
import unittest

import numpy as np

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

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


class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=36000						,    __UpperCamelCase=13						,    __UpperCamelCase=7						,    __UpperCamelCase=True						,    __UpperCamelCase=32						,    __UpperCamelCase=42						,    __UpperCamelCase=5						,    __UpperCamelCase=2						,    __UpperCamelCase=4						,    __UpperCamelCase=2						,    __UpperCamelCase=32						,    __UpperCamelCase=80						,    __UpperCamelCase=33						,    __UpperCamelCase=0.0						,    __UpperCamelCase=1E-6						,    __UpperCamelCase=100						,    __UpperCamelCase=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 					lowercase_						(					self					):
      return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"					)

  def 					lowercase_						(					self					):
      A_								=					ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size					)

      A_								=					self.get_config()

      return (config, input_ids)

  def 					lowercase_						(					self					):
      A_								=					ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size					)

      A_								=					self.get_config()

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

  def 					lowercase_						(					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 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    ):
      A_								=					GPTSanJapaneseForConditionalGeneration(config=__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()
      A_								=					model(
          input_ids=__UpperCamelCase						,    )
      self.parent.assertIsNotNone(__UpperCamelCase					)



@require_torch
class 							lowerCamelCase       (		__snake_case			,  __snake_case			,  unittest.TestCase					):



  """simple docstring"""





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

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      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 					lowercase_						(					self					):
      A_								=					GPTSanJapaneseTester(self					)
      A_								=					ConfigTester(self						,    config_class=__UpperCamelCase						,    d_model=37					)

  def 					lowercase_						(					self					):
      GPTSanJapaneseConfig()

  def 					lowercase_						(					self					):
      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 					lowercase_						(					self					):
      super().test_model_parallelism()

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



@require_torch
class 							lowerCamelCase       (		__snake_case			,  __snake_case			,  unittest.TestCase					):



  """simple docstring"""





  lowerCAmelCase_								=		(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
  lowerCAmelCase_								=		False
  lowerCAmelCase_								=		False
  lowerCAmelCase_								=		False
  lowerCAmelCase_								=		False
  lowerCAmelCase_								=		False
  lowerCAmelCase_								=		False
  # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  lowerCAmelCase_								=		[0.8, 0.9]

  def 					lowercase_						(					self					):
      A_								=					GPTSanJapaneseTester(self					)
      A_								=					ConfigTester(self						,    config_class=__UpperCamelCase						,    d_model=37					)

  def 					lowercase_						(					self					):
      GPTSanJapaneseConfig()

  def 					lowercase_						(					self					):
      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 					lowercase_						(					self					):
      super().test_model_parallelism()

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

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

      def check(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase=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 					lowercase_						(					self					):
      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 					lowercase_						(					self					):
      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", 35675),
          ("武田信玄は、", 45),
          ("武田信玄は、この", 29),
          ("武田信玄は、このよう", 30642),
          ("武田信玄は、このような", 35680),
          ("武田信玄は、このような「", 8640),
          ("武田信玄は、このような「武田", 31617),
          ("武田信玄は、このような「武田家", 30646),
          ("武田信玄は、このような「武田家の", 31617),
          ("武田信玄は、このような「武田家の家", 31381),
      ]
      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 					lowercase_						(					self					):
      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
          [0.8788_2208, 0.3842_6396, 0.3322_0248, 0.4389_0406, 0.1656_2252,
          0.0480_3985, 0.21_1572  , 0.2318_8473, 0.3715_3068, 0.783_6377 ,
          0.0216_0172, 0.3876_1719, 0.7529_0772, 0.9019_8857, 0.3436_5777,
          0.6416_8169, 0.4431_8471, 0.1457_5746, 0.9256_2881, 0.4081_2148,
          0.2901_9122, 0.8886_1599, 0.6552_4846, 0.4356_3456, 0.3817_7187,
          0.7083_2965, 0.8152_7892, 0.6883_2812, 0.3883_3192, 0.456_1522 ,
          0.1482_8817, 0.4724_8213, 0.5435_7335, 0.8200_9566, 0.133_8884 ,
          0.0275_5417, 0.1976_4677, 0.242_2084 , 0.0475_7674, 0.6540_9606,
          0.082_4589 , 0.0330_4383, 0.9438_7689, 0.9876_4509, 0.8243_3901,
          0.2764_6741, 0.6490_7493, 0.7600_9406, 0.3008_7915, 0.1790_4689,
          0.4160_1714, 0.6704_6398, 0.1042_2822, 0.0844_7374, 0.0735_4344,
          0.6142_3565, 0.7028_4866, 0.753_2333 , 0.197_2038 , 0.2957_5659,
          0.9058_3886, 0.2926_5307, 0.5000_0175, 0.7040_7655, 0.88_9363  ,
          0.8190_4418, 0.6682_9128, 0.6446_8815, 0.5656_3723, 0.8560_1875,
          0.9492_4672, 0.0016_6762, 0.2522_0643, 0.7454_0219, 0.6799_3247,
          0.154_9675 , 0.3938_5352, 0.9215_3607, 0.6374_5931, 0.2775_9043,
          0.8470_2295, 0.6590_4271, 0.5867_6614, 0.866_6936 , 0.3960_7438,
          0.7995_4983, 0.4222_0697, 0.3965_0381, 0.784_9864 , 0.5615_0201,
          0.1567_8925, 0.1474_6032, 0.3454_2114, 0.4702_6783, 0.1195_6489,
          0.2542_1435, 0.3378_8901, 0.6893_4842, 0.3642_4685, 0.7173_7898,
          0.3898_3449, 0.9439_3779, 0.3957_5588, 0.3661_6553, 0.8710_4665,
          0.6463_0203, 0.2251_6905, 0.8827_0804, 0.1503_1338, 0.7514_4345,
          0.4645_9025, 0.8539_6454, 0.8635_5643, 0.6513_9851, 0.7026_6061,
          0.3024_1389, 0.8105_6497, 0.8886_5969, 0.3877_3807, 0.7063_5849,
          0.9071_8459, 0.4324_5789, 0.2800_0654, 0.4593_5562, 0.0877_3519,
          0.955_2151 , 0.9390_1511, 0.2248_9288], # uniform
          [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0., 0., 0., 0., 0., 0., 0.],
          # fmt: on
      ]

      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 					lowercase_						(					self					):
      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]					)








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
__magic_name__  :  Dict     			=   False
__magic_name__  :  Union[str, Any]     			=   Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__magic_name__  :  Optional[Any]     			=   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 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase=sys.argv[1:]					):
      A_								=					None
      A_								=					None
      A_								=					None
      A_								=					1
      A_	,	A_								=					getopt.getopt(__UpperCamelCase						,    "i:o:b:s"						,    ["inputdir=", "outfile=", "batch_size=", "subset_list="]					)
      for opt, arg in opts:
          if opt in ("-i", "--inputdir"):
              A_								=					arg
          elif opt in ("-o", "--outfile"):
              A_								=					arg
          elif opt in ("-b", "--batch_size"):
              A_								=					int(__UpperCamelCase					)
          elif opt in ("-s", "--subset_list"):
              A_								=					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:
              A_								=					{self._vqa_file_split()[0] for x in tryload(__UpperCamelCase					)}
      else:
          A_								=					None

      A_								=					CONFIG
      if torch.cuda.is_available():
          A_								=					"cuda"
      A_								=					os.path.realpath(__UpperCamelCase					)
      A_								=					os.path.realpath(__UpperCamelCase					)
      A_								=					Preprocess(self.config					)
      A_								=					GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"						,    config=self.config					)
      A_								=					batch_size if batch_size != 0 else 1
      A_								=					DEFAULT_SCHEMA

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					int(file.split("."					)[0].split("_"					)[-1]					)
      A_								=					os.path.join(self.inputdir						,    __UpperCamelCase					)
      return (img_id, filepath)

  @property
  def 					lowercase_						(					self					):
      A_								=					[]
      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:
              A_								=					batch
              A_								=					[]
              yield list(map(__UpperCamelCase						,    zip(*__UpperCamelCase					)					)					)

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

  def __call__(					self					):
      # make writer
      if not TEST:
          A_								=					datasets.ArrowWriter(features=self.schema						,    path=self.outputfile					)
      # do file generator
      for i, (img_ids, filepaths) in enumerate(self.file_generator					):
          A_	,	A_	,	A_								=					self.preprocess(__UpperCamelCase					)
          A_								=					self.model(
              __UpperCamelCase						,    __UpperCamelCase						,    scales_yx=__UpperCamelCase						,    padding="max_detections"						,    max_detections=self.config.MAX_DETECTIONS						,    pad_value=0						,    return_tensors="np"						,    location="cpu"						,    )
          A_								=					output_dict.pop("normalized_boxes"					)
          if not TEST:
              A_								=					np.array(__UpperCamelCase					)
              A_								=					self.schema.encode_batch(__UpperCamelCase					)
              writer.write_batch(__UpperCamelCase					)
          if TEST:
              break
          # finalizer the writer
      if not TEST:
          A_	,	A_								=					writer.finalize()
          print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'					)





def        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    try:
        A_								=					json.load(snake_case__ )
        try:
            A_								=					list(data.keys() )
        except Exception:
            A_								=					[d["img_id"] for d in data]
    except Exception:
        try:
            A_								=					eval(stream.read() )
        except Exception:
            A_								=					stream.read().split("\n" )
    return data


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






