

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase							:  Tuple     =				logging.get_logger(__name__)

def      a__	(						)    ->		Dict:
  UpperCAmelCase			:				Optional[int]										=					DetaConfig(
      num_queries=900							,					encoder_ffn_dim=2_048							,					decoder_ffn_dim=2_048							,					num_feature_levels=5							,					assign_first_stage=UpperCAmelCase							,					with_box_refine=UpperCAmelCase							,					two_stage=UpperCAmelCase							,					)

  # set labels
  UpperCAmelCase			:				int										=					91
  UpperCAmelCase			:				List[Any]										=					'''huggingface/label-files'''
  UpperCAmelCase			:				Tuple										=					'''coco-detection-id2label.json'''
  UpperCAmelCase			:				Dict										=					json.load(open(cached_download(hf_hub_url(UpperCAmelCase							,					UpperCAmelCase							,					repo_type='''dataset'''     )     )							,					'''r'''     )     )
  UpperCAmelCase			:				int										=					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}
  UpperCAmelCase			:				Optional[Any]										=					idalabel
  UpperCAmelCase			:				int										=					{v: k for k, v in idalabel.items()}

  return config

def      a__	(						UpperCAmelCase				:     List[str]     )    ->		Tuple:
  UpperCAmelCase			:				List[str]										=					[]

  # stem
  # fmt: off
  rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''')     )
  rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''')     )
  rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''')     )
  rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''')     )
  rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.model.embedder.embedder.normalization.running_var''')     )
  # stages
  for stage_idx in range(len(config.backbone_config.depths     )     ):
    for layer_idx in range(config.backbone_config.depths[stage_idx]     ):
      # shortcut
      if layer_idx == 0:
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
            )     )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
            )     )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
            )     )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
            )     )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
            )     )
      # 3 convs
      for i in range(3     ):
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
            )     )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
            )     )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
            )     )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
            )     )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
            )     )
    # transformer encoder
  for i in range(config.encoder_layers     ):
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')     )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')     )

  # transformer decoder
  for i in range(config.decoder_layers     ):
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')     )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')     )

  # fmt: on

  return rename_keys

def      a__	(						UpperCAmelCase				:     Optional[Any]							,					UpperCAmelCase				:     Any							,					UpperCAmelCase				:     int     )    ->		Optional[int]:
  UpperCAmelCase			:				List[Any]										=					dct.pop(UpperCAmelCase     )
  UpperCAmelCase			:				int										=					val

def      a__	(						UpperCAmelCase				:     List[Any]							,					UpperCAmelCase				:     List[str]     )    ->		Dict:
  # transformer decoder self-attention layers
  UpperCAmelCase			:				Dict										=					config.d_model
  for i in range(config.decoder_layers     ):
    # read in weights + bias of input projection layer of self-attention
    UpperCAmelCase			:				List[Any]										=					state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''     )
    UpperCAmelCase			:				int										=					state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''     )
    # next, add query, keys and values (in that order) to the state dict
    UpperCAmelCase			:				str										=					in_proj_weight[:hidden_size, :]
    UpperCAmelCase			:				Tuple										=					in_proj_bias[:hidden_size]
    UpperCAmelCase			:				Any										=					in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    UpperCAmelCase			:				int										=					in_proj_bias[hidden_size : hidden_size * 2]
    UpperCAmelCase			:				Dict										=					in_proj_weight[-hidden_size:, :]
    UpperCAmelCase			:				Union[str, Any]										=					in_proj_bias[-hidden_size:]

def      a__	(						)    ->		Union[str, Any]:
  UpperCAmelCase			:				Dict										=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  UpperCAmelCase			:				List[str]										=					Image.open(requests.get(UpperCAmelCase							,					stream=UpperCAmelCase     ).raw     )

  return im

@torch.no_grad()
def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     List[Any]							,					UpperCAmelCase				:     str     )    ->		List[Any]:
  UpperCAmelCase			:				Optional[int]										=					get_deta_config()

  # load original state dict
  if model_name == "deta-resnet-50":
    UpperCAmelCase			:				Any										=					'''adet_checkpoint0011.pth'''
  elif model_name == "deta-resnet-50-24-epochs":
    UpperCAmelCase			:				Optional[Any]										=					'''adet_2x_checkpoint0023.pth'''
  else:
    raise ValueError(f'''Model name {model_name} not supported'''     )
  UpperCAmelCase			:				Union[str, Any]										=					hf_hub_download(repo_id='''nielsr/deta-checkpoints'''							,					filename=UpperCAmelCase     )
  UpperCAmelCase			:				List[Any]										=					torch.load(UpperCAmelCase							,					map_location='''cpu'''     )['''model''']

  # rename keys
  UpperCAmelCase			:				List[Any]										=					create_rename_keys(UpperCAmelCase     )
  for src, dest in rename_keys:
    rename_key(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase     )
  read_in_decoder_q_k_v(UpperCAmelCase							,					UpperCAmelCase     )

  # fix some prefixes
  for key in state_dict.copy().keys():
    if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
      UpperCAmelCase			:				Optional[int]										=					state_dict.pop(UpperCAmelCase     )
      UpperCAmelCase			:				List[Any]										=					val
    if "input_proj" in key:
      UpperCAmelCase			:				Optional[int]										=					state_dict.pop(UpperCAmelCase     )
      UpperCAmelCase			:				Any										=					val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      UpperCAmelCase			:				Optional[int]										=					state_dict.pop(UpperCAmelCase     )
      UpperCAmelCase			:				Dict										=					val

    # finally, create HuggingFace model and load state dict
  UpperCAmelCase			:				Any										=					DetaForObjectDetection(UpperCAmelCase     )
  model.load_state_dict(UpperCAmelCase     )
  model.eval()

  UpperCAmelCase			:				List[Any]										=					'''cuda''' if torch.cuda.is_available() else '''cpu'''
  model.to(UpperCAmelCase     )

  # load image processor
  UpperCAmelCase			:				Union[str, Any]										=					DetaImageProcessor(format='''coco_detection'''     )

  # verify our conversion on image
  UpperCAmelCase			:				Optional[int]										=					prepare_img()
  UpperCAmelCase			:				Optional[Any]										=					processor(images=UpperCAmelCase							,					return_tensors='''pt'''     )
  UpperCAmelCase			:				Optional[int]										=					encoding['''pixel_values''']
  UpperCAmelCase			:				List[Any]										=					model(pixel_values.to(UpperCAmelCase     )     )

  # verify logits
  if model_name == "deta-resnet-50":
    UpperCAmelCase			:				str										=					torch.tensor(
        [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]     )
    UpperCAmelCase			:				int										=					torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]     )
  elif model_name == "deta-resnet-50-24-epochs":
    UpperCAmelCase			:				Optional[int]										=					torch.tensor(
        [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]     )
    UpperCAmelCase			:				Union[str, Any]										=					torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]     )

  assert torch.allclose(outputs.logits[0, :3, :3]							,					expected_logits.to(UpperCAmelCase     )							,					atol=1E-4     )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,					expected_boxes.to(UpperCAmelCase     )							,					atol=1E-4     )
  print('''Everything ok!'''     )

  if pytorch_dump_folder_path:
    # Save model and processor
    logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''     )
    Path(UpperCAmelCase     ).mkdir(exist_ok=UpperCAmelCase     )
    model.save_pretrained(UpperCAmelCase     )
    processor.save_pretrained(UpperCAmelCase     )

  # Push to hub
  if push_to_hub:
    print('''Pushing model and processor to hub...'''     )
    model.push_to_hub(f'''jozhang97/{model_name}'''     )
    processor.push_to_hub(f'''jozhang97/{model_name}'''     )


if __name__ == "__main__":
     _lowerCamelCase							:  Any     =				argparse.ArgumentParser()

     parser.add_argument(
         "--model_name",
         type=str,
         default="deta-resnet-50",
         choices=["deta-resnet-50", "deta-resnet-50-24-epochs"],
         help="Name of the model you'd like to convert.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path",
         default=None,
         type=str,
         help="Path to the folder to output PyTorch model.",
     )
     parser.add_argument(
         "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
     )
     _lowerCamelCase							:  Union[str, Any]     =				parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








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
_lowerCamelCase							:  Any     =				False
_lowerCamelCase							:  int     =				Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
_lowerCamelCase							:  Union[str, 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    __UpperCAmelCase	:



   def __init__(				self				:	List[str],						__A				:	int=sys.argv[1:]		):
     UpperCAmelCase			:				List[str]										=					None
     UpperCAmelCase			:				Optional[Any]										=					None
     UpperCAmelCase			:				int										=					None
     UpperCAmelCase			:				Any										=					1
     UpperCAmelCase					,		UpperCAmelCase			:				Optional[int]										=					getopt.getopt(__A,						'''i:o:b:s''',						['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']		)
     for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
         UpperCAmelCase			:				Dict										=					arg
       elif opt in ("-o", "--outfile"):
         UpperCAmelCase			:				Optional[int]										=					arg
       elif opt in ("-b", "--batch_size"):
         UpperCAmelCase			:				Tuple										=					int(__A		)
       elif opt in ("-s", "--subset_list"):
         UpperCAmelCase			:				Optional[Any]										=					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			:				Optional[Any]										=					{self._vqa_file_split()[0] for x in tryload(__A		)}
     else:
       UpperCAmelCase			:				Tuple										=					None

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



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



   @property
   def 			__magic_name__       (				self				:	str		):
     UpperCAmelCase			:				Tuple										=					[]
     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			:				Tuple										=					batch
         UpperCAmelCase			:				str										=					[]
         yield list(map(__A,						zip(*__A		)		)		)

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





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






def      a__	(						UpperCAmelCase				:     List[Any]     )    ->		int:
  try:
    UpperCAmelCase			:				List[str]										=					json.load(UpperCAmelCase     )
    try:
      UpperCAmelCase			:				Optional[Any]										=					list(data.keys()     )
    except Exception:
      UpperCAmelCase			:				Dict										=					[d['''img_id'''] for d in data]
  except Exception:
    try:
      UpperCAmelCase			:				List[Any]										=					eval(stream.read()     )
    except Exception:
      UpperCAmelCase			:				List[Any]										=					stream.read().split('''\n'''     )
  return data


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





