





"""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  _UpperCAmelCase       :


					def __init__( self      :str					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Dict=3_60_00					,	__UpperCamelCase      :Any=13					,	__UpperCamelCase      :List[str]=7					,	__UpperCamelCase      :List[Any]=True					,	__UpperCamelCase      :int=32					,	__UpperCamelCase      :Dict=42					,	__UpperCamelCase      :int=5					,	__UpperCamelCase      :Dict=2					,	__UpperCamelCase      :Optional[int]=4					,	__UpperCamelCase      :Any=2					,	__UpperCamelCase      :Union[str, Any]=32					,	__UpperCamelCase      :Dict=80					,	__UpperCamelCase      :Optional[int]=33					,	__UpperCamelCase      :Tuple=0.0					,	__UpperCamelCase      :List[str]=1e-6					,	__UpperCamelCase      :str=1_00					,	__UpperCamelCase      :str=0.0					,	):
												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      lowerCamelCase	( self      :Union[str, Any]		):
												return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"		)


					def      lowerCamelCase	( self      :List[Any]		):
												A		=  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)

												A		=  self.get_config()

												return (config, input_ids)


					def      lowerCamelCase	( self      :Dict		):
												A		=  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)

												A		=  self.get_config()

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


					def      lowerCamelCase	( self      :Union[str, Any]		):
												return GPTSanJapaneseConfig(
												    vocab_size=self.vocab_size					,	num_contexts=self.seq_length					,	d_model=self.hidden_size					,	d_ff=self.d_ff					,	d_ext=self.d_ext					,	d_spout=self.d_spout					,	num_switch_layers=self.num_hidden_layers - self.num_ext_layers					,	num_ext_layers=self.num_ext_layers					,	num_heads=self.num_attention_heads					,	num_experts=self.num_experts					,	expert_capacity=self.expert_capacity					,	dropout_rate=self.dropout_rate					,	layer_norm_epsilon=self.layer_norm_epsilon					,	router_jitter_noise=self.router_jitter_noise					,	)


					def      lowerCamelCase	( self      :int					,	__UpperCamelCase      :List[Any]					,	__UpperCamelCase      :Any					,	):
												A		=  GPTSanJapaneseForConditionalGeneration(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(
												    input_ids=__UpperCamelCase					,	)
												self.parent.assertIsNotNone(__UpperCamelCase		)







@require_torch
class  _UpperCAmelCase       (     lowercase_					,							lowercase_					,							unittest.TestCase			):
					UpperCamelCase								=							(GPTSanJapaneseModel,) if is_torch_available() else ()
					UpperCamelCase								=							(
					    {
					        '''conversational''': GPTSanJapaneseForConditionalGeneration,
					        '''feature-extraction''': GPTSanJapaneseForConditionalGeneration,
					        '''summarization''': GPTSanJapaneseForConditionalGeneration,
					        '''text2text-generation''': GPTSanJapaneseForConditionalGeneration,
					        '''translation''': GPTSanJapaneseForConditionalGeneration,
					    }
					    if is_torch_available()
					    else {}
					)
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
					UpperCamelCase								=							[0.8, 0.9]


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


					def      lowerCamelCase	( self      :int		):
												GPTSanJapaneseConfig()


					def      lowerCamelCase	( self      :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      lowerCamelCase	( self      :List[Any]		):
												super().test_model_parallelism()


					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
					def      lowerCamelCase	( self      :Dict		):
												pass







@require_torch
class  _UpperCAmelCase       (     lowercase_					,							lowercase_					,							unittest.TestCase			):
					UpperCamelCase								=							(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False
					# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
					UpperCamelCase								=							[0.8, 0.9]


					def      lowerCamelCase	( self      :Union[str, Any]		):
												A		=  GPTSanJapaneseTester(self		)
												A		=  ConfigTester(self					,	config_class=__UpperCamelCase					,	d_model=37		)


					def      lowerCamelCase	( self      :List[str]		):
												GPTSanJapaneseConfig()


					def      lowerCamelCase	( self      :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      lowerCamelCase	( self      :int		):
												super().test_model_parallelism()


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


					@slow
					def      lowerCamelCase	( self      :Union[str, Any]		):
												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
												    [-12.037_839_889_526_367, -12.433_061_599_731_445, -14.333_840_370_178_223, -12.450_345_993_041_992, -11.1_661_376_953_125,
												    -11.930_137_634_277_344, -10.659_740_447_998_047, -12.909_574_508_666_992, -13.241_043_090_820_312, -13.398_579_597_473_145,
												    -11.107_524_871_826_172, -12.3_685_941_696_167, -22.97_943_115_234_375, -10.481_067_657_470_703, -12.484_030_723_571_777,
												    -12.807_360_649_108_887, -14.769_700_050_354_004, -12.233_579_635_620_117, -13.428_145_408_630_371, -22.624_177_932_739_258],
												    [-7.511_149_883_270_264, -8.281_851_768_493_652, -7.943_127_155_303_955, -7.55_021_333_694_458, -6.49_869_966_506_958,
												    -7.586_796_283_721_924, -6.978_085_994_720_459, -7.839_145_183_563_232, -8.21_964_168_548_584, -8.695_091_247_558_594,
												    -6.706_910_610_198_975, -6.6_585_798_263_549_805, -19.565_698_623_657_227, -5.353_842_735_290_527, -8.350_686_073_303_223,
												    -8.039_388_656_616_211, -10.856_569_290_161_133, -7.75_154_447_555_542, -8.819_022_178_649_902, -19.51_532_745_361_328],
												    [-9.73_066_234_588_623, -10.223_922_729_492_188, -9.932_981_491_088_867, -11.857_836_723_327_637, -7.662_626_266_479_492,
												    -11.13_529_109_954_834, -7.765_097_618_103_027, -11.472_923_278_808_594, -9.543_149_948_120_117, -11.905_633_926_391_602,
												    -9.366_164_207_458_496, -11.5_734_281_539_917, -23.699_003_219_604_492, -9.429_590_225_219_727, -10.42_839_241_027_832,
												    -10.585_240_364_074_707, -10.94_771_957_397_461, -11.095_416_069_030_762, -10.390_240_669_250_488, -23.769_372_940_063_477],
												    [-9.728_265_762_329_102, -9.859_712_600_708_008, -10.09_729_290_008_545, -9.678_522_109_985_352, -6.879_519_939_422_607,
												    -9.68_487_548_828_125, -4.2_803_425_788_879_395, -10.018_914_222_717_285, -9.308_445_930_480_957, -10.63_394_546_508_789,
												    -8.083_646_774_291_992, -9.06_301_498_413_086, -21.904_266_357_421_875, -8.90_160_846_710_205, -8.841_876_029_968_262,
												    -11.856_719_970_703_125, -12.079_398_155_212_402, -11.233_753_204_345_703, -10.177_338_600_158_691, -21.87_256_622_314_453],
												    [-9.669_764_518_737_793, -9.614_198_684_692_383, -9.814_510_345_458_984, -9.996_501_922_607_422, -11.375_690_460_205_078,
												    -10.113_405_227_661_133, -10.546_867_370_605_469, -10.04_369_068_145_752, -10.907_809_257_507_324, -10.504_216_194_152_832,
												    -11.129_199_028_015_137, -10.151_124_000_549_316, -21.96_586_799_621_582, -9.086_349_487_304_688, -11.730_339_050_292_969,
												    -10.460_667_610_168_457, -10.298_049_926_757_812, -10.784_148_216_247_559, -10.840_693_473_815_918, -22.03_152_847_290_039],
												    # fmt: on
												]
												A		=  np.array(__UpperCamelCase		).flatten()
												A		=  output_logits[0, :, :20].flatten()

												def check(__UpperCamelCase      :str					,	__UpperCamelCase      :Union[str, Any]					,	__UpperCamelCase      :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      lowerCamelCase	( self      :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

												# 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      lowerCamelCase	( self      :Union[str, Any]		):
												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_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:
																			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      lowerCamelCase	( self      :Dict		):
												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_882_208, 0.38_426_396, 0.33_220_248, 0.43_890_406, 0.16_562_252,
												    0.04_803_985, 0.211_572  , 0.23_188_473, 0.37_153_068, 0.7_836_377 ,
												    0.02_160_172, 0.38_761_719, 0.75_290_772, 0.90_198_857, 0.34_365_777,
												    0.64_168_169, 0.44_318_471, 0.14_575_746, 0.92_562_881, 0.40_812_148,
												    0.29_019_122, 0.88_861_599, 0.65_524_846, 0.43_563_456, 0.38_177_187,
												    0.70_832_965, 0.81_527_892, 0.68_832_812, 0.38_833_192, 0.4_561_522 ,
												    0.14_828_817, 0.47_248_213, 0.54_357_335, 0.82_009_566, 0.1_338_884 ,
												    0.02_755_417, 0.19_764_677, 0.2_422_084 , 0.04_757_674, 0.65_409_606,
												    0.0_824_589 , 0.03_304_383, 0.94_387_689, 0.98_764_509, 0.82_433_901,
												    0.27_646_741, 0.64_907_493, 0.76_009_406, 0.30_087_915, 0.17_904_689,
												    0.41_601_714, 0.67_046_398, 0.10_422_822, 0.08_447_374, 0.07_354_344,
												    0.61_423_565, 0.70_284_866, 0.7_532_333 , 0.1_972_038 , 0.29_575_659,
												    0.90_583_886, 0.29_265_307, 0.50_000_175, 0.70_407_655, 0.889_363  ,
												    0.81_904_418, 0.66_829_128, 0.64_468_815, 0.56_563_723, 0.85_601_875,
												    0.94_924_672, 0.00_166_762, 0.25_220_643, 0.74_540_219, 0.67_993_247,
												    0.1_549_675 , 0.39_385_352, 0.92_153_607, 0.63_745_931, 0.27_759_043,
												    0.84_702_295, 0.65_904_271, 0.58_676_614, 0.8_666_936 , 0.39_607_438,
												    0.79_954_983, 0.42_220_697, 0.39_650_381, 0.7_849_864 , 0.56_150_201,
												    0.15_678_925, 0.14_746_032, 0.34_542_114, 0.47_026_783, 0.11_956_489,
												    0.25_421_435, 0.33_788_901, 0.68_934_842, 0.36_424_685, 0.71_737_898,
												    0.38_983_449, 0.94_393_779, 0.39_575_588, 0.36_616_553, 0.87_104_665,
												    0.64_630_203, 0.22_516_905, 0.88_270_804, 0.15_031_338, 0.75_144_345,
												    0.46_459_025, 0.85_396_454, 0.86_355_643, 0.65_139_851, 0.70_266_061,
												    0.30_241_389, 0.81_056_497, 0.88_865_969, 0.38_773_807, 0.70_635_849,
												    0.90_718_459, 0.43_245_789, 0.28_000_654, 0.45_935_562, 0.08_773_519,
												    0.9_552_151 , 0.93_901_511, 0.22_489_288], # 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      lowerCamelCase	( self      :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 unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		_snake_case					:		Dict							=			'platform'

		import jax
		import jax.numpy as jnp

		from transformers import MarianTokenizer
		from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase=None ,  UpperCamelCase=None ,  UpperCamelCase=None ,  UpperCamelCase=None ,  UpperCamelCase=None ,  UpperCamelCase=None ,  ):
							if attention_mask is None:
														A		=  np.where(input_ids != config.pad_token_id ,  1 ,  0	)
							if decoder_attention_mask is None:
														A		=  np.where(decoder_input_ids != config.pad_token_id ,  1 ,  0	)
							if head_mask is None:
														A		=  np.ones((config.encoder_layers, config.encoder_attention_heads)	)
							if decoder_head_mask is None:
														A		=  np.ones((config.decoder_layers, config.decoder_attention_heads)	)
							if cross_attn_head_mask is None:
														A		=  np.ones((config.decoder_layers, config.decoder_attention_heads)	)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							}






class  _UpperCAmelCase       :


					def __init__( self      :Union[str, Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[str]=13					,	__UpperCamelCase      :Dict=7					,	__UpperCamelCase      :Union[str, Any]=True					,	__UpperCamelCase      :int=False					,	__UpperCamelCase      :int=99					,	__UpperCamelCase      :Tuple=16					,	__UpperCamelCase      :Optional[Any]=2					,	__UpperCamelCase      :Any=4					,	__UpperCamelCase      :Union[str, Any]=4					,	__UpperCamelCase      :Any="gelu"					,	__UpperCamelCase      :Union[str, Any]=0.1					,	__UpperCamelCase      :List[Any]=0.1					,	__UpperCamelCase      :Optional[Any]=32					,	__UpperCamelCase      :Tuple=2					,	__UpperCamelCase      :int=1					,	__UpperCamelCase      :Optional[Any]=0					,	__UpperCamelCase      :Union[str, Any]=0.02					,	):
												A		=  parent
												A		=  batch_size
												A		=  seq_length
												A		=  is_training
												A		=  use_labels
												A		=  vocab_size
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  max_position_embeddings
												A		=  eos_token_id
												A		=  pad_token_id
												A		=  bos_token_id
												A		=  initializer_range


					def      lowerCamelCase	( self      :Tuple		):
												A		=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,	self.vocab_size		)					,	3					,	self.vocab_size		)
												A		=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,	dtype=np.intaa		))					,	-1		)

												A		=  shift_tokens_right(__UpperCamelCase					,	1					,	2		)

												A		=  MarianConfig(
												    vocab_size=self.vocab_size					,	d_model=self.hidden_size					,	encoder_layers=self.num_hidden_layers					,	decoder_layers=self.num_hidden_layers					,	encoder_attention_heads=self.num_attention_heads					,	decoder_attention_heads=self.num_attention_heads					,	encoder_ffn_dim=self.intermediate_size					,	decoder_ffn_dim=self.intermediate_size					,	dropout=self.hidden_dropout_prob					,	attention_dropout=self.attention_probs_dropout_prob					,	max_position_embeddings=self.max_position_embeddings					,	eos_token_id=self.eos_token_id					,	bos_token_id=self.bos_token_id					,	pad_token_id=self.pad_token_id					,	initializer_range=self.initializer_range					,	use_cache=__UpperCamelCase					,	)
												A		=  prepare_marian_inputs_dict(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)
												return config, inputs_dict


					def      lowerCamelCase	( self      :Optional[Any]		):
												A,     A		=  self.prepare_config_and_inputs()
												return config, inputs_dict


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :str					,	__UpperCamelCase      :List[str]		):
												A		=  20
												A		=  model_class_name(__UpperCamelCase		)

												A		=  model.encode(inputs_dict["input_ids"]		)

												A,     A		=  (
												    inputs_dict["decoder_input_ids"],
												    inputs_dict["decoder_attention_mask"],
												)

												A		=  model.init_cache(decoder_input_ids.shape[0]					,	__UpperCamelCase					,	__UpperCamelCase		)
												A		=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,	dtype="i4"		)

												A		=  jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]					,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,	)
												A		=  model.decode(
												    decoder_input_ids[:, :-1]					,	__UpperCamelCase					,	decoder_attention_mask=__UpperCamelCase					,	past_key_values=__UpperCamelCase					,	decoder_position_ids=__UpperCamelCase					,	)

												A		=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,	dtype="i4"		)
												A		=  model.decode(
												    decoder_input_ids[:, -1:]					,	__UpperCamelCase					,	decoder_attention_mask=__UpperCamelCase					,	past_key_values=outputs_cache.past_key_values					,	decoder_position_ids=__UpperCamelCase					,	)

												A		=  model.decode(__UpperCamelCase					,	__UpperCamelCase		)

												A		=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
												self.parent.assertTrue(diff < 1e-3					,	msg=f"Max diff is {diff}"		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Dict					,	__UpperCamelCase      :Tuple		):
												A		=  20
												A		=  model_class_name(__UpperCamelCase		)

												A		=  model.encode(inputs_dict["input_ids"]		)

												A,     A		=  (
												    inputs_dict["decoder_input_ids"],
												    inputs_dict["decoder_attention_mask"],
												)

												A		=  jnp.concatenate(
												    [
												        decoder_attention_mask,
												        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
												    ]					,	axis=-1					,	)

												A		=  model.init_cache(decoder_input_ids.shape[0]					,	__UpperCamelCase					,	__UpperCamelCase		)
												A		=  jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]					,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,	)

												A		=  model.decode(
												    decoder_input_ids[:, :-1]					,	__UpperCamelCase					,	decoder_attention_mask=__UpperCamelCase					,	past_key_values=__UpperCamelCase					,	decoder_position_ids=__UpperCamelCase					,	)
												A		=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,	dtype="i4"		)
												A		=  model.decode(
												    decoder_input_ids[:, -1:]					,	__UpperCamelCase					,	past_key_values=outputs_cache.past_key_values					,	decoder_attention_mask=__UpperCamelCase					,	decoder_position_ids=__UpperCamelCase					,	)

												A		=  model.decode(__UpperCamelCase					,	__UpperCamelCase					,	decoder_attention_mask=__UpperCamelCase		)

												A		=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
												self.parent.assertTrue(diff < 1e-3					,	msg=f"Max diff is {diff}"		)







@require_flax
class  _UpperCAmelCase       (     lowercase_					,							unittest.TestCase					,							lowercase_			):
					UpperCamelCase								=							True
					UpperCamelCase								=							(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
					UpperCamelCase								=							(FlaxMarianMTModel,) if is_flax_available() else ()


					def      lowerCamelCase	( self      :Dict		):
												A		=  FlaxMarianModelTester(self		)


					def      lowerCamelCase	( self      :str		):
												A,     A		=  self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :Dict		):
												A,     A		=  self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :int		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__		):
																										A		=  self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase		)
																										A		=  model_class(__UpperCamelCase		)

																										@jax.jit
																										def encode_jitted(__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :Dict=None					,	**__UpperCamelCase      :List[Any]		):
																																	return model.encode(input_ids=__UpperCamelCase					,	attention_mask=__UpperCamelCase		)

																										with self.subTest("JIT Enabled"		):
																																	A		=  encode_jitted(**__UpperCamelCase		).to_tuple()

																										with self.subTest("JIT Disabled"		):
																																	with jax.disable_jit():
																																								A		=  encode_jitted(**__UpperCamelCase		).to_tuple()

																										self.assertEqual(len(__UpperCamelCase		)					,	len(__UpperCamelCase		)		)
																										for jitted_output, output in zip(__UpperCamelCase					,	__UpperCamelCase		):
																																	self.assertEqual(jitted_output.shape					,	output.shape		)


					def      lowerCamelCase	( self      :Optional[int]		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__		):
																										A		=  model_class(__UpperCamelCase		)
																										A		=  model.encode(inputs_dict["input_ids"]					,	inputs_dict["attention_mask"]		)

																										A		=  {
																										    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																										    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																										    "encoder_outputs": encoder_outputs,
																										}

																										@jax.jit
																										def decode_jitted(__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :List[str]		):
																																	return model.decode(
																																	    decoder_input_ids=__UpperCamelCase					,	decoder_attention_mask=__UpperCamelCase					,	encoder_outputs=__UpperCamelCase					,	)

																										with self.subTest("JIT Enabled"		):
																																	A		=  decode_jitted(**__UpperCamelCase		).to_tuple()

																										with self.subTest("JIT Disabled"		):
																																	with jax.disable_jit():
																																								A		=  decode_jitted(**__UpperCamelCase		).to_tuple()

																										self.assertEqual(len(__UpperCamelCase		)					,	len(__UpperCamelCase		)		)
																										for jitted_output, output in zip(__UpperCamelCase					,	__UpperCamelCase		):
																																	self.assertEqual(jitted_output.shape					,	output.shape		)


					@slow
					def      lowerCamelCase	( self      :str		):
												for model_class_name in self.all_model_classes:
																			A		=  model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"		)
																			# FlaxMarianForSequenceClassification expects eos token in input_ids
																			A		=  np.ones((1, 1)		) * model.config.eos_token_id
																			A		=  model(__UpperCamelCase		)
																			self.assertIsNotNone(__UpperCamelCase		)







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     unittest.TestCase			):
					UpperCamelCase								=							None
					UpperCamelCase								=							None


					@classmethod
					def      lowerCamelCase	( cls      :Tuple		):
												A		=  f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
												return cls


					@cached_property
					def      lowerCamelCase	( self      :Optional[int]		):
												return MarianTokenizer.from_pretrained(self.model_name		)


					@property
					def      lowerCamelCase	( self      :int		):
												return self.tokenizer.eos_token_id


					@cached_property
					def      lowerCamelCase	( self      :List[Any]		):
												A		=  FlaxMarianMTModel.from_pretrained(self.model_name		)
												self.assertEqual(model.config.decoder_start_token_id					,	model.config.pad_token_id		)
												return model


					def      lowerCamelCase	( self      :int					,	**__UpperCamelCase      :List[str]		):
												A		=  self.translate_src_text(**__UpperCamelCase		)
												self.assertListEqual(self.expected_text					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :List[Any]					,	**__UpperCamelCase      :List[Any]		):
												A		=  self.tokenizer(self.src_text					,	padding=__UpperCamelCase					,	return_tensors="np"					,	**__UpperCamelCase		)
												A		=  self.model.generate(
												    model_inputs.input_ids					,	attention_mask=model_inputs.attention_mask					,	num_beams=2					,	max_length=1_28					,	).sequences
												A		=  self.tokenizer.batch_decode(__UpperCamelCase					,	skip_special_tokens=__UpperCamelCase		)
												return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							'''fr'''
					UpperCamelCase								=							[
					    '''I am a small frog.''',
					    '''Now I can forget the 100 words of german that I know.''',
					]
					UpperCamelCase								=							[
					    '''Je suis une petite grenouille.''',
					    '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
					]


					@slow
					def      lowerCamelCase	( self      :str		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''fr'''
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							[
					    '''Donnez moi le micro.''',
					    '''Tom et Mary étaient assis à une table.''',  # Accents
					]
					UpperCamelCase								=							[
					    '''Give me the microphone.''',
					    '''Tom and Mary were sitting at a table.''',
					]


					@slow
					def      lowerCamelCase	( self      :Optional[Any]		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''mt'''
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
					UpperCamelCase								=							['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']


					@slow
					def      lowerCamelCase	( self      :Optional[int]		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							'''de'''
					UpperCamelCase								=							[
					    '''I am a small frog.''',
					    '''Now I can forget the 100 words of german that I know.''',
					    '''Tom asked his teacher for advice.''',
					    '''That\'s how I would do it.''',
					    '''Tom really admired Mary\'s courage.''',
					    '''Turn around and close your eyes.''',
					]
					UpperCamelCase								=							[
					    '''Ich bin ein kleiner Frosch.''',
					    '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
					    '''Tom bat seinen Lehrer um Rat.''',
					    '''So würde ich das machen.''',
					    '''Tom bewunderte Marias Mut wirklich.''',
					    '''Drehen Sie sich um und schließen Sie die Augen.''',
					]


					@slow
					def      lowerCamelCase	( self      :Any		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							'''zh'''
					UpperCamelCase								=							['''My name is Wolfgang and I live in Berlin''']
					UpperCamelCase								=							['''我叫沃尔夫冈 我住在柏林''']


					@slow
					def      lowerCamelCase	( self      :List[str]		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''ru'''
					UpperCamelCase								=							'''fr'''
					UpperCamelCase								=							['''Он показал мне рукопись своей новой пьесы.''']
					UpperCamelCase								=							['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']


					@slow
					def      lowerCamelCase	( self      :Any		):
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''en'''
					UpperCamelCase								=							'''ROMANCE'''
					UpperCamelCase								=							[
					    '''>>fr<< Don\'t spend so much time watching TV.''',
					    '''>>pt<< Your message has been sent.''',
					    '''>>es<< He\'s two years older than me.''',
					]
					UpperCamelCase								=							[
					    '''Ne passez pas autant de temps à regarder la télé.''',
					    '''A sua mensagem foi enviada.''',
					    '''Es dos años más viejo que yo.''',
					]


					@slow
					def      lowerCamelCase	( self      :Optional[int]		):
												self._assert_generated_batch_equal_expected()


