

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_			:


					def __init__(							self      :		int      ,  UpperCAmelCase      :		str      ,  UpperCAmelCase      :		Optional[int]=3_6_0_0_0      ,  UpperCAmelCase      :		int=1_3      ,  UpperCAmelCase      :		Optional[int]=7      ,  UpperCAmelCase      :		List[str]=True      ,  UpperCAmelCase      :		Union[str, Any]=3_2      ,  UpperCAmelCase      :		str=4_2      ,  UpperCAmelCase      :		int=5      ,  UpperCAmelCase      :		Dict=2      ,  UpperCAmelCase      :		List[str]=4      ,  UpperCAmelCase      :		Optional[int]=2      ,  UpperCAmelCase      :		Tuple=3_2      ,  UpperCAmelCase      :		Any=8_0      ,  UpperCAmelCase      :		Optional[Any]=3_3      ,  UpperCAmelCase      :		Dict=0.0      ,  UpperCAmelCase      :		Any=1E-6      ,  UpperCAmelCase      :		Tuple=1_0_0      ,  UpperCAmelCase      :		int=0.0      ,  )							->     Dict:
						__lowerCAmelCase:       Any            =				vocab_size
						__lowerCAmelCase:       List[str]            =				parent
						__lowerCAmelCase:       List[Any]            =				batch_size
						__lowerCAmelCase:       Tuple            =				num_contexts
						# For common tests
						__lowerCAmelCase:       Tuple            =				self.num_contexts
						__lowerCAmelCase:       Any            =				is_training
						__lowerCAmelCase:       int            =				hidden_size
						__lowerCAmelCase:       Tuple            =				num_ext_layers
						__lowerCAmelCase:       Union[str, Any]            =				ext_size
						__lowerCAmelCase:       Optional[Any]            =				num_hidden_layers
						__lowerCAmelCase:       Any            =				num_attention_heads
						__lowerCAmelCase:       str            =				num_experts
						__lowerCAmelCase:       Any            =				d_ff
						__lowerCAmelCase:       List[str]            =				d_ext
						__lowerCAmelCase:       Any            =				d_spout
						__lowerCAmelCase:       Optional[Any]            =				dropout_rate
						__lowerCAmelCase:       Optional[int]            =				layer_norm_epsilon
						__lowerCAmelCase:       Union[str, Any]            =				expert_capacity
						__lowerCAmelCase:       Tuple            =				router_jitter_noise


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


					def   UpperCAmelCase  (							self      :		Dict )							->     int:
						__lowerCAmelCase:       Union[str, Any]            =				ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

						__lowerCAmelCase:       Optional[Any]            =				self.get_config()

						return (config, input_ids)


					def   UpperCAmelCase  (							self      :		str )							->     Optional[Any]:
						__lowerCAmelCase:       Dict            =				ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

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

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


					def   UpperCAmelCase  (							self      :		Any )							->     List[str]:
						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   UpperCAmelCase  (							self      :		Optional[Any]      ,  UpperCAmelCase      :		Optional[Any]      ,  UpperCAmelCase      :		Union[str, Any]      ,  )							->     Any:
						__lowerCAmelCase:       Union[str, Any]            =				GPTSanJapaneseForConditionalGeneration(config=UpperCAmelCase )
						model.to(UpperCAmelCase )
						model.eval()
						__lowerCAmelCase:       int            =				model(
						    input_ids=UpperCAmelCase      ,  )
						self.parent.assertIsNotNone(UpperCAmelCase )







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


					def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		int )							->     Dict:
						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   UpperCAmelCase  (							self      :		Optional[Any] )							->     int:
						__lowerCAmelCase:       Any            =				GPTSanJapaneseTester(self )
						__lowerCAmelCase:       Any            =				ConfigTester(self      ,  config_class=UpperCAmelCase      ,  d_model=3_7 )


					def   UpperCAmelCase  (							self      :		Tuple )							->     Any:
						GPTSanJapaneseConfig()


					def   UpperCAmelCase  (							self      :		Optional[int] )							->     Optional[Any]:
						__lowerCAmelCase:       Dict            =				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   UpperCAmelCase  (							self      :		List[str] )							->     Any:
						super().test_model_parallelism()


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







@require_torch
class 							A_			(		snake_case__							,     snake_case__							,     unittest.TestCase ):
					_lowercase      :					Any       =     (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
					_lowercase      :					Any       =     False
					_lowercase      :					str       =     False
					_lowercase      :					Dict       =     False
					_lowercase      :					Dict       =     False
					_lowercase      :					List[Any]       =     False
					_lowercase      :					Optional[Any]       =     False
					# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
					_lowercase      :					Any       =     [0.8, 0.9]


					def   UpperCAmelCase  (							self      :		int )							->     int:
						__lowerCAmelCase:       Tuple            =				GPTSanJapaneseTester(self )
						__lowerCAmelCase:       str            =				ConfigTester(self      ,  config_class=UpperCAmelCase      ,  d_model=3_7 )


					def   UpperCAmelCase  (							self      :		Optional[Any] )							->     Optional[int]:
						GPTSanJapaneseConfig()


					def   UpperCAmelCase  (							self      :		Optional[Any] )							->     List[Any]:
						__lowerCAmelCase:       Tuple            =				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   UpperCAmelCase  (							self      :		int )							->     Optional[int]:
						super().test_model_parallelism()


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


					@slow
					def   UpperCAmelCase  (							self      :		Optional[int] )							->     Optional[Any]:
						__lowerCAmelCase:       List[str]            =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
						__lowerCAmelCase:       Optional[Any]            =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
						__lowerCAmelCase:       Union[str, Any]            =				tokenizer.encode('武田信玄は'      ,  return_tensors='pt' )
						__lowerCAmelCase:       Any            =				model(UpperCAmelCase )
						__lowerCAmelCase:       str            =				outputs.logits.detach().cpu().numpy()
						# Output of original model created with mesh-tensoflow
						__lowerCAmelCase:       int            =				[
						    # fmt: off
						    [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
						    -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
						    -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
						    -12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
						    [-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
						    -7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
						    -6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
						    -8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
						    [-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
						    -11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
						    -9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
						    -10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
						    [-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
						    -9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
						    -8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
						    -11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
						    [-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
						    -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
						    -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
						    -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
						    # fmt: on
						]
						__lowerCAmelCase:       Dict            =				np.array(UpperCAmelCase ).flatten()
						__lowerCAmelCase:       Optional[Any]            =				output_logits[0, :, :2_0].flatten()

						def check(UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		Union[str, 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   UpperCAmelCase  (							self      :		Tuple )							->     List[str]:
						__lowerCAmelCase:       str            =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
						__lowerCAmelCase:       Any            =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
						model.to(UpperCAmelCase )

						# set deterministically
						__lowerCAmelCase:       Any            =				GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese' )
						__lowerCAmelCase:       List[str]            =				1

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

						__lowerCAmelCase:       Tuple            =				'left'
						__lowerCAmelCase:       Optional[Any]            =				tokenizer(UpperCAmelCase      ,  return_tensors='pt'      ,  padding=UpperCAmelCase )
						__lowerCAmelCase:       int            =				inputs['input_ids'].to(UpperCAmelCase )

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

						__lowerCAmelCase:       Optional[int]            =				model.generate(
						    input_ids=UpperCAmelCase      ,  attention_mask=inputs['attention_mask'].to(UpperCAmelCase )      ,  max_new_tokens=3      ,  generation_config=UpperCAmelCase      ,  )

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

						__lowerCAmelCase:       List[str]            =				tokenizer(sentences[1]      ,  return_tensors='pt' ).input_ids.to(UpperCAmelCase )
						__lowerCAmelCase:       int            =				model.generate(input_ids=UpperCAmelCase      ,  max_new_tokens=3      ,  generation_config=UpperCAmelCase )

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

						__lowerCAmelCase:       Dict            =				tokenizer.batch_decode(UpperCAmelCase      ,  skip_special_tokens=UpperCAmelCase )
						__lowerCAmelCase:       Optional[Any]            =				tokenizer.decode(output_non_padded[0]      ,  skip_special_tokens=UpperCAmelCase )
						__lowerCAmelCase:       List[Any]            =				tokenizer.decode(output_padded[0]      ,  skip_special_tokens=UpperCAmelCase )

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


					@tooslow
					def   UpperCAmelCase  (							self      :		Any )							->     Tuple:
						__lowerCAmelCase:       Optional[Any]            =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
						__lowerCAmelCase:       Union[str, Any]            =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
						# Output of original model created with mesh-tensoflow
						__lowerCAmelCase:       int            =				[
						    ('武田信玄は', 3_5_6_7_5),
						    ('武田信玄は、', 4_5),
						    ('武田信玄は、この', 2_9),
						    ('武田信玄は、このよう', 3_0_6_4_2),
						    ('武田信玄は、このような', 3_5_6_8_0),
						    ('武田信玄は、このような「', 8_6_4_0),
						    ('武田信玄は、このような「武田', 3_1_6_1_7),
						    ('武田信玄は、このような「武田家', 3_0_6_4_6),
						    ('武田信玄は、このような「武田家の', 3_1_6_1_7),
						    ('武田信玄は、このような「武田家の家', 3_1_3_8_1),
						]
						for input, output in target:
							__lowerCAmelCase:       Optional[int]            =				tokenizer.encode(UpperCAmelCase      ,  return_tensors='pt' )
							__lowerCAmelCase:       Dict            =				model(UpperCAmelCase )
							__lowerCAmelCase:       Tuple            =				outputs.logits.detach().cpu().numpy()[0]
							__lowerCAmelCase:       int            =				np.argmax(output_logits[-1] )
							self.assertEqual(UpperCAmelCase      ,  UpperCAmelCase )


					@slow
					def   UpperCAmelCase  (							self      :		Optional[Any] )							->     str:
						__lowerCAmelCase:       Optional[int]            =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
						__lowerCAmelCase:       Union[str, Any]            =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
						model.to(UpperCAmelCase )

						# set deterministically
						__lowerCAmelCase:       Any            =				GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese' )
						__lowerCAmelCase:       List[Any]            =				1

						__lowerCAmelCase:       Optional[int]            =				'武田信玄は、'
						__lowerCAmelCase:       List[Any]            =				tokenizer(UpperCAmelCase      ,  return_tensors='pt' ).input_ids.to(UpperCAmelCase )
						__lowerCAmelCase:       Tuple            =				tokenizer([input_text, input_text]      ,  return_tensors='pt' ).input_ids.to(UpperCAmelCase )

						# spout from uniform and one-hot
						__lowerCAmelCase:       str            =				[
						    # fmt: off
						    [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
						    0.04803985, 0.211572  , 0.23188473, 0.37153068, 0.7836377 ,
						    0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
						    0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
						    0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
						    0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
						    0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
						    0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
						    0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
						    0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
						    0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
						    0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
						    0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363  ,
						    0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
						    0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
						    0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
						    0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
						    0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
						    0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
						    0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
						    0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
						    0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
						    0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
						    0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
						    0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
						    0.9552151 , 0.93901511, 0.22489288], # 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
						]

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

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

						__lowerCAmelCase:       List[str]            =				model.generate(
						    input_ids=UpperCAmelCase      ,  spout=UpperCAmelCase      ,  max_new_tokens=2_0      ,  generation_config=UpperCAmelCase      ,  )

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

						__lowerCAmelCase:       Dict            =				[
						    '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   UpperCAmelCase  (							self      :		Optional[int] )							->     str:
						__lowerCAmelCase:       int            =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese' )
						__lowerCAmelCase:       int            =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese' )
						model.to(UpperCAmelCase )

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

						__lowerCAmelCase:       Dict            =				'武田信玄'
						__lowerCAmelCase:       Tuple            =				'織田信長'
						__lowerCAmelCase:       Tuple            =				'は、'
						__lowerCAmelCase:       Optional[Any]            =				'が、'
						__lowerCAmelCase:       Dict            =				tokenizer(UpperCAmelCase      ,  prefix_text=UpperCAmelCase      ,  return_tensors='pt' )
						__lowerCAmelCase:       Tuple            =				tokenizer(UpperCAmelCase      ,  prefix_text=UpperCAmelCase      ,  return_tensors='pt' )
						__lowerCAmelCase:       Union[str, Any]            =				tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]      ,  return_tensors='pt' )

						__lowerCAmelCase:       Optional[Any]            =				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=2_0      ,  generation_config=UpperCAmelCase      ,  )

						__lowerCAmelCase:       int            =				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=2_0      ,  generation_config=UpperCAmelCase      ,  )

						__lowerCAmelCase:       Any            =				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=2_0      ,  generation_config=UpperCAmelCase      ,  )

						__lowerCAmelCase:       Optional[Any]            =				tokenizer.decode(outputa[0] )
						__lowerCAmelCase:       str            =				tokenizer.decode(outputa[0] )
						__lowerCAmelCase:       int            =				tokenizer.batch_decode(UpperCAmelCase )

						__lowerCAmelCase:       Dict            =				[
						    '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] )








import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
_a			=       False
_a			=       Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
_a			=       datasets.Features(
    OrderedDict(
        {
            '''attr_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''attr_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''boxes''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='''float32'''),
            '''img_id''': datasets.Value('''int32'''),
            '''obj_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''obj_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''roi_features''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)






class 							A_			:


     def __init__(							self      :		Optional[int]      ,  UpperCAmelCase      :		str=sys.argv[1:] )							->     Optional[int]:
      __lowerCAmelCase:       List[Any]            =				None
      __lowerCAmelCase:       str            =				None
      __lowerCAmelCase:       str            =				None
      __lowerCAmelCase:       Any            =				1
      __lowerCAmelCase     ,							__lowerCAmelCase:       int            =				getopt.getopt(UpperCAmelCase      ,  'i:o:b:s'      ,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list='] )
      for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
        __lowerCAmelCase:       Any            =				arg
       elif opt in ("-o", "--outfile"):
        __lowerCAmelCase:       List[Any]            =				arg
       elif opt in ("-b", "--batch_size"):
        __lowerCAmelCase:       List[Any]            =				int(UpperCAmelCase )
       elif opt in ("-s", "--subset_list"):
        __lowerCAmelCase:       Union[str, Any]            =				arg

      assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
      assert outputfile is not None and not os.path.isfile(UpperCAmelCase ), F'''{outputfile}'''
      if subset_list is not None:
       with open(os.path.realpath(UpperCAmelCase ) ) as f:
        __lowerCAmelCase:       Any            =				{self._vqa_file_split()[0] for x in tryload(UpperCAmelCase )}
      else:
       __lowerCAmelCase:       Union[str, Any]            =				None

      __lowerCAmelCase:       List[str]            =				CONFIG
      if torch.cuda.is_available():
       __lowerCAmelCase:       Optional[Any]            =				'cuda'
      __lowerCAmelCase:       Union[str, Any]            =				os.path.realpath(UpperCAmelCase )
      __lowerCAmelCase:       List[Any]            =				os.path.realpath(UpperCAmelCase )
      __lowerCAmelCase:       Optional[Any]            =				Preprocess(self.config )
      __lowerCAmelCase:       int            =				GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'      ,  config=self.config )
      __lowerCAmelCase:       Tuple            =				batch_size if batch_size != 0 else 1
      __lowerCAmelCase:       Optional[int]            =				DEFAULT_SCHEMA


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Dict )							->     Dict:
      __lowerCAmelCase:       str            =				int(file.split('.' )[0].split('_' )[-1] )
      __lowerCAmelCase:       Union[str, Any]            =				os.path.join(self.inputdir      ,  UpperCAmelCase )
      return (img_id, filepath)


     @property
     def   UpperCAmelCase  (							self      :		int )							->     Dict:
      __lowerCAmelCase:       Any            =				[]
      for i, file in enumerate(os.listdir(self.inputdir ) ):
       if self.subset_list is not None and i not in self.subset_list:
        continue
       batch.append(self._vqa_file_split(UpperCAmelCase ) )
       if len(UpperCAmelCase ) == self.batch:
        __lowerCAmelCase:       Optional[int]            =				batch
        __lowerCAmelCase:       Tuple            =				[]
        yield list(map(UpperCAmelCase      ,  zip(*UpperCAmelCase ) ) )

      for i in range(1 ):
       yield list(map(UpperCAmelCase      ,  zip(*UpperCAmelCase ) ) )


     def __call__(							self      :		Tuple )							->     Dict:
      # make writer
      if not TEST:
       __lowerCAmelCase:       Tuple            =				datasets.ArrowWriter(features=self.schema      ,  path=self.outputfile )
      # do file generator
      for i, (img_ids, filepaths) in enumerate(self.file_generator ):
       __lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase:       List[Any]            =				self.preprocess(UpperCAmelCase )
       __lowerCAmelCase:       str            =				self.model(
           UpperCAmelCase      ,  UpperCAmelCase      ,  scales_yx=UpperCAmelCase      ,  padding='max_detections'      ,  max_detections=self.config.MAX_DETECTIONS      ,  pad_value=0      ,  return_tensors='np'      ,  location='cpu'      ,  )
       __lowerCAmelCase:       str            =				output_dict.pop('normalized_boxes' )
       if not TEST:
        __lowerCAmelCase:       Dict            =				np.array(UpperCAmelCase )
        __lowerCAmelCase:       Optional[int]            =				self.schema.encode_batch(UpperCAmelCase )
        writer.write_batch(UpperCAmelCase )
       if TEST:
        break
       # finalizer the writer
      if not TEST:
       __lowerCAmelCase     ,							__lowerCAmelCase:       Optional[int]            =				writer.finalize()
       print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb''' )







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[int]		)   ->   Any:

 """simple docstring"""






 try:
  __lowerCAmelCase:       Optional[Any]            =				json.load(SCREAMING_SNAKE_CASE		)
  try:
   __lowerCAmelCase:       Tuple            =				list(data.keys()		)
  except Exception:
   __lowerCAmelCase:       int            =				[d['img_id'] for d in data]
 except Exception:
  try:
   __lowerCAmelCase:       Dict            =				eval(stream.read()		)
  except Exception:
   __lowerCAmelCase:       Dict            =				stream.read().split('\n'		)
 return data


if __name__ == "__main__":
  _a			=       Extract(sys.argv[1:])
  extract()
  if not TEST:
    _a			=       datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)





