

'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_a			:	int								=  logging.get_logger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[Any]    ,				SCREAMING_SNAKE_CASE :				int=False    ,				SCREAMING_SNAKE_CASE :				Any=False  ):
  UpperCAmelCase  							=		'backbone.' if is_semantic else ''

  UpperCAmelCase  							=		[]
  for i in range(config.num_hidden_layers  ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''')  )
    rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')  )
    rename_keys.append(
        (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')  )
    rename_keys.append(
        (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')  )
    rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')  )
    rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')  )
    rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')  )
    rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')  )
    rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')  )
    rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')  )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          (f'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
          (f'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
          (f'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
      ]  )

  if has_lm_head:
    # mask token + shared relative position bias + layernorm
    rename_keys.extend(
        [
            ('mask_token', 'beit.embeddings.mask_token'),
            (
                'rel_pos_bias.relative_position_bias_table',
                'beit.encoder.relative_position_bias.relative_position_bias_table',
            ),
            (
                'rel_pos_bias.relative_position_index',
                'beit.encoder.relative_position_bias.relative_position_index',
            ),
            ('norm.weight', 'layernorm.weight'),
            ('norm.bias', 'layernorm.bias'),
        ]  )
  elif is_semantic:
    # semantic segmentation classification heads
    rename_keys.extend(
        [
            ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
            ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
            ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
            ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
        ]  )
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
            ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
            ('head.weight', 'classifier.weight'),
            ('head.bias', 'classifier.bias'),
        ]  )

  return rename_keys


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[Any]    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				Optional[int]=False    ,				SCREAMING_SNAKE_CASE :				List[str]=False  ):
  for i in range(config.num_hidden_layers  ):
    UpperCAmelCase  							=		'backbone.' if is_semantic else ''
    # queries, keys and values
    UpperCAmelCase  							=		state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''  )
    UpperCAmelCase  							=		state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''  )
    UpperCAmelCase  							=		state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''  )

    UpperCAmelCase  							=		in_proj_weight[
        : config.hidden_size, :
    ]
    UpperCAmelCase  							=		q_bias
    UpperCAmelCase  							=		in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    UpperCAmelCase  							=		in_proj_weight[
        -config.hidden_size :, :
    ]
    UpperCAmelCase  							=		v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    UpperCAmelCase  							=		state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''  )
    UpperCAmelCase  							=		state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''  )

    UpperCAmelCase  							=		gamma_a
    UpperCAmelCase  							=		gamma_a

    # relative_position bias table + index
    if not has_lm_head:
      # each layer has its own relative position bias
      UpperCAmelCase  							=		state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''  )
      UpperCAmelCase  							=		state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''  )

      UpperCAmelCase  							=		table
      UpperCAmelCase  							=		index


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				int  ):
  UpperCAmelCase  							=		dct.pop(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		val


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		'http://images.cocodataset.org/val2017/000000039769.jpg'
  UpperCAmelCase  							=		Image.open(requests.get(SCREAMING_SNAKE_CASE    ,				stream=SCREAMING_SNAKE_CASE  ).raw  )
  return im


@torch.no_grad()
def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				List[str]  ):
  UpperCAmelCase  							=		BeitConfig()
  UpperCAmelCase  							=		False
  UpperCAmelCase  							=		False
  UpperCAmelCase  							=		'huggingface/label-files'
  # set config parameters based on URL
  if checkpoint_url[-9:-4] == "pt22k":
    # masked image modeling
    UpperCAmelCase  							=		True
    UpperCAmelCase  							=		True
    UpperCAmelCase  							=		True
  elif checkpoint_url[-9:-4] == "ft22k":
    # intermediate fine-tuning on ImageNet-22k
    UpperCAmelCase  							=		True
    UpperCAmelCase  							=		2_1841
    UpperCAmelCase  							=		'imagenet-22k-id2label.json'
    UpperCAmelCase  							=		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				repo_type='dataset'  )    ,				'r'  )  )
    UpperCAmelCase  							=		{int(SCREAMING_SNAKE_CASE  ): v for k, v in idalabel.items()}
    # this dataset contains 21843 labels but the model only has 21841
    # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
    del idalabel[9205]
    del idalabel[1_5027]
    UpperCAmelCase  							=		idalabel
    UpperCAmelCase  							=		{v: k for k, v in idalabel.items()}
  elif checkpoint_url[-8:-4] == "to1k":
    # fine-tuning on ImageNet-1k
    UpperCAmelCase  							=		True
    UpperCAmelCase  							=		1000
    UpperCAmelCase  							=		'imagenet-1k-id2label.json'
    UpperCAmelCase  							=		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				repo_type='dataset'  )    ,				'r'  )  )
    UpperCAmelCase  							=		{int(SCREAMING_SNAKE_CASE  ): v for k, v in idalabel.items()}
    UpperCAmelCase  							=		idalabel
    UpperCAmelCase  							=		{v: k for k, v in idalabel.items()}
    if "384" in checkpoint_url:
      UpperCAmelCase  							=		384
    if "512" in checkpoint_url:
      UpperCAmelCase  							=		512
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    UpperCAmelCase  							=		True
    UpperCAmelCase  							=		150
    UpperCAmelCase  							=		'ade20k-id2label.json'
    UpperCAmelCase  							=		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				repo_type='dataset'  )    ,				'r'  )  )
    UpperCAmelCase  							=		{int(SCREAMING_SNAKE_CASE  ): v for k, v in idalabel.items()}
    UpperCAmelCase  							=		idalabel
    UpperCAmelCase  							=		{v: k for k, v in idalabel.items()}
    UpperCAmelCase  							=		640
    UpperCAmelCase  							=		True
  else:
    raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''  )

  # size of the architecture
  if "base" in checkpoint_url:
    pass
  elif "large" in checkpoint_url:
    UpperCAmelCase  							=		1024
    UpperCAmelCase  							=		4096
    UpperCAmelCase  							=		24
    UpperCAmelCase  							=		16
    if "ade20k" in checkpoint_url:
      UpperCAmelCase  							=		640
      UpperCAmelCase  							=		[7, 11, 15, 23]
  else:
    raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'  )

  # load state_dict of original model, remove and rename some keys
  UpperCAmelCase  							=		torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE    ,				map_location='cpu'    ,				check_hash=SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

  UpperCAmelCase  							=		create_rename_keys(SCREAMING_SNAKE_CASE    ,				has_lm_head=SCREAMING_SNAKE_CASE    ,				is_semantic=SCREAMING_SNAKE_CASE  )
  for src, dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )
  read_in_q_k_v(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				has_lm_head=SCREAMING_SNAKE_CASE    ,				is_semantic=SCREAMING_SNAKE_CASE  )
  if is_semantic:
    # add prefix to decoder keys
    for key, val in state_dict.copy().items():
      UpperCAmelCase  							=		state_dict.pop(SCREAMING_SNAKE_CASE  )
      if key.startswith('backbone.fpn'  ):
        UpperCAmelCase  							=		key.replace('backbone.fpn'    ,				'fpn'  )
      UpperCAmelCase  							=		val

    # load HuggingFace model
  if checkpoint_url[-9:-4] == "pt22k":
    UpperCAmelCase  							=		BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE  )
  elif "ade20k" in checkpoint_url:
    UpperCAmelCase  							=		BeitForSemanticSegmentation(SCREAMING_SNAKE_CASE  )
  else:
    UpperCAmelCase  							=		BeitForImageClassification(SCREAMING_SNAKE_CASE  )
  model.eval()
  model.load_state_dict(SCREAMING_SNAKE_CASE  )

  # Check outputs on an image
  if is_semantic:
    UpperCAmelCase  							=		BeitImageProcessor(size=config.image_size    ,				do_center_crop=SCREAMING_SNAKE_CASE  )
    UpperCAmelCase  							=		load_dataset('hf-internal-testing/fixtures_ade20k'    ,				split='test'  )
    UpperCAmelCase  							=		Image.open(ds[0]['file']  )
  else:
    UpperCAmelCase  							=		BeitImageProcessor(
        size=config.image_size    ,				resample=PILImageResampling.BILINEAR    ,				do_center_crop=SCREAMING_SNAKE_CASE  )
    UpperCAmelCase  							=		prepare_img()

  UpperCAmelCase  							=		image_processor(images=SCREAMING_SNAKE_CASE    ,				return_tensors='pt'  )
  UpperCAmelCase  							=		encoding['pixel_values']

  UpperCAmelCase  							=		model(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		outputs.logits

  # verify logits
  UpperCAmelCase  							=		torch.Size([1, 1000]  )
  if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'  ):
    UpperCAmelCase  							=		torch.Size([1, 196, 8192]  )
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'  ):
    UpperCAmelCase  							=		torch.Size([1, 196, 8192]  )
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'  ):
    UpperCAmelCase  							=		torch.Size([1, 2_1841]  )
    UpperCAmelCase  							=		torch.tensor([2.2288, 2.4671, 0.7395]  )
    UpperCAmelCase  							=		2397
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'  ):
    UpperCAmelCase  							=		torch.Size([1, 2_1841]  )
    UpperCAmelCase  							=		torch.tensor([1.6881, -0.2787, 0.5901]  )
    UpperCAmelCase  							=		2396
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'  ):
    UpperCAmelCase  							=		torch.tensor([0.1241, 0.0798, -0.6569]  )
    UpperCAmelCase  							=		285
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'  ):
    UpperCAmelCase  							=		torch.tensor([-1.2385, -1.0987, -1.0108]  )
    UpperCAmelCase  							=		281
  elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'  ):
    UpperCAmelCase  							=		torch.tensor([-1.5303, -0.9484, -0.3147]  )
    UpperCAmelCase  							=		761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'  ):
    UpperCAmelCase  							=		torch.tensor([0.4610, -0.0928, 0.2086]  )
    UpperCAmelCase  							=		761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'  ):
    UpperCAmelCase  							=		torch.tensor([-0.4804, 0.6257, -0.1837]  )
    UpperCAmelCase  							=		761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'  ):
    UpperCAmelCase  							=		torch.tensor([[-0.5122, 0.5117, -0.2113]]  )
    UpperCAmelCase  							=		761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'  ):
    UpperCAmelCase  							=		torch.tensor([-0.3062, 0.7261, 0.4852]  )
    UpperCAmelCase  							=		761
  elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'  ):
    UpperCAmelCase  							=		(1, 150, 160, 160)
    UpperCAmelCase  							=		torch.tensor(
        [
            [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
            [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
            [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
        ]  )
  elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'  ):
    UpperCAmelCase  							=		(1, 150, 160, 160)
    UpperCAmelCase  							=		torch.tensor(
        [
            [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
            [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
            [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
        ]  )
  else:
    raise ValueError('Can\'t verify logits as model is not supported'  )

  assert logits.shape == expected_shape, "Shape of logits not as expected"
  if not has_lm_head:
    if is_semantic:
      assert torch.allclose(
          logits[0, :3, :3, :3]    ,				SCREAMING_SNAKE_CASE    ,				atol=1E-3  ), "First elements of logits not as expected"
    else:
      print('Predicted class idx:'    ,				logits.argmax(-1  ).item()  )
      assert torch.allclose(
          logits[0, :3]    ,				SCREAMING_SNAKE_CASE    ,				atol=1E-3  ), "First elements of logits not as expected"
      assert logits.argmax(-1  ).item() == expected_class_idx, "Predicted class index not as expected"

  Path(SCREAMING_SNAKE_CASE  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE  )
  print(f'''Saving model to {pytorch_dump_folder_path}'''  )
  model.save_pretrained(SCREAMING_SNAKE_CASE  )
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
  _a			:	Dict								=  argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_url',
      default='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth',
      type=str,
      help='URL to the original PyTorch checkpoint (.pth file).',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  _a			:	Union[str, Any]								=  parser.parse_args()
  convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





'''simple docstring'''




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			:	Any								=  False
_a			:	Union[str, Any]								=  Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_a			:	Dict								=  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_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)



class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_=sys.argv[1:]       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		1
    UpperCAmelCase							, UpperCAmelCase  							=		getopt.getopt(a_				,	'i:o:b:s'				,	['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']       )
    for opt, arg in opts:
      if opt in ("-i", "--inputdir"):
        UpperCAmelCase  							=		arg
      elif opt in ("-o", "--outfile"):
        UpperCAmelCase  							=		arg
      elif opt in ("-b", "--batch_size"):
        UpperCAmelCase  							=		int(a_       )
      elif opt in ("-s", "--subset_list"):
        UpperCAmelCase  							=		arg

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

    UpperCAmelCase  							=		CONFIG
    if torch.cuda.is_available():
      UpperCAmelCase  							=		'cuda'
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		Preprocess(self.config       )
    UpperCAmelCase  							=		GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'				,	config=self.config       )
    UpperCAmelCase  							=		batch_size if batch_size != 0 else 1
    UpperCAmelCase  							=		DEFAULT_SCHEMA
  def   snake_case_     (       self				,	a_       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		int(file.split('.'       )[0].split('_'       )[-1]       )
    UpperCAmelCase  							=		os.path.join(self.inputdir				,	a_       )
    return (img_id, filepath)
  @property
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		[]
    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(a_       )       )
      if len(a_       ) == self.batch:
        UpperCAmelCase  							=		batch
        UpperCAmelCase  							=		[]
        yield list(map(a_				,	zip(*a_       )       )       )

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





  def __call__(       self       )							->			str:


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




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int  ):
  try:
    UpperCAmelCase  							=		json.load(SCREAMING_SNAKE_CASE  )
    try:
      UpperCAmelCase  							=		list(data.keys()  )
    except Exception:
      UpperCAmelCase  							=		[d['img_id'] for d in data]
  except Exception:
    try:
      UpperCAmelCase  							=		eval(stream.read()  )
    except Exception:
      UpperCAmelCase  							=		stream.read().split('\n'  )
  return data


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


