
import unittest

import numpy as np

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

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







class 	A_       :




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case=3_6000		,   snake_case=13		,   snake_case=7		,   snake_case=True		,   snake_case=32		,   snake_case=42		,   snake_case=5		,   snake_case=2		,   snake_case=4		,   snake_case=2		,   snake_case=32		,   snake_case=80		,   snake_case=33		,   snake_case=0.0		,   snake_case=1E-6		,   snake_case=100		,   snake_case=0.0		,   ):
		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	):
		return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese'	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		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	):
		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	):
		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		,   snake_case		,   snake_case		,   ):
		lowercase					  =   GPTSanJapaneseForConditionalGeneration(config=snake_case	)
		model.to(snake_case	)
		model.eval()
		lowercase					  =   model(
		    input_ids=snake_case		,   )
		self.parent.assertIsNotNone(snake_case	)







@require_torch
class 	A_       (      __lowerCamelCase				,     __lowerCamelCase				,     unittest.TestCase       ):




	'''simple docstring'''


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





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
		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	):
		lowercase					  =   GPTSanJapaneseTester(self	)
		lowercase					  =   ConfigTester(self		,   config_class=snake_case		,   d_model=37	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		GPTSanJapaneseConfig()





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case	)





	@unittest.skip(
	    reason='skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'	)
	def      SCREAMING_SNAKE_CASE__						(	self	):
		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	):
		pass







@require_torch
class 	A_       (      __lowerCamelCase				,     __lowerCamelCase				,     unittest.TestCase       ):




	'''simple docstring'''


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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   GPTSanJapaneseTester(self	)
		lowercase					  =   ConfigTester(self		,   config_class=snake_case		,   d_model=37	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		GPTSanJapaneseConfig()





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case	)





	@unittest.skip(
	    reason='skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'	)
	def      SCREAMING_SNAKE_CASE__						(	self	):
		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	):
		pass





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		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(snake_case	)
		lowercase					  =   outputs.logits.detach().cpu().numpy()
		# Output of original model created with mesh-tensoflow
		lowercase					  =   [
		    # 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
		]
		lowercase					  =   np.array(snake_case	).flatten()
		lowercase					  =   output_logits[0, :, :20].flatten()

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

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





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'	)
		lowercase					  =   GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'	)
		model.to(snake_case	)

		# 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(snake_case		,   return_tensors='pt'		,   padding=snake_case	)
		lowercase					  =   inputs['input_ids'].to(snake_case	)

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

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

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

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

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

		lowercase					  =   tokenizer.batch_decode(snake_case		,   skip_special_tokens=snake_case	)
		lowercase					  =   tokenizer.decode(output_non_padded[0]		,   skip_special_tokens=snake_case	)
		lowercase					  =   tokenizer.decode(output_padded[0]		,   skip_special_tokens=snake_case	)

		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(snake_case		,   snake_case	)
		self.assertListEqual(snake_case		,   [non_padded_sentence, padded_sentence]	)





	@tooslow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		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_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:
			lowercase					  =   tokenizer.encode(snake_case		,   return_tensors='pt'	)
			lowercase					  =   model(snake_case	)
			lowercase					  =   outputs.logits.detach().cpu().numpy()[0]
			lowercase					  =   np.argmax(output_logits[-1]	)
			self.assertEqual(snake_case		,   snake_case	)





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'	)
		lowercase					  =   GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'	)
		model.to(snake_case	)

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

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

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

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

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

		lowercase					  =   model.generate(
		    input_ids=snake_case		,   spout=snake_case		,   max_new_tokens=20		,   generation_config=snake_case		,   )

		lowercase					  =   tokenizer.decode(outputa[0]	)
		lowercase					  =   tokenizer.decode(outputa[0]	)
		lowercase					  =   tokenizer.batch_decode(snake_case	)

		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(snake_case		,   snake_case	)
		self.assertListEqual(snake_case		,   [outa_sentence, outa_sentence]	)





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'	)
		lowercase					  =   GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'	)
		model.to(snake_case	)

		# 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(snake_case		,   prefix_text=snake_case		,   return_tensors='pt'	)
		lowercase					  =   tokenizer(snake_case		,   prefix_text=snake_case		,   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(snake_case	)		,   token_type_ids=input_tok_a.token_type_ids.to(snake_case	)		,   max_new_tokens=20		,   generation_config=snake_case		,   )

		lowercase					  =   model.generate(
		    input_ids=input_tok_a.input_ids.to(snake_case	)		,   token_type_ids=input_tok_a.token_type_ids.to(snake_case	)		,   max_new_tokens=20		,   generation_config=snake_case		,   )

		lowercase					  =   model.generate(
		    input_ids=input_tok_a.input_ids.to(snake_case	)		,   token_type_ids=input_tok_a.token_type_ids.to(snake_case	)		,   attention_mask=input_tok_a.attention_mask.to(snake_case	)		,   max_new_tokens=20		,   generation_config=snake_case		,   )

		lowercase					  =   tokenizer.decode(outputa[0]	)
		lowercase					  =   tokenizer.decode(outputa[0]	)
		lowercase					  =   tokenizer.batch_decode(snake_case	)

		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(snake_case		,   snake_case	)
		self.assertListEqual(snake_case		,   [outa_sentence, outa_sentence]	)



# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


UpperCAmelCase     				=			logging.get_logger(__name__)


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset

	from transformers import Trainer







	class 	A_       (      __lowerCamelCase       ):




		'''simple docstring'''


		def __init__(	self		,   snake_case = 101	):
			lowercase					  =   length





		def __len__(	self	):
			return self.length





		def __getitem__(	self		,   snake_case	):
			return i







	class 	A_       :




		'''simple docstring'''


		def __call__(	self		,   snake_case	):
			return {"input_ids": torch.tensor(snake_case	), "labels": torch.tensor(snake_case	)}







	class 	A_       (      nn.Module       ):




		'''simple docstring'''


		def __init__(	self	):
			super().__init__()
			# Add some (unused) params otherwise DDP will complain.
			lowercase					  =   nn.Linear(120		,   80	)





		def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case=None	):
			if labels is not None:
				return torch.tensor(0.0		,   device=input_ids.device	), input_ids
			else:
				return input_ids






def 							UpperCAmelCase_		(							):
	lowercase					  =   HfArgumentParser((TrainingArguments,)							)
	sys.argv += ["--output_dir", "./examples"]
	lowercase					  =   parser.parse_args_into_dataclasses()[0]

	logger.warning(
	    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, '''
	    F'''tpu_num_cores: {training_args.tpu_num_cores}'''			,							)

	# Essentially, what we want to verify in the distributed case is
	# that we get all samples back, in the right order.
	# (this is crucial for prediction for instance)
	for dataset_length in [1001, 256, 15]:
		lowercase					  =   DummyDataset(__SCREAMING_SNAKE_CASE							)

		def compute_metrics(__SCREAMING_SNAKE_CASE							) -> Dict:
			lowercase					  =   list(range(len(__SCREAMING_SNAKE_CASE							)							)							)
			lowercase					  =   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
			return {"success": success}

		lowercase					  =   Trainer(
		    model=DummyModel()			,							args=__SCREAMING_SNAKE_CASE			,							data_collator=DummyDataCollator()			,							eval_dataset=__SCREAMING_SNAKE_CASE			,							compute_metrics=__SCREAMING_SNAKE_CASE			,							)
		lowercase					  =   trainer.evaluate()
		logger.info(__SCREAMING_SNAKE_CASE							)
		if metrics["eval_success"] is not True:
			logger.error(__SCREAMING_SNAKE_CASE							)
			exit(1							)

		lowercase					  =   trainer.predict(__SCREAMING_SNAKE_CASE							)
		logger.info(p.metrics							)
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics							)
			exit(1							)

		lowercase					  =   2

		lowercase					  =   trainer.evaluate()
		logger.info(__SCREAMING_SNAKE_CASE							)
		if metrics["eval_success"] is not True:
			logger.error(__SCREAMING_SNAKE_CASE							)
			exit(1							)

		lowercase					  =   trainer.predict(__SCREAMING_SNAKE_CASE							)
		logger.info(p.metrics							)
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics							)
			exit(1							)

		lowercase					  =   None

	logger.info('🔥 All distributed tests successful'							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()

