
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         RemBertConfig,
         RemBertForCausalLM,
         RemBertForMaskedLM,
         RemBertForMultipleChoice,
         RemBertForQuestionAnswering,
         RemBertForSequenceClassification,
         RemBertForTokenClassification,
         RemBertModel,
     )
     from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST


class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=13						,    __UpperCamelCase=7						,    __UpperCamelCase=True						,    __UpperCamelCase=True						,    __UpperCamelCase=True						,    __UpperCamelCase=True						,    __UpperCamelCase=99						,    __UpperCamelCase=32						,    __UpperCamelCase=18						,    __UpperCamelCase=43						,    __UpperCamelCase=5						,    __UpperCamelCase=4						,    __UpperCamelCase=37						,    __UpperCamelCase="gelu"						,    __UpperCamelCase=0.1						,    __UpperCamelCase=0.1						,    __UpperCamelCase=512						,    __UpperCamelCase=16						,    __UpperCamelCase=2						,    __UpperCamelCase=0.02						,    __UpperCamelCase=3						,    __UpperCamelCase=4						,    __UpperCamelCase=None						,    ):
      A_								=					parent
      A_								=					batch_size
      A_								=					seq_length
      A_								=					is_training
      A_								=					use_input_mask
      A_								=					use_token_type_ids
      A_								=					use_labels
      A_								=					vocab_size
      A_								=					hidden_size
      A_								=					input_embedding_size
      A_								=					output_embedding_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_								=					type_vocab_size
      A_								=					type_sequence_label_size
      A_								=					initializer_range
      A_								=					num_labels
      A_								=					num_choices
      A_								=					scope

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

      A_								=					None
      if self.use_input_mask:
          A_								=					random_attention_mask([self.batch_size, self.seq_length]					)

      A_								=					None
      if self.use_token_type_ids:
          A_								=					ids_tensor([self.batch_size, self.seq_length]						,    self.type_vocab_size					)

      A_								=					None
      A_								=					None
      A_								=					None
      if self.use_labels:
          A_								=					ids_tensor([self.batch_size]						,    self.type_sequence_label_size					)
          A_								=					ids_tensor([self.batch_size, self.seq_length]						,    self.num_labels					)
          A_								=					ids_tensor([self.batch_size]						,    self.num_choices					)

      A_								=					RemBertConfig(
          vocab_size=self.vocab_size						,    hidden_size=self.hidden_size						,    input_embedding_size=self.input_embedding_size						,    output_embedding_size=self.output_embedding_size						,    num_hidden_layers=self.num_hidden_layers						,    num_attention_heads=self.num_attention_heads						,    intermediate_size=self.intermediate_size						,    hidden_act=self.hidden_act						,    hidden_dropout_prob=self.hidden_dropout_prob						,    attention_probs_dropout_prob=self.attention_probs_dropout_prob						,    max_position_embeddings=self.max_position_embeddings						,    type_vocab_size=self.type_vocab_size						,    is_decoder=__UpperCamelCase						,    initializer_range=self.initializer_range						,    )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

  def 					lowercase_						(					self					):
      (
          (
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	
      )								=					self.prepare_config_and_inputs()

      A_								=					True
      A_								=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
      A_								=					ids_tensor([self.batch_size, self.seq_length]						,    vocab_size=2					)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					RemBertModel(config=__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()
      A_								=					model(__UpperCamelCase						,    attention_mask=__UpperCamelCase						,    token_type_ids=__UpperCamelCase					)
      A_								=					model(__UpperCamelCase						,    token_type_ids=__UpperCamelCase					)
      A_								=					model(__UpperCamelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    ):
      A_								=					True
      A_								=					RemBertModel(__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()
      A_								=					model(
          __UpperCamelCase						,    attention_mask=__UpperCamelCase						,    token_type_ids=__UpperCamelCase						,    encoder_hidden_states=__UpperCamelCase						,    encoder_attention_mask=__UpperCamelCase						,    )
      A_								=					model(
          __UpperCamelCase						,    attention_mask=__UpperCamelCase						,    token_type_ids=__UpperCamelCase						,    encoder_hidden_states=__UpperCamelCase						,    )
      A_								=					model(__UpperCamelCase						,    attention_mask=__UpperCamelCase						,    token_type_ids=__UpperCamelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    ):
      A_								=					RemBertForCausalLM(config=__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()
      A_								=					model(__UpperCamelCase						,    attention_mask=__UpperCamelCase						,    token_type_ids=__UpperCamelCase						,    labels=__UpperCamelCase					)
      self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.vocab_size)					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					RemBertForMaskedLM(config=__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()
      A_								=					model(__UpperCamelCase						,    attention_mask=__UpperCamelCase						,    token_type_ids=__UpperCamelCase						,    labels=__UpperCamelCase					)
      self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.vocab_size)					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    ):
      A_								=					True
      A_								=					True
      A_								=					RemBertForCausalLM(config=__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()

      # first forward pass
      A_								=					model(
          __UpperCamelCase						,    attention_mask=__UpperCamelCase						,    encoder_hidden_states=__UpperCamelCase						,    encoder_attention_mask=__UpperCamelCase						,    use_cache=__UpperCamelCase						,    )
      A_								=					outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      A_								=					ids_tensor((self.batch_size, 3)						,    config.vocab_size					)
      A_								=					ids_tensor((self.batch_size, 3)						,    vocab_size=2					)

      # append to next input_ids and
      A_								=					torch.cat([input_ids, next_tokens]						,    dim=-1					)
      A_								=					torch.cat([input_mask, next_mask]						,    dim=-1					)

      A_								=					model(
          __UpperCamelCase						,    attention_mask=__UpperCamelCase						,    encoder_hidden_states=__UpperCamelCase						,    encoder_attention_mask=__UpperCamelCase						,    output_hidden_states=__UpperCamelCase						,    )["hidden_states"][0]
      A_								=					model(
          __UpperCamelCase						,    attention_mask=__UpperCamelCase						,    encoder_hidden_states=__UpperCamelCase						,    encoder_attention_mask=__UpperCamelCase						,    past_key_values=__UpperCamelCase						,    output_hidden_states=__UpperCamelCase						,    )["hidden_states"][0]

      # select random slice
      A_								=					ids_tensor((1,)						,    output_from_past.shape[-1]					).item()
      A_								=					output_from_no_past[:, -3:, random_slice_idx].detach()
      A_								=					output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(__UpperCamelCase						,    __UpperCamelCase						,    atol=1E-3					)					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					RemBertForQuestionAnswering(config=__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()
      A_								=					model(
          __UpperCamelCase						,    attention_mask=__UpperCamelCase						,    token_type_ids=__UpperCamelCase						,    start_positions=__UpperCamelCase						,    end_positions=__UpperCamelCase						,    )
      self.parent.assertEqual(result.start_logits.shape						,    (self.batch_size, self.seq_length)					)
      self.parent.assertEqual(result.end_logits.shape						,    (self.batch_size, self.seq_length)					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					self.num_labels
      A_								=					RemBertForSequenceClassification(__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()
      A_								=					model(__UpperCamelCase						,    attention_mask=__UpperCamelCase						,    token_type_ids=__UpperCamelCase						,    labels=__UpperCamelCase					)
      self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_labels)					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					self.num_labels
      A_								=					RemBertForTokenClassification(config=__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()
      A_								=					model(__UpperCamelCase						,    attention_mask=__UpperCamelCase						,    token_type_ids=__UpperCamelCase						,    labels=__UpperCamelCase					)
      self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.num_labels)					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					self.num_choices
      A_								=					RemBertForMultipleChoice(config=__UpperCamelCase					)
      model.to(__UpperCamelCase					)
      model.eval()
      A_								=					input_ids.unsqueeze(1					).expand(-1						,    self.num_choices						,    -1					).contiguous()
      A_								=					token_type_ids.unsqueeze(1					).expand(-1						,    self.num_choices						,    -1					).contiguous()
      A_								=					input_mask.unsqueeze(1					).expand(-1						,    self.num_choices						,    -1					).contiguous()
      A_								=					model(
          __UpperCamelCase						,    attention_mask=__UpperCamelCase						,    token_type_ids=__UpperCamelCase						,    labels=__UpperCamelCase						,    )
      self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_choices)					)

  def 					lowercase_						(					self					):
      A_								=					self.prepare_config_and_inputs()
      (
          (
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	
      )								=					config_and_inputs
      A_								=					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict



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



  """simple docstring"""





  lowerCAmelCase_								=		(
      (
          RemBertModel,
          RemBertForMaskedLM,
          RemBertForCausalLM,
          RemBertForMultipleChoice,
          RemBertForQuestionAnswering,
          RemBertForSequenceClassification,
          RemBertForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  lowerCAmelCase_								=		(RemBertForCausalLM,) if is_torch_available() else ()
  lowerCAmelCase_								=		(
      {
          """feature-extraction""": RemBertModel,
          """fill-mask""": RemBertForMaskedLM,
          """question-answering""": RemBertForQuestionAnswering,
          """text-classification""": RemBertForSequenceClassification,
          """text-generation""": RemBertForCausalLM,
          """token-classification""": RemBertForTokenClassification,
          """zero-shot""": RemBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  def 					lowercase_						(					self					):
      A_								=					RemBertModelTester(self					)
      A_								=					ConfigTester(self						,    config_class=__UpperCamelCase						,    hidden_size=37					)

  def 					lowercase_						(					self					):
      self.config_tester.run_common_tests()

  def 					lowercase_						(					self					):
      A_								=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
          A_								=					type
          self.model_tester.create_and_check_model(*__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*__UpperCamelCase					)

  def 					lowercase_						(					self					):
      # This regression test was failing with PyTorch < 1.3
      (
          (
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	(
          A_
      )	,	
      )								=					self.model_tester.prepare_config_and_inputs_for_decoder()

      A_								=					None

      self.model_tester.create_and_check_model_as_decoder(
          __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    )

  @slow
  def 					lowercase_						(					self					):
      for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          A_								=					RemBertModel.from_pretrained(__UpperCamelCase					)
          self.assertIsNotNone(__UpperCamelCase					)



@require_torch
class 							lowerCamelCase       (		unittest.TestCase					):



  """simple docstring"""





  @slow
  def 					lowercase_						(					self					):
      # Test exact values at the last hidden layer
      A_								=					RemBertModel.from_pretrained("google/rembert"					)
      A_								=					torch.tensor([[312, 56498, 313, 2125, 313]]					)
      A_								=					torch.tensor([[0, 0, 0, 1, 1]]					)
      with torch.no_grad():
          A_								=					model(__UpperCamelCase						,    token_type_ids=__UpperCamelCase						,    output_hidden_states=__UpperCamelCase					)

      A_								=					1152

      A_								=					torch.Size((1, 5, hidden_size)					)
      self.assertEqual(output["last_hidden_state"].shape						,    __UpperCamelCase					)

      A_								=					torch.tensor(
          [
              [
                  [0.0754, -0.2022, 0.1904],
                  [-0.3354, -0.3692, -0.4791],
                  [-0.2314, -0.6729, -0.0749],
                  [-0.0396, -0.3105, -0.4234],
                  [-0.1571, -0.0525, 0.5353],
              ]
          ]					)

      # Running on the original tf implementation gives slightly different results here.
      # Not clear why this variations is present
      # TODO: Find reason for discrepancy
      # expected_original_implementation = [[
      #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
      #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
      #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
      #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
      #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
      # ]]

      self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3]						,    __UpperCamelCase						,    atol=1E-4					)					)








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)






