
"""simple docstring"""


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_			:



						'''simple docstring'''


						def __init__(	self       : str					,A_       : str					,A_       : Dict=3_6000					,A_       : Any=13					,A_       : List[str]=7					,A_       : List[Any]=True					,A_       : int=32					,A_       : Dict=42					,A_       : int=5					,A_       : Dict=2					,A_       : Optional[int]=4					,A_       : Any=2					,A_       : Union[str, Any]=32					,A_       : Dict=80					,A_       : Optional[int]=33					,A_       : Tuple=0.0					,A_       : List[str]=1e-6					,A_       : str=100					,A_       : str=0.0					,)       ->				str:
							A  				=				vocab_size
							A  				=				parent
							A  				=				batch_size
							A  				=				num_contexts
							# For common tests
							A  				=				self.num_contexts
							A  				=				is_training
							A  				=				hidden_size
							A  				=				num_ext_layers
							A  				=				ext_size
							A  				=				num_hidden_layers
							A  				=				num_attention_heads
							A  				=				num_experts
							A  				=				d_ff
							A  				=				d_ext
							A  				=				d_spout
							A  				=				dropout_rate
							A  				=				layer_norm_epsilon
							A  				=				expert_capacity
							A  				=				router_jitter_noise


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				str:
							return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese' )


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Optional[Any]:
							A  				=				ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size )

							A  				=				self.get_config()

							return (config, input_ids)


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				int:
							A  				=				ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size )

							A  				=				self.get_config()

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


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				Tuple:
							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   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : List[Any]					,A_       : Any					,)       ->				List[str]:
							A  				=				GPTSanJapaneseForConditionalGeneration(config=__UpperCamelCase )
							model.to(__UpperCamelCase )
							model.eval()
							A  				=				model(
							    input_ids=__UpperCamelCase					,)
							self.parent.assertIsNotNone(__UpperCamelCase )



@require_torch
class 			lowerCAmelCase_			( lowercase_						,   lowercase_						,   unittest.TestCase      ):



						'''simple docstring'''


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


						def   _SCREAMING_SNAKE_CASE (	self       : Dict					,A_       : List[str]					,A_       : Optional[int]					,A_       : str					,A_       : Union[str, Any]					,A_       : Optional[int] )       ->				Optional[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   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				int:
							A  				=				GPTSanJapaneseTester(self )
							A  				=				ConfigTester(self					,config_class=__UpperCamelCase					,d_model=37 )


						def   _SCREAMING_SNAKE_CASE (	self       : int )       ->				str:
							GPTSanJapaneseConfig()


						def   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				List[Any]:
							A  				=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCamelCase )


						@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   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Tuple:
							super().test_model_parallelism()




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



@require_torch
class 			lowerCAmelCase_			( lowercase_						,   lowercase_						,   unittest.TestCase      ):



						'''simple docstring'''


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


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				int:
							A  				=				GPTSanJapaneseTester(self )
							A  				=				ConfigTester(self					,config_class=__UpperCamelCase					,d_model=37 )


						def   _SCREAMING_SNAKE_CASE (	self       : List[str] )       ->				Tuple:
							GPTSanJapaneseConfig()


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Dict:
							A  				=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCamelCase )


						@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   _SCREAMING_SNAKE_CASE (	self       : int )       ->				Optional[int]:
							super().test_model_parallelism()


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


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

							def check(A_       : str					,A_       : Union[str, Any]					,A_       : Optional[Any]=5e-4 ):
								return abs(a - b ) < epsilon * max(abs(__UpperCamelCase )					,abs(__UpperCamelCase ) )

							self.assertTrue(np.all([check(target[i]					,predict[i] ) for i in range(len(__UpperCamelCase ) )] ) )


						@slow
						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				Optional[Any]:
							A  				=				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
							A  				=				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
							model.to(__UpperCamelCase )

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

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

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

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

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

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

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

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

							A  				=				tokenizer.batch_decode(__UpperCamelCase					,skip_special_tokens=__UpperCamelCase )
							A  				=				tokenizer.decode(output_non_padded[0]					,skip_special_tokens=__UpperCamelCase )
							A  				=				tokenizer.decode(output_padded[0]					,skip_special_tokens=__UpperCamelCase )

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


						@tooslow
						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				List[str]:
							A  				=				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
							A  				=				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
							# Output of original model created with mesh-tensoflow
							A  				=				[
							    ('武田信玄は', 3_5675),
							    ('武田信玄は、', 45),
							    ('武田信玄は、この', 29),
							    ('武田信玄は、このよう', 3_0642),
							    ('武田信玄は、このような', 3_5680),
							    ('武田信玄は、このような「', 8640),
							    ('武田信玄は、このような「武田', 3_1617),
							    ('武田信玄は、このような「武田家', 3_0646),
							    ('武田信玄は、このような「武田家の', 3_1617),
							    ('武田信玄は、このような「武田家の家', 3_1381),
							]
							for input, output in target:
								A  				=				tokenizer.encode(__UpperCamelCase					,return_tensors='pt' )
								A  				=				model(__UpperCamelCase )
								A  				=				outputs.logits.detach().cpu().numpy()[0]
								A  				=				np.argmax(output_logits[-1] )
								self.assertEqual(__UpperCamelCase					,__UpperCamelCase )


						@slow
						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				str:
							A  				=				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
							A  				=				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
							model.to(__UpperCamelCase )

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

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

							# spout from uniform and one-hot
							A  				=				[
							    # 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
							]

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

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

							A  				=				model.generate(
							    input_ids=__UpperCamelCase					,spout=__UpperCamelCase					,max_new_tokens=20					,generation_config=__UpperCamelCase					,)

							A  				=				tokenizer.decode(outputa[0] )
							A  				=				tokenizer.decode(outputa[0] )
							A  				=				tokenizer.batch_decode(__UpperCamelCase )

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




						@slow
						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				Optional[int]:
							A  				=				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
							A  				=				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
							model.to(__UpperCamelCase )

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

							A  				=				'武田信玄'
							A  				=				'織田信長'
							A  				=				'は、'
							A  				=				'が、'
							A  				=				tokenizer(__UpperCamelCase					,prefix_text=__UpperCamelCase					,return_tensors='pt' )
							A  				=				tokenizer(__UpperCamelCase					,prefix_text=__UpperCamelCase					,return_tensors='pt' )
							A  				=				tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]					,return_tensors='pt' )

							A  				=				model.generate(
							    input_ids=input_tok_a.input_ids.to(__UpperCamelCase )					,token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase )					,max_new_tokens=20					,generation_config=__UpperCamelCase					,)

							A  				=				model.generate(
							    input_ids=input_tok_a.input_ids.to(__UpperCamelCase )					,token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase )					,max_new_tokens=20					,generation_config=__UpperCamelCase					,)

							A  				=				model.generate(
							    input_ids=input_tok_a.input_ids.to(__UpperCamelCase )					,token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase )					,attention_mask=input_tok_a.attention_mask.to(__UpperCamelCase )					,max_new_tokens=20					,generation_config=__UpperCamelCase					,)

							A  				=				tokenizer.decode(outputa[0] )
							A  				=				tokenizer.decode(outputa[0] )
							A  				=				tokenizer.batch_decode(__UpperCamelCase )

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

"""simple docstring"""


import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling






def  _snake_case			(						snake_case__				:       Tuple       ):
	A  				=				'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

	with open(snake_case__		, 'wb'       ) as f:
		with requests.get(snake_case__		, stream=snake_case__       ) as req:
			for chunk in req.iter_content(chunk_size=2048       ):
				f.write(snake_case__       )






def  _snake_case			(						):
	A  				=				VivitConfig()

	A  				=				400
	A  				=				'huggingface/label-files'
	A  				=				'kinetics400-id2label.json'

	A  				=				json.load(open(hf_hub_download(snake_case__		, snake_case__		, repo_type='dataset'       )		, 'r'       )       )
	A  				=				{int(snake_case__       ): v for k, v in idalabel.items()}
	A  				=				idalabel
	A  				=				{v: k for k, v in idalabel.items()}
	return config






def  _snake_case			(						):
	A  				=				hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video'		, filename='eating_spaghetti_32_frames.npy'		, repo_type='dataset'       )
	A  				=				np.load(snake_case__       )
	return list(snake_case__       )






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	if np.ndim(snake_case__       ) == 2:
		return transform_attention_bias(snake_case__       )

	elif np.ndim(snake_case__       ) == 3:
		return transform_attention_kernel(snake_case__       )

	else:
		raise Exception(F'Invalid number of dimesions: {np.ndim(snake_case__       )}'       )






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return current.flatten()






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return np.reshape(snake_case__		, (current.shape[0], current.shape[1] * current.shape[2])       ).T






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return np.reshape(snake_case__		, (current.shape[0] * current.shape[1], current.shape[2])       ).T






def  _snake_case			(						snake_case__				:       str		, snake_case__				:       List[str]       ):
	A  				=				state_dict['optimizer']['target']['Transformer'][F'encoderblock_{i}']

	A  				=				F'encoder.layer.{i}.'
	A  				=				{
	    prefix + 'intermediate.dense.bias': state['MlpBlock_0']['Dense_0']['bias'],
	    prefix + 'intermediate.dense.weight': np.transpose(state['MlpBlock_0']['Dense_0']['kernel']       ),
	    prefix + 'output.dense.bias': state['MlpBlock_0']['Dense_1']['bias'],
	    prefix + 'output.dense.weight': np.transpose(state['MlpBlock_0']['Dense_1']['kernel']       ),
	    prefix + 'layernorm_before.bias': state['LayerNorm_0']['bias'],
	    prefix + 'layernorm_before.weight': state['LayerNorm_0']['scale'],
	    prefix + 'layernorm_after.bias': state['LayerNorm_1']['bias'],
	    prefix + 'layernorm_after.weight': state['LayerNorm_1']['scale'],
	    prefix
	    + 'attention.attention.query.bias': transform_attention(
	        state['MultiHeadDotProductAttention_0']['query']['bias']       ),
	    prefix
	    + 'attention.attention.query.weight': transform_attention(
	        state['MultiHeadDotProductAttention_0']['query']['kernel']       ),
	    prefix
	    + 'attention.attention.key.bias': transform_attention(state['MultiHeadDotProductAttention_0']['key']['bias']       ),
	    prefix
	    + 'attention.attention.key.weight': transform_attention(
	        state['MultiHeadDotProductAttention_0']['key']['kernel']       ),
	    prefix
	    + 'attention.attention.value.bias': transform_attention(
	        state['MultiHeadDotProductAttention_0']['value']['bias']       ),
	    prefix
	    + 'attention.attention.value.weight': transform_attention(
	        state['MultiHeadDotProductAttention_0']['value']['kernel']       ),
	    prefix + 'attention.output.dense.bias': state['MultiHeadDotProductAttention_0']['out']['bias'],
	    prefix
	    + 'attention.output.dense.weight': transform_attention_output_weight(
	        state['MultiHeadDotProductAttention_0']['out']['kernel']       ),
	}

	return new_state






def  _snake_case			(						snake_case__				:       Dict       ):
	return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]       )






def  _snake_case			(						snake_case__				:       Union[str, Any]		, snake_case__				:       Optional[int]=False       ):
	A  				=				get_n_layers(snake_case__       )

	A  				=				OrderedDict()

	A  				=				state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
	A  				=				state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

	A  				=				np.transpose(
	    state_dict['optimizer']['target']['embedding']['kernel']		, (4, 3, 0, 1, 2)       )
	A  				=				state_dict['optimizer']['target']['embedding']['bias']

	A  				=				state_dict['optimizer']['target']['cls']
	A  				=				state_dict['optimizer']['target']['Transformer']['posembed_input'][
	    'pos_embedding'
	]

	for i in range(snake_case__       ):
		new_state.update(transform_state_encoder_block(snake_case__		, snake_case__       )       )

	if classification_head:
		A  				=				{'vivit.' + k: v for k, v in new_state.items()}
		A  				=				np.transpose(state_dict['optimizer']['target']['output_projection']['kernel']       )
		A  				=				np.transpose(state_dict['optimizer']['target']['output_projection']['bias']       )

	return {k: torch.tensor(snake_case__       ) for k, v in new_state.items()}






def  _snake_case			(						):
	A  				=				VivitImageProcessor()

	assert extractor.do_resize is True
	assert extractor.size == {"shortest_edge": 256}
	assert extractor.do_center_crop is True
	assert extractor.crop_size == {"width": 224, "height": 224}
	assert extractor.resample == PILImageResampling.BILINEAR

	# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
	# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
	# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
	assert extractor.do_normalize is False
	assert extractor.do_rescale is True
	assert extractor.rescale_factor == 1 / 255

	# zero-centering = True in original implementation
	assert extractor.do_zero_centering is True

	return extractor






def  _snake_case			(						snake_case__				:       str       ):
	A  				=				'checkpoint'

	if not os.path.exists(snake_case__       ):
		download_checkpoint(snake_case__       )

	A  				=				restore_checkpoint(snake_case__		, snake_case__       )
	A  				=				transform_state(snake_case__		, classification_head=snake_case__       )

	A  				=				get_vivit_config()

	assert config.image_size == 224
	assert config.num_frames == 32

	A  				=				VivitForVideoClassification(snake_case__       )
	model.load_state_dict(snake_case__       )
	model.eval()

	A  				=				get_processor()

	A  				=				prepare_video()
	A  				=				extractor(snake_case__		, return_tensors='pt'       )

	A  				=				model(**snake_case__       )

	A  				=				torch.Size([1, 400]       )
	A  				=				torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]       )

	assert outputs.logits.shape == expected_shape
	assert torch.allclose(outputs.logits[0, :5]		, snake_case__		, atol=1e-4       ), outputs.logits[0, :5]

	model.save_pretrained(snake_case__       )
	extractor.save_pretrained(snake_case__       )


if __name__ == "__main__":
		_lowercase											=			argparse.ArgumentParser()

		parser.add_argument('''--output_model_name''', '''-o''', type=str, help='''Output path for the converted HuggingFace model''')

		_lowercase											=			parser.parse_args()
		convert(args.output_model_name)