaneseConfig()



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






								"""simple docstring"""



								UpperCAmelCase:					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     A__ (     self							)						->     Dict:






								"""simple docstring"""



								super().test_model_parallelism()



							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								pass



							@slow
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"							)
								UpperCAmelCase:					Optional[int] 						=     GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"							)
								UpperCAmelCase:					List[str] 						=     tokenizer.encode("武田信玄は"	,				return_tensors="pt"							)
								UpperCAmelCase:					Dict 						=     model(__snake_case							)
								UpperCAmelCase:					Any 						=     outputs.logits.detach().cpu().numpy()
								# Output of original model created with mesh-tensoflow
								UpperCAmelCase:					List[str] 						=     [
								    # 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
								]
								UpperCAmelCase:					Any 						=     np.array(__snake_case							).flatten()
								UpperCAmelCase:					Tuple 						=     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     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"							)
								UpperCAmelCase:					Dict 						=     GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"							)
								model.to(__snake_case							)

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

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

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

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

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

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

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

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

								UpperCAmelCase:					Union[str, Any] 						=     tokenizer.batch_decode(__snake_case	,				skip_special_tokens=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     tokenizer.decode(output_non_padded[0]	,				skip_special_tokens=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     tokenizer.decode(output_padded[0]	,				skip_special_tokens=__snake_case							)

								UpperCAmelCase:					Optional[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(__snake_case	,				__snake_case							)
								self.assertListEqual(__snake_case	,				[non_padded_sentence, padded_sentence]							)



							@tooslow
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"							)
								UpperCAmelCase:					int 						=     GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"							)
								# Output of original model created with mesh-tensoflow
								UpperCAmelCase:					Dict 						=     [
								    ("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:
									UpperCAmelCase:					Any 						=     tokenizer.encode(__snake_case	,				return_tensors="pt"							)
									UpperCAmelCase:					Optional[Any] 						=     model(__snake_case							)
									UpperCAmelCase:					int 						=     outputs.logits.detach().cpu().numpy()[0]
									UpperCAmelCase:					Optional[int] 						=     np.argmax(output_logits[-1]							)
									self.assertEqual(__snake_case	,				__snake_case							)



							@slow
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"							)
								UpperCAmelCase:					Tuple 						=     GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"							)
								model.to(__snake_case							)

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

								UpperCAmelCase:					Optional[Any] 						=     "武田信玄は、"
								UpperCAmelCase:					Union[str, Any] 						=     tokenizer(__snake_case	,				return_tensors="pt"							).input_ids.to(__snake_case							)
								UpperCAmelCase:					int 						=     tokenizer([input_text, input_text]	,				return_tensors="pt"							).input_ids.to(__snake_case							)

								# spout from uniform and one-hot
								UpperCAmelCase:					Union[str, Any] 						=     [
								    # 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
								]

								UpperCAmelCase:					Optional[int] 						=     model.generate(
								    input_ids=__snake_case	,				spout=spouts[0]	,				max_new_tokens=2_0	,				generation_config=__snake_case	,				)

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

								UpperCAmelCase:					Optional[int] 						=     model.generate(
								    input_ids=__snake_case	,				spout=__snake_case	,				max_new_tokens=2_0	,				generation_config=__snake_case	,				)

								UpperCAmelCase:					str 						=     tokenizer.decode(outputa[0]							)
								UpperCAmelCase:					Dict 						=     tokenizer.decode(outputa[0]							)
								UpperCAmelCase:					Any 						=     tokenizer.batch_decode(__snake_case							)

								UpperCAmelCase:					List[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     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"							)
								UpperCAmelCase:					Tuple 						=     GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"							)
								model.to(__snake_case							)

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

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

								UpperCAmelCase:					str 						=     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	,				)

								UpperCAmelCase:					Dict 						=     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	,				)

								UpperCAmelCase:					Tuple 						=     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	,				)

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

								UpperCAmelCase:					Optional[Any] 						=     [
								    "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 unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin





class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				int      =							UNetaDModel
							lowerCamelCase__:				Any      =							'''sample'''



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     4
								UpperCAmelCase:					Optional[Any] 						=     1_4
								UpperCAmelCase:					str 						=     1_6

								UpperCAmelCase:					Optional[int] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

								return {"sample": noise, "timestep": time_step}



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								return (4, 1_4, 1_6)



							@property
							def     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								return (4, 1_4, 1_6)



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								pass



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






								"""simple docstring"""



								pass



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								super().test_determinism()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								super().test_outputs_equivalence()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								super().test_from_save_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     str:






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								super().test_model_from_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								super().test_output()



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






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "time_embedding_type": "positional",
								    "use_timestep_embedding": True,
								    "flip_sin_to_cos": False,
								    "freq_shift": 1.0,
								    "out_block_type": "OutConv1DBlock",
								    "mid_block_type": "MidResTemporalBlock1D",
								    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
								    "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
								    "act_fn": "mish",
								}
								UpperCAmelCase:					Tuple 						=     self.dummy_input
								return init_dict, inputs_dict



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="unet"							)
								self.assertIsNotNone(__snake_case							)
								self.assertEqual(len(loading_info["missing_keys"]							)	,				0							)

								model.to(__snake_case							)
								UpperCAmelCase:					Tuple 						=     model(**self.dummy_input							)

								assert image is not None, "Make sure output is not None"



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     UNetaDModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32"	,				subfolder="unet"							)
								torch.manual_seed(0							)
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0							)

								UpperCAmelCase:					int 						=     model.config.in_channels
								UpperCAmelCase:					List[str] 						=     1_6
								UpperCAmelCase:					Union[str, Any] 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					List[str] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(__snake_case	,				__snake_case							).sample.permute(0	,				2	,				1							)

								UpperCAmelCase:					str 						=     output[0, -3:, -3:].flatten()
								# fmt: off
								UpperCAmelCase:					Dict 						=     torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass




							@slow
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     "harmonai/maestro-150k"
								UpperCAmelCase:					Optional[Any] 						=     UNetaDModel.from_pretrained(__snake_case	,				subfolder="unet"							)
								model.to(__snake_case							)

								UpperCAmelCase:					Any 						=     6_5_5_3_6
								UpperCAmelCase:					str 						=     torch.sin(torch.arange(__snake_case							)[None, None, :].repeat(1	,				2	,				1							)							).to(__snake_case							)
								UpperCAmelCase:					Dict 						=     torch.tensor([1]							).to(__snake_case							)

								with torch.no_grad():
									UpperCAmelCase:					Optional[Any] 						=     model(__snake_case	,				__snake_case							).sample

								UpperCAmelCase:					List[Any] 						=     output.abs().sum()
								UpperCAmelCase:					Dict 						=     output.abs().max()

								assert (output_sum - 2_24.08_96).abs() < 0.5
								assert (output_max - 0.06_07).abs() < 4e-4





class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				Dict      =							UNetaDModel
							lowerCamelCase__:				int      =							'''sample'''



							@property
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     4
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					Optional[int] 						=     1_6

								UpperCAmelCase:					List[str] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

								return {"sample": noise, "timestep": time_step}



							@property
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								return (4, 1_4, 1_6)



							@property
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								return (4, 1_4, 1)



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								super().test_determinism()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								super().test_outputs_equivalence()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								super().test_from_save_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								super().test_model_from_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Optional[int] 						=     self.prepare_init_args_and_inputs_for_common()
								UpperCAmelCase:					Tuple 						=     self.model_class(**__snake_case							)
								model.to(__snake_case							)
								model.eval()

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(**__snake_case							)

									if isinstance(__snake_case	,				__snake_case							):
										UpperCAmelCase:					List[str] 						=     output.sample

								self.assertIsNotNone(__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     torch.Size((inputs_dict["sample"].shape[0], 1)							)
								self.assertEqual(output.shape	,				__snake_case	,				"Input and output shapes do not match"							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
								    "up_block_types": [],
								    "out_block_type": "ValueFunction",
								    "mid_block_type": "ValueFunctionMidBlock1D",
								    "block_out_channels": [3_2, 6_4, 1_2_8, 2_5_6],
								    "layers_per_block": 1,
								    "downsample_each_block": True,
								    "use_timestep_embedding": True,
								    "freq_shift": 1.0,
								    "flip_sin_to_cos": False,
								    "time_embedding_type": "positional",
								    "act_fn": "mish",
								}
								UpperCAmelCase:					Union[str, Any] 						=     self.dummy_input
								return init_dict, inputs_dict



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="value_function"							)
								self.assertIsNotNone(__snake_case							)
								self.assertEqual(len(vf_loading_info["missing_keys"]							)	,				0							)

								value_function.to(__snake_case							)
								UpperCAmelCase:					Union[str, Any] 						=     value_function(**self.dummy_input							)

								assert image is not None, "Make sure output is not None"



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="value_function"							)
								torch.manual_seed(0							)
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0							)

								UpperCAmelCase:					str 						=     value_function.config.in_channels
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					int 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					Union[str, Any] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Dict 						=     value_function(__snake_case	,				__snake_case							).sample

								# fmt: off
								UpperCAmelCase:					Tuple 						=     torch.tensor([1_65.25] * seq_len							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)




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






								"""simple docstring"""



								pass
