
import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=1       )				->					Tuple:







       if n_shave_prefix_segments >= 0:
              return ".".join(path.split("""."""       )[n_shave_prefix_segments:]       )
       else:
              return ".".join(path.split("""."""       )[:n_shave_prefix_segments]       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=0       )				->					List[str]:
       lowercase							:  Optional[int] 			=   []
       for old_item in old_list:
              lowercase							:  Optional[int] 			=   old_item
              lowercase							:  Optional[int] 			=   new_item.replace("""block."""      ,			"""resnets."""       )
              lowercase							:  Optional[int] 			=   new_item.replace("""conv_shorcut"""      ,			"""conv1"""       )
              lowercase							:  List[Any] 			=   new_item.replace("""in_shortcut"""      ,			"""conv_shortcut"""       )
              lowercase							:  str 			=   new_item.replace("""temb_proj"""      ,			"""time_emb_proj"""       )

              lowercase							:  Any 			=   shave_segments(_A      ,			n_shave_prefix_segments=_A       )

              mapping.append({"""old""": old_item, """new""": new_item}       )

       return mapping







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=0      ,			SCREAMING_SNAKE_CASE__=False       )				->					Dict:
       lowercase							:  Dict 			=   []
       for old_item in old_list:
              lowercase							:  Any 			=   old_item

              # In `model.mid`, the layer is called `attn`.
              if not in_mid:
                     lowercase							:  Union[str, Any] 			=   new_item.replace("""attn"""      ,			"""attentions"""       )
              lowercase							:  int 			=   new_item.replace(""".k."""      ,			""".key."""       )
              lowercase							:  Any 			=   new_item.replace(""".v."""      ,			""".value."""       )
              lowercase							:  Dict 			=   new_item.replace(""".q."""      ,			""".query."""       )

              lowercase							:  List[str] 			=   new_item.replace("""proj_out"""      ,			"""proj_attn"""       )
              lowercase							:  str 			=   new_item.replace("""norm"""      ,			"""group_norm"""       )

              lowercase							:  str 			=   shave_segments(_A      ,			n_shave_prefix_segments=_A       )
              mapping.append({"""old""": old_item, """new""": new_item}       )

       return mapping







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=None      ,			SCREAMING_SNAKE_CASE__=None      ,			SCREAMING_SNAKE_CASE__=None       )				->					Optional[int]:
       assert isinstance(_A      ,			_A       ), "Paths should be a list of dicts containing 'old' and 'new' keys."

       if attention_paths_to_split is not None:
              if config is None:
                     raise ValueError("""Please specify the config if setting \'attention_paths_to_split\' to \'True\'."""       )

              for path, path_map in attention_paths_to_split.items():
                     lowercase							:  Any 			=   old_checkpoint[path]
                     lowercase							:  Optional[int] 			=   old_tensor.shape[0] // 3

                     lowercase							:  str 			=   (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

                     lowercase							:  Optional[int] 			=   old_tensor.shape[0] // config.get("""num_head_channels"""      ,			1       ) // 3

                     lowercase							:  Tuple 			=   old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]       )
                     lowercase			,    lowercase			,    lowercase							:  str 			=   old_tensor.split(channels // num_heads      ,			dim=1       )

                     lowercase							:  int 			=   query.reshape(_A       ).squeeze()
                     lowercase							:  str 			=   key.reshape(_A       ).squeeze()
                     lowercase							:  Dict 			=   value.reshape(_A       ).squeeze()

       for path in paths:
              lowercase							:  Union[str, Any] 			=   path["""new"""]

              if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                     continue

              lowercase							:  Any 			=   new_path.replace("""down."""      ,			"""down_blocks."""       )
              lowercase							:  Union[str, Any] 			=   new_path.replace("""up."""      ,			"""up_blocks."""       )

              if additional_replacements is not None:
                     for replacement in additional_replacements:
                            lowercase							:  List[Any] 			=   new_path.replace(replacement["""old"""]      ,			replacement["""new"""]       )

              if "attentions" in new_path:
                     lowercase							:  Tuple 			=   old_checkpoint[path["""old"""]].squeeze()
              else:
                     lowercase							:  Dict 			=   old_checkpoint[path["""old"""]]







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					List[str]:
       lowercase							:  Dict 			=   {}

       lowercase							:  List[str] 			=   checkpoint["""temb.dense.0.weight"""]
       lowercase							:  Any 			=   checkpoint["""temb.dense.0.bias"""]
       lowercase							:  Tuple 			=   checkpoint["""temb.dense.1.weight"""]
       lowercase							:  Optional[int] 			=   checkpoint["""temb.dense.1.bias"""]

       lowercase							:  Dict 			=   checkpoint["""norm_out.weight"""]
       lowercase							:  int 			=   checkpoint["""norm_out.bias"""]

       lowercase							:  List[Any] 			=   checkpoint["""conv_in.weight"""]
       lowercase							:  Dict 			=   checkpoint["""conv_in.bias"""]
       lowercase							:  List[str] 			=   checkpoint["""conv_out.weight"""]
       lowercase							:  Tuple 			=   checkpoint["""conv_out.bias"""]

       lowercase							:  str 			=   len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """down""" in layer}       )
       lowercase							:  Tuple 			=   {
           layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(_A       )
       }

       lowercase							:  Any 			=   len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """up""" in layer}       )
       lowercase							:  str 			=   {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(_A       )}

       for i in range(_A       ):
              lowercase							:  List[Any] 			=   (i - 1) // (config["""layers_per_block"""] + 1)

              if any("""downsample""" in layer for layer in down_blocks[i]       ):
                     lowercase							:  Any 			=   checkpoint[
                         f"down.{i}.downsample.op.weight"
                     ]
                     lowercase							:  Union[str, Any] 			=   checkpoint[f"down.{i}.downsample.op.bias"]
              #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
              #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

              if any("""block""" in layer for layer in down_blocks[i]       ):
                     lowercase							:  Tuple 			=   len(
                         {""".""".join(shave_segments(_A      ,			2       ).split("""."""       )[:2]       ) for layer in down_blocks[i] if """block""" in layer}       )
                     lowercase							:  List[str] 			=   {
                         layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                         for layer_id in range(_A       )
                     }

                     if num_blocks > 0:
                            for j in range(config["""layers_per_block"""]       ):
                                   lowercase							:  Dict 			=   renew_resnet_paths(blocks[j]       )
                                   assign_to_checkpoint(_A      ,			_A      ,			_A       )

              if any("""attn""" in layer for layer in down_blocks[i]       ):
                     lowercase							:  Any 			=   len(
                         {""".""".join(shave_segments(_A      ,			2       ).split("""."""       )[:2]       ) for layer in down_blocks[i] if """attn""" in layer}       )
                     lowercase							:  List[Any] 			=   {
                         layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                         for layer_id in range(_A       )
                     }

                     if num_attn > 0:
                            for j in range(config["""layers_per_block"""]       ):
                                   lowercase							:  str 			=   renew_attention_paths(attns[j]       )
                                   assign_to_checkpoint(_A      ,			_A      ,			_A      ,			config=_A       )

       lowercase							:  List[Any] 			=   [key for key in checkpoint if """mid.block_1""" in key]
       lowercase							:  Optional[int] 			=   [key for key in checkpoint if """mid.block_2""" in key]
       lowercase							:  Optional[int] 			=   [key for key in checkpoint if """mid.attn_1""" in key]

       # Mid new 2
       lowercase							:  List[str] 			=   renew_resnet_paths(_A       )
       assign_to_checkpoint(
           _A      ,			_A      ,			_A      ,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]      ,			)

       lowercase							:  List[Any] 			=   renew_resnet_paths(_A       )
       assign_to_checkpoint(
           _A      ,			_A      ,			_A      ,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]      ,			)

       lowercase							:  List[str] 			=   renew_attention_paths(_A      ,			in_mid=_A       )
       assign_to_checkpoint(
           _A      ,			_A      ,			_A      ,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]      ,			)

       for i in range(_A       ):
              lowercase							:  Tuple 			=   num_up_blocks - 1 - i

              if any("""upsample""" in layer for layer in up_blocks[i]       ):
                     lowercase							:  Tuple 			=   checkpoint[
                         f"up.{i}.upsample.conv.weight"
                     ]
                     lowercase							:  Tuple 			=   checkpoint[f"up.{i}.upsample.conv.bias"]

              if any("""block""" in layer for layer in up_blocks[i]       ):
                     lowercase							:  List[str] 			=   len(
                         {""".""".join(shave_segments(_A      ,			2       ).split("""."""       )[:2]       ) for layer in up_blocks[i] if """block""" in layer}       )
                     lowercase							:  Dict 			=   {
                         layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(_A       )
                     }

                     if num_blocks > 0:
                            for j in range(config["""layers_per_block"""] + 1       ):
                                   lowercase							:  Any 			=   {"""old""": f"up_blocks.{i}", """new""": f"up_blocks.{block_id}"}
                                   lowercase							:  Optional[Any] 			=   renew_resnet_paths(blocks[j]       )
                                   assign_to_checkpoint(_A      ,			_A      ,			_A      ,			additional_replacements=[replace_indices]       )

              if any("""attn""" in layer for layer in up_blocks[i]       ):
                     lowercase							:  Dict 			=   len(
                         {""".""".join(shave_segments(_A      ,			2       ).split("""."""       )[:2]       ) for layer in up_blocks[i] if """attn""" in layer}       )
                     lowercase							:  Optional[Any] 			=   {
                         layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(_A       )
                     }

                     if num_attn > 0:
                            for j in range(config["""layers_per_block"""] + 1       ):
                                   lowercase							:  Optional[Any] 			=   {"""old""": f"up_blocks.{i}", """new""": f"up_blocks.{block_id}"}
                                   lowercase							:  Optional[int] 			=   renew_attention_paths(attns[j]       )
                                   assign_to_checkpoint(_A      ,			_A      ,			_A      ,			additional_replacements=[replace_indices]       )

       lowercase							:  Optional[Any] 			=   {k.replace("""mid_new_2"""      ,			"""mid_block"""       ): v for k, v in new_checkpoint.items()}
       return new_checkpoint







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Tuple:
       lowercase							:  List[str] 			=   {}

       lowercase							:  List[Any] 			=   checkpoint["""encoder.norm_out.weight"""]
       lowercase							:  Dict 			=   checkpoint["""encoder.norm_out.bias"""]

       lowercase							:  Optional[Any] 			=   checkpoint["""encoder.conv_in.weight"""]
       lowercase							:  str 			=   checkpoint["""encoder.conv_in.bias"""]
       lowercase							:  Union[str, Any] 			=   checkpoint["""encoder.conv_out.weight"""]
       lowercase							:  Any 			=   checkpoint["""encoder.conv_out.bias"""]

       lowercase							:  List[Any] 			=   checkpoint["""decoder.norm_out.weight"""]
       lowercase							:  int 			=   checkpoint["""decoder.norm_out.bias"""]

       lowercase							:  Dict 			=   checkpoint["""decoder.conv_in.weight"""]
       lowercase							:  str 			=   checkpoint["""decoder.conv_in.bias"""]
       lowercase							:  List[str] 			=   checkpoint["""decoder.conv_out.weight"""]
       lowercase							:  Tuple 			=   checkpoint["""decoder.conv_out.bias"""]

       lowercase							:  Tuple 			=   len({""".""".join(layer.split("""."""       )[:3]       ) for layer in checkpoint if """down""" in layer}       )
       lowercase							:  str 			=   {
           layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(_A       )
       }

       lowercase							:  Optional[Any] 			=   len({""".""".join(layer.split("""."""       )[:3]       ) for layer in checkpoint if """up""" in layer}       )
       lowercase							:  List[Any] 			=   {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(_A       )}

       for i in range(_A       ):
              lowercase							:  Tuple 			=   (i - 1) // (config["""layers_per_block"""] + 1)

              if any("""downsample""" in layer for layer in down_blocks[i]       ):
                     lowercase							:  Union[str, Any] 			=   checkpoint[
                         f"encoder.down.{i}.downsample.conv.weight"
                     ]
                     lowercase							:  List[str] 			=   checkpoint[
                         f"encoder.down.{i}.downsample.conv.bias"
                     ]

              if any("""block""" in layer for layer in down_blocks[i]       ):
                     lowercase							:  Dict 			=   len(
                         {""".""".join(shave_segments(_A      ,			3       ).split("""."""       )[:3]       ) for layer in down_blocks[i] if """block""" in layer}       )
                     lowercase							:  List[str] 			=   {
                         layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                         for layer_id in range(_A       )
                     }

                     if num_blocks > 0:
                            for j in range(config["""layers_per_block"""]       ):
                                   lowercase							:  Optional[int] 			=   renew_resnet_paths(blocks[j]       )
                                   assign_to_checkpoint(_A      ,			_A      ,			_A       )

              if any("""attn""" in layer for layer in down_blocks[i]       ):
                     lowercase							:  Tuple 			=   len(
                         {""".""".join(shave_segments(_A      ,			3       ).split("""."""       )[:3]       ) for layer in down_blocks[i] if """attn""" in layer}       )
                     lowercase							:  Union[str, Any] 			=   {
                         layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                         for layer_id in range(_A       )
                     }

                     if num_attn > 0:
                            for j in range(config["""layers_per_block"""]       ):
                                   lowercase							:  List[str] 			=   renew_attention_paths(attns[j]       )
                                   assign_to_checkpoint(_A      ,			_A      ,			_A      ,			config=_A       )

       lowercase							:  str 			=   [key for key in checkpoint if """mid.block_1""" in key]
       lowercase							:  str 			=   [key for key in checkpoint if """mid.block_2""" in key]
       lowercase							:  Any 			=   [key for key in checkpoint if """mid.attn_1""" in key]

       # Mid new 2
       lowercase							:  int 			=   renew_resnet_paths(_A       )
       assign_to_checkpoint(
           _A      ,			_A      ,			_A      ,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]      ,			)

       lowercase							:  Optional[int] 			=   renew_resnet_paths(_A       )
       assign_to_checkpoint(
           _A      ,			_A      ,			_A      ,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]      ,			)

       lowercase							:  List[str] 			=   renew_attention_paths(_A      ,			in_mid=_A       )
       assign_to_checkpoint(
           _A      ,			_A      ,			_A      ,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]      ,			)

       for i in range(_A       ):
              lowercase							:  Union[str, Any] 			=   num_up_blocks - 1 - i

              if any("""upsample""" in layer for layer in up_blocks[i]       ):
                     lowercase							:  List[Any] 			=   checkpoint[
                         f"decoder.up.{i}.upsample.conv.weight"
                     ]
                     lowercase							:  List[Any] 			=   checkpoint[
                         f"decoder.up.{i}.upsample.conv.bias"
                     ]

              if any("""block""" in layer for layer in up_blocks[i]       ):
                     lowercase							:  int 			=   len(
                         {""".""".join(shave_segments(_A      ,			3       ).split("""."""       )[:3]       ) for layer in up_blocks[i] if """block""" in layer}       )
                     lowercase							:  int 			=   {
                         layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(_A       )
                     }

                     if num_blocks > 0:
                            for j in range(config["""layers_per_block"""] + 1       ):
                                   lowercase							:  str 			=   {"""old""": f"up_blocks.{i}", """new""": f"up_blocks.{block_id}"}
                                   lowercase							:  Any 			=   renew_resnet_paths(blocks[j]       )
                                   assign_to_checkpoint(_A      ,			_A      ,			_A      ,			additional_replacements=[replace_indices]       )

              if any("""attn""" in layer for layer in up_blocks[i]       ):
                     lowercase							:  Tuple 			=   len(
                         {""".""".join(shave_segments(_A      ,			3       ).split("""."""       )[:3]       ) for layer in up_blocks[i] if """attn""" in layer}       )
                     lowercase							:  List[str] 			=   {
                         layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(_A       )
                     }

                     if num_attn > 0:
                            for j in range(config["""layers_per_block"""] + 1       ):
                                   lowercase							:  Dict 			=   {"""old""": f"up_blocks.{i}", """new""": f"up_blocks.{block_id}"}
                                   lowercase							:  List[Any] 			=   renew_attention_paths(attns[j]       )
                                   assign_to_checkpoint(_A      ,			_A      ,			_A      ,			additional_replacements=[replace_indices]       )

       lowercase							:  Dict 			=   {k.replace("""mid_new_2"""      ,			"""mid_block"""       ): v for k, v in new_checkpoint.items()}
       lowercase							:  Any 			=   checkpoint["""quant_conv.weight"""]
       lowercase							:  Tuple 			=   checkpoint["""quant_conv.bias"""]
       if "quantize.embedding.weight" in checkpoint:
              lowercase							:  List[str] 			=   checkpoint["""quantize.embedding.weight"""]
       lowercase							:  Dict 			=   checkpoint["""post_quant_conv.weight"""]
       lowercase							:  Tuple 			=   checkpoint["""post_quant_conv.bias"""]

       return new_checkpoint


if __name__ == "__main__":
  lowercase						:      List[str]							=							argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
  )

  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the architecture.""",
  )

  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

  lowercase						:      Dict							=							parser.parse_args()
  lowercase						:      Any							=							torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    lowercase						:      str							=							json.loads(f.read())

  # unet case
  lowercase						:      Tuple							=							{key.split(""".""")[0] for key in checkpoint.keys()}
  if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    lowercase						:      Any							=							convert_vq_autoenc_checkpoint(checkpoint, config)
  else:
    lowercase						:      int							=							convert_ddpm_checkpoint(checkpoint, config)

  if "ddpm" in config:
    del config["ddpm"]

  if config["_class_name"] == "VQModel":
    lowercase						:      List[str]							=							VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  elif config["_class_name"] == "AutoencoderKL":
    lowercase						:      Any							=							AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  else:
    lowercase						:      Union[str, Any]							=							UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    lowercase						:      List[Any]							=							DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

    lowercase						:      str							=							DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)


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
lowercase						:      List[Any]							=							False
lowercase						:      Union[str, Any]							=							Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowercase						:      List[str]							=							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, 2048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)

class __snake_case	:
    def __init__(						self	,snake_case=sys.argv[1:]   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   None
           lowercase							:  Optional[Any] 			=   None
           lowercase							:  Tuple 			=   None
           lowercase							:  Optional[int] 			=   1
           lowercase			,    lowercase							:  str 			=   getopt.getopt(snake_case	,"""i:o:b:s"""	,["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]   )
           for opt, arg in opts:
                  if opt in ("-i", "--inputdir"):
                         lowercase							:  List[Any] 			=   arg
                  elif opt in ("-o", "--outfile"):
                         lowercase							:  Any 			=   arg
                  elif opt in ("-b", "--batch_size"):
                         lowercase							:  Any 			=   int(snake_case   )
                  elif opt in ("-s", "--subset_list"):
                         lowercase							:  str 			=   arg

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

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



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   int(file.split("""."""   )[0].split("""_"""   )[-1]   )
           lowercase							:  List[str] 			=   os.path.join(self.inputdir	,snake_case   )
           return (img_id, filepath)
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   []
           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(snake_case   )   )
                  if len(snake_case   ) == self.batch:
                         lowercase							:  Dict 			=   batch
                         lowercase							:  int 			=   []
                         yield list(map(snake_case	,zip(*snake_case   )   )   )

           for i in range(1   ):
                  yield list(map(snake_case	,zip(*snake_case   )   )   )
    def __call__(						self   ):



           '''simple docstring'''







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







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       try:
              lowercase							:  Dict 			=   json.load(SCREAMING_SNAKE_CASE__       )
              try:
                     lowercase							:  Tuple 			=   list(data.keys()       )
              except Exception:
                     lowercase							:  Tuple 			=   [d["""img_id"""] for d in data]
       except Exception:
              try:
                     lowercase							:  Optional[Any] 			=   eval(stream.read()       )
              except Exception:
                     lowercase							:  Any 			=   stream.read().split("""\n"""       )
       return data


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