




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        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__=3_60_00				,       lowerCAmelCase__=13				,       lowerCAmelCase__=7				,       lowerCAmelCase__=True				,       lowerCAmelCase__=32				,       lowerCAmelCase__=42				,       lowerCAmelCase__=5				,       lowerCAmelCase__=2				,       lowerCAmelCase__=4				,       lowerCAmelCase__=2				,       lowerCAmelCase__=32				,       lowerCAmelCase__=80				,       lowerCAmelCase__=33				,       lowerCAmelCase__=0.0				,       lowerCAmelCase__=1E-6				,       lowerCAmelCase__=1_00				,       lowerCAmelCase__=0.0				,       )					-> Tuple:

							'''simple docstring'''


							__lowercase			      =							vocab_size
							__lowercase			      =							parent
							__lowercase			      =							batch_size
							__lowercase			      =							num_contexts
							# For common tests
							__lowercase			      =							self.num_contexts
							__lowercase			      =							is_training
							__lowercase			      =							hidden_size
							__lowercase			      =							num_ext_layers
							__lowercase			      =							ext_size
							__lowercase			      =							num_hidden_layers
							__lowercase			      =							num_attention_heads
							__lowercase			      =							num_experts
							__lowercase			      =							d_ff
							__lowercase			      =							d_ext
							__lowercase			      =							d_spout
							__lowercase			      =							dropout_rate
							__lowercase			      =							layer_norm_epsilon
							__lowercase			      =							expert_capacity
							__lowercase			      =							router_jitter_noise





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

							'''simple docstring'''


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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size						)

							__lowercase			      =							self.get_config()

							return (config, input_ids)





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

							'''simple docstring'''


							__lowercase			      =							ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size						)

							__lowercase			      =							self.get_config()

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





			def 	_SCREAMING_SNAKE_CASE      (       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 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       )					-> Any:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration(config=lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase			      =							model(
							    input_ids=lowerCAmelCase__				,       )
							self.parent.assertIsNotNone(lowerCAmelCase__						)




@require_torch
class        _UpperCamelCase				(						A__						,A__						,unittest.TestCase			):


			"""simple docstring"""






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





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> 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 	_SCREAMING_SNAKE_CASE      (       self						)					-> Tuple:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseTester(self						)
							__lowercase			      =							ConfigTester(self				,       config_class=lowerCAmelCase__				,       d_model=37						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


							GPTSanJapaneseConfig()





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

							'''simple docstring'''


							__lowercase			      =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__						)





			@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[str]:

							'''simple docstring'''


							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						)					-> List[Any]:

							'''simple docstring'''


							pass




@require_torch
class        _UpperCamelCase				(						A__						,A__						,unittest.TestCase			):


			"""simple docstring"""






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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Tuple:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseTester(self						)
							__lowercase			      =							ConfigTester(self				,       config_class=lowerCAmelCase__				,       d_model=37						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							GPTSanJapaneseConfig()





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

							'''simple docstring'''


							__lowercase			      =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__						)





			@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						)					-> 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 	_SCREAMING_SNAKE_CASE      (       self						)					-> Tuple:

							'''simple docstring'''


							pass





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							tokenizer.encode('''武田信玄は'''				,       return_tensors='''pt'''						)
							__lowercase			      =							model(lowerCAmelCase__						)
							__lowercase			      =							outputs.logits.detach().cpu().numpy()
							# Output of original model created with mesh-tensoflow
							__lowercase			      =							[
							    # fmt: off
							    [-12.037_8398_8952_6367, -12.433_0615_9973_1445, -14.333_8403_7017_8223, -12.450_3459_9304_1992, -11.1_6613_7695_3125,
							    -11.930_1376_3427_7344, -10.659_7404_4799_8047, -12.909_5745_0866_6992, -13.241_0430_9082_0312, -13.398_5795_9747_3145,
							    -11.107_5248_7182_6172, -12.3_6859_4169_6167, -22.97_9431_1523_4375, -10.481_0676_5747_0703, -12.484_0307_2357_1777,
							    -12.807_3606_4910_8887, -14.769_7000_5035_4004, -12.233_5796_3562_0117, -13.428_1454_0863_0371, -22.624_1779_3273_9258],
							    [-7.511_1498_8327_0264, -8.281_8517_6849_3652, -7.943_1271_5530_3955, -7.55_0213_3369_4458, -6.49_8699_6650_6958,
							    -7.586_7962_8372_1924, -6.978_0859_9472_0459, -7.839_1451_8356_3232, -8.21_9641_6854_8584, -8.695_0912_4755_8594,
							    -6.706_9106_1019_8975, -6.6585_7982_6354_9805, -19.565_6986_2365_7227, -5.353_8427_3529_0527, -8.350_6860_7330_3223,
							    -8.039_3886_5661_6211, -10.856_5692_9016_1133, -7.75_1544_4755_5542, -8.819_0221_7864_9902, -19.51_5327_4536_1328],
							    [-9.73_0662_3458_8623, -10.223_9227_2949_2188, -9.932_9814_9108_8867, -11.857_8367_2332_7637, -7.662_6262_6647_9492,
							    -11.13_5291_0995_4834, -7.765_0976_1810_3027, -11.472_9232_7880_8594, -9.543_1499_4812_0117, -11.905_6339_2639_1602,
							    -9.366_1642_0745_8496, -11.5_7342_8153_9917, -23.699_0032_1960_4492, -9.429_5902_2521_9727, -10.42_8392_4102_7832,
							    -10.585_2403_6407_4707, -10.94_7719_5739_7461, -11.095_4160_6903_0762, -10.390_2406_6925_0488, -23.769_3729_4006_3477],
							    [-9.728_2657_6232_9102, -9.859_7126_0070_8008, -10.09_7292_9000_8545, -9.678_5221_0998_5352, -6.879_5199_3942_2607,
							    -9.68_4875_4882_8125, -4.2803_4257_8887_9395, -10.018_9142_2271_7285, -9.308_4459_3048_0957, -10.63_3945_4650_8789,
							    -8.083_6467_7429_1992, -9.06_3014_9841_3086, -21.904_2663_5742_1875, -8.90_1608_4671_0205, -8.841_8760_2996_8262,
							    -11.856_7199_7070_3125, -12.079_3981_5521_2402, -11.233_7532_0434_5703, -10.177_3386_0015_8691, -21.87_2566_2231_4453],
							    [-9.669_7645_1873_7793, -9.614_1986_8469_2383, -9.814_5103_4545_8984, -9.996_5019_2260_7422, -11.375_6904_6020_5078,
							    -10.113_4052_2766_1133, -10.546_8673_7060_5469, -10.04_3690_6814_5752, -10.907_8092_5750_7324, -10.504_2161_9415_2832,
							    -11.129_1990_2801_5137, -10.151_1240_0054_9316, -21.96_5867_9962_1582, -9.086_3494_8730_4688, -11.730_3390_5029_2969,
							    -10.460_6676_1016_8457, -10.298_0499_2675_7812, -10.784_1482_1624_7559, -10.840_6934_7381_5918, -22.03_1528_4729_0039],
							    # fmt: on
							]
							__lowercase			      =							np.array(lowerCAmelCase__						).flatten()
							__lowercase			      =							output_logits[0, :, :20].flatten()

							def check(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=5E-4						):
											return abs(a - b						) < epsilon * max(abs(lowerCAmelCase__						)				,       abs(lowerCAmelCase__						)						)

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





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							model.to(lowerCAmelCase__						)

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

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

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

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

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

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

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

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

							__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__				,       skip_special_tokens=lowerCAmelCase__						)
							__lowercase			      =							tokenizer.decode(output_non_padded[0]				,       skip_special_tokens=lowerCAmelCase__						)
							__lowercase			      =							tokenizer.decode(output_padded[0]				,       skip_special_tokens=lowerCAmelCase__						)

							__lowercase			      =							[
							    '''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(lowerCAmelCase__				,       lowerCAmelCase__						)
							self.assertListEqual(lowerCAmelCase__				,       [non_padded_sentence, padded_sentence]						)





			@tooslow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							# Output of original model created with mesh-tensoflow
							__lowercase			      =							[
							    ('''武田信玄は''', 3_56_75),
							    ('''武田信玄は、''', 45),
							    ('''武田信玄は、この''', 29),
							    ('''武田信玄は、このよう''', 3_06_42),
							    ('''武田信玄は、このような''', 3_56_80),
							    ('''武田信玄は、このような「''', 86_40),
							    ('''武田信玄は、このような「武田''', 3_16_17),
							    ('''武田信玄は、このような「武田家''', 3_06_46),
							    ('''武田信玄は、このような「武田家の''', 3_16_17),
							    ('''武田信玄は、このような「武田家の家''', 3_13_81),
							]
							for input, output in target:
											__lowercase			      =							tokenizer.encode(lowerCAmelCase__				,       return_tensors='''pt'''						)
											__lowercase			      =							model(lowerCAmelCase__						)
											__lowercase			      =							outputs.logits.detach().cpu().numpy()[0]
											__lowercase			      =							np.argmax(output_logits[-1]						)
											self.assertEqual(lowerCAmelCase__				,       lowerCAmelCase__						)





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							model.to(lowerCAmelCase__						)

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

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

							# spout from uniform and one-hot
							__lowercase			      =							[
							    # fmt: off
							    [0.8788_2208, 0.3842_6396, 0.3322_0248, 0.4389_0406, 0.1656_2252,
							    0.0480_3985, 0.21_1572  , 0.2318_8473, 0.3715_3068, 0.783_6377 ,
							    0.0216_0172, 0.3876_1719, 0.7529_0772, 0.9019_8857, 0.3436_5777,
							    0.6416_8169, 0.4431_8471, 0.1457_5746, 0.9256_2881, 0.4081_2148,
							    0.2901_9122, 0.8886_1599, 0.6552_4846, 0.4356_3456, 0.3817_7187,
							    0.7083_2965, 0.8152_7892, 0.6883_2812, 0.3883_3192, 0.456_1522 ,
							    0.1482_8817, 0.4724_8213, 0.5435_7335, 0.8200_9566, 0.133_8884 ,
							    0.0275_5417, 0.1976_4677, 0.242_2084 , 0.0475_7674, 0.6540_9606,
							    0.082_4589 , 0.0330_4383, 0.9438_7689, 0.9876_4509, 0.8243_3901,
							    0.2764_6741, 0.6490_7493, 0.7600_9406, 0.3008_7915, 0.1790_4689,
							    0.4160_1714, 0.6704_6398, 0.1042_2822, 0.0844_7374, 0.0735_4344,
							    0.6142_3565, 0.7028_4866, 0.753_2333 , 0.197_2038 , 0.2957_5659,
							    0.9058_3886, 0.2926_5307, 0.5000_0175, 0.7040_7655, 0.88_9363  ,
							    0.8190_4418, 0.6682_9128, 0.6446_8815, 0.5656_3723, 0.8560_1875,
							    0.9492_4672, 0.0016_6762, 0.2522_0643, 0.7454_0219, 0.6799_3247,
							    0.154_9675 , 0.3938_5352, 0.9215_3607, 0.6374_5931, 0.2775_9043,
							    0.8470_2295, 0.6590_4271, 0.5867_6614, 0.866_6936 , 0.3960_7438,
							    0.7995_4983, 0.4222_0697, 0.3965_0381, 0.784_9864 , 0.5615_0201,
							    0.1567_8925, 0.1474_6032, 0.3454_2114, 0.4702_6783, 0.1195_6489,
							    0.2542_1435, 0.3378_8901, 0.6893_4842, 0.3642_4685, 0.7173_7898,
							    0.3898_3449, 0.9439_3779, 0.3957_5588, 0.3661_6553, 0.8710_4665,
							    0.6463_0203, 0.2251_6905, 0.8827_0804, 0.1503_1338, 0.7514_4345,
							    0.4645_9025, 0.8539_6454, 0.8635_5643, 0.6513_9851, 0.7026_6061,
							    0.3024_1389, 0.8105_6497, 0.8886_5969, 0.3877_3807, 0.7063_5849,
							    0.9071_8459, 0.4324_5789, 0.2800_0654, 0.4593_5562, 0.0877_3519,
							    0.955_2151 , 0.9390_1511, 0.2248_9288], # uniform
							    [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
							     0., 0., 0., 0., 0., 0., 0., 0.],
							    # fmt: on
							]

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

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

							__lowercase			      =							model.generate(
							    input_ids=lowerCAmelCase__				,       spout=lowerCAmelCase__				,       max_new_tokens=20				,       generation_config=lowerCAmelCase__				,       )

							__lowercase			      =							tokenizer.decode(outputa[0]						)
							__lowercase			      =							tokenizer.decode(outputa[0]						)
							__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__						)

							__lowercase			      =							[
							    '''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(lowerCAmelCase__				,       lowerCAmelCase__						)
							self.assertListEqual(lowerCAmelCase__				,       [outa_sentence, outa_sentence]						)





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							model.to(lowerCAmelCase__						)

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

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

							__lowercase			      =							model.generate(
							    input_ids=input_tok_a.input_ids.to(lowerCAmelCase__						)				,       token_type_ids=input_tok_a.token_type_ids.to(lowerCAmelCase__						)				,       max_new_tokens=20				,       generation_config=lowerCAmelCase__				,       )

							__lowercase			      =							model.generate(
							    input_ids=input_tok_a.input_ids.to(lowerCAmelCase__						)				,       token_type_ids=input_tok_a.token_type_ids.to(lowerCAmelCase__						)				,       max_new_tokens=20				,       generation_config=lowerCAmelCase__				,       )

							__lowercase			      =							model.generate(
							    input_ids=input_tok_a.input_ids.to(lowerCAmelCase__						)				,       token_type_ids=input_tok_a.token_type_ids.to(lowerCAmelCase__						)				,       attention_mask=input_tok_a.attention_mask.to(lowerCAmelCase__						)				,       max_new_tokens=20				,       generation_config=lowerCAmelCase__				,       )

							__lowercase			      =							tokenizer.decode(outputa[0]						)
							__lowercase			      =							tokenizer.decode(outputa[0]						)
							__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__						)

							__lowercase			      =							[
							    '''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(lowerCAmelCase__				,       lowerCAmelCase__						)
							self.assertListEqual(lowerCAmelCase__				,       [outa_sentence, outa_sentence]						)
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser





def     UpperCAmelCase						(   lowercase=None							,      lowercase=None							):







				"""simple docstring"""



				return field(default_factory=lambda: default							,      metadata=lowercase							)




@dataclass
class        _UpperCamelCase				:


			"""simple docstring"""






			__a  :			str      			=						field(
			    metadata={'''help''': '''The csv file to plot.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Disable logarithmic scale when plotting'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={
			        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
			    }						,)
			__a  :			Optional[str]      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}						,)
			__a  :			Optional[List[str]]      			=						list_field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}			)





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				try:
								int(lowercase							)
								return True
				except ValueError:
								return False





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				try:
								float(lowercase							)
								return True
				except ValueError:
								return False




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							args
							__lowercase			      =							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

							with open(self.args.csv_file				,       newline=''''''						) as csv_file:
											__lowercase			      =							csv.DictReader(lowerCAmelCase__						)
											for row in reader:
															__lowercase			      =							row['''model''']
															self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']						)						)
															self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']						)						)
															if can_convert_to_int(row['''result''']						):
																			# value is not None
																			__lowercase			      =							int(row['''result''']						)
															elif can_convert_to_float(row['''result''']						):
																			# value is not None
																			__lowercase			      =							float(row['''result''']						)





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

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							plt.subplots()
							__lowercase			      =							'''Time usage''' if self.args.is_time else '''Memory usage'''
							__lowercase			      =							title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

							if not self.args.no_log_scale:
											# set logarithm scales
											ax.set_xscale('''log'''						)
											ax.set_yscale('''log'''						)

							for axis in [ax.xaxis, ax.yaxis]:
											axis.set_major_formatter(ScalarFormatter()						)

							for model_name_idx, model_name in enumerate(self.result_dict.keys()						):
											__lowercase			      =							sorted(set(self.result_dict[model_name]['''bsz''']						)						)
											__lowercase			      =							sorted(set(self.result_dict[model_name]['''seq_len''']						)						)
											__lowercase			      =							self.result_dict[model_name]['''result''']

											((__lowercase)      ,					(__lowercase))			      =							(
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											__lowercase			      =							(
											    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
											)

											for inner_loop_value in inner_loop_array:
															if self.args.plot_along_batch:
																			__lowercase			      =							np.asarray(
																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,       dtype=lowerCAmelCase__				,       )
															else:
																			__lowercase			      =							np.asarray(
																			    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				,       dtype=np.floataa				,       )

															((__lowercase)      ,					(__lowercase))			      =							(
															    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
															)

															__lowercase			      =							np.asarray(lowerCAmelCase__				,       lowerCAmelCase__						)[: len(lowerCAmelCase__						)]
															plt.scatter(
															    lowerCAmelCase__				,       lowerCAmelCase__				,       label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"						)
															plt.plot(lowerCAmelCase__				,       lowerCAmelCase__				,       '''--'''						)

											title_str += F" {label_model_name} vs."

							__lowercase			      =							title_str[:-4]
							__lowercase			      =							'''Time in s''' if self.args.is_time else '''Memory in MB'''

							# plot
							plt.title(lowerCAmelCase__						)
							plt.xlabel(lowerCAmelCase__						)
							plt.ylabel(lowerCAmelCase__						)
							plt.legend()

							if self.args.figure_png_file is not None:
											plt.savefig(self.args.figure_png_file						)
							else:
											plt.show()





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							HfArgumentParser(lowercase							)
				__lowercase			      =							parser.parse_args_into_dataclasses()[0]
				__lowercase			      =							Plot(args=lowercase							)
				plot.plot()


if __name__ == "__main__":
		main()