


from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :torch.Tensor      ,						lowercase__  :torch.Tensor      ,						lowercase__  :bool				)       ->  torch.Tensor:
						# The first operation in a checkpoint can't be in-place, but it's
						# nice to have in-place addition during inference. Thus...
						if not inplace:
												__magic_name__				:							Optional[Any]				=      ma + ma
						else:
												ma += ma

						return ma
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :torch.Tensor      ,						lowercase__  :List[int]				)       ->  torch.Tensor:
						__magic_name__				:							Any				=      -1 * len(lowercase__				)
						__magic_name__				:							Dict				=      list(range(len(tensor.shape[:zero_index]				)				)				)
						return tensor.permute(first_inds + [zero_index + i for i in inds]				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :torch.Tensor      ,						lowercase__  :int				)       ->  torch.Tensor:
						return t.reshape(t.shape[:-no_dims] + (-1,)				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :torch.Tensor      ,						lowercase__  :torch.Tensor      ,						lowercase__  :int      ,						lowercase__  :float = 1e-4				)       ->  torch.Tensor:
						__magic_name__				:							Dict				=      mask.expand(*value.shape				)
						return torch.sum(mask * value      ,						dim=lowercase__				) / (eps + torch.sum(lowercase__      ,						dim=lowercase__				))
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :torch.Tensor      ,						lowercase__  :torch.types.Number = 2.3_1_2_5      ,						lowercase__  :torch.types.Number = 2_1.6_8_7_5      ,						lowercase__  :int = 6_4				)       ->  torch.Tensor:
						__magic_name__				:							Any				=      torch.linspace(lowercase__      ,						lowercase__      ,						no_bins - 1      ,						device=pts.device				)
						__magic_name__				:							Union[str, Any]				=      torch.sqrt(torch.sum((pts.unsqueeze(-2				) - pts.unsqueeze(-3				)) ** 2      ,						dim=-1				)				)
						return torch.bucketize(lowercase__      ,						lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Callable[[list], Any]      ,						lowercase__  :List[dict]				)       ->  dict:
						__magic_name__				:							Optional[int]				=      dicts[0]
						__magic_name__				:							Tuple				=      {}
						for k, v in first.items():
												__magic_name__				:							List[Any]				=      [d[k] for d in dicts]
												if isinstance(lowercase__      ,						lowercase__				):
																		__magic_name__				:							List[str]				=      dict_multimap(lowercase__      ,						lowercase__				)
												else:
																		__magic_name__				:							List[str]				=      fn(lowercase__				)

						return new_dict
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :torch.Tensor      ,						lowercase__  :torch.Tensor				)       ->  torch.Tensor:
						__magic_name__				:							List[Any]				=      v_bins.view(((1,) * len(x.shape				)) + (len(lowercase__				),)				)
						__magic_name__				:							str				=      x[..., None] - reshaped_bins
						__magic_name__				:							Dict				=      torch.argmin(torch.abs(lowercase__				)      ,						dim=-1				)
						return nn.functional.one_hot(lowercase__      ,						num_classes=len(lowercase__				)				).float()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :torch.Tensor      ,						lowercase__  :torch.Tensor      ,						lowercase__  :int = 0      ,						lowercase__  :int = 0				)       ->  torch.Tensor:
						__magic_name__				:							List[Union[slice, torch.Tensor]]				=      []
						for i, s in enumerate(data.shape[:no_batch_dims]				):
												__magic_name__				:							Tuple				=      torch.arange(lowercase__				)
												__magic_name__				:							int				=      r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape				) - i - 1)))				)
												ranges.append(lowercase__				)

						__magic_name__				:							List[Union[slice, torch.Tensor]]				=      [slice(lowercase__				) for _ in range(len(data.shape				) - no_batch_dims				)]
						__magic_name__				:							Union[str, Any]				=      inds
						ranges.extend(lowercase__				)
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(lowercase__				)]


snake_case__     							= TypeVar('T')
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Callable[[T], Any]      ,						lowercase__  :Dict[Any, Union[dict, list, tuple, T]]      ,						lowercase__  :Type[T]				)       ->  Dict[Any, Union[dict, list, tuple, Any]]:
						__magic_name__				:							Dict[Any, Union[dict, list, tuple, Any]]				=      {}
						for k, v in dic.items():
												if isinstance(lowercase__      ,						lowercase__				):
																		__magic_name__				:							str				=      dict_map(lowercase__      ,						lowercase__      ,						lowercase__				)
												else:
																		__magic_name__				:							Dict				=      tree_map(lowercase__      ,						lowercase__      ,						lowercase__				)

						return new_dict
@overload
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Callable[[T], Any]      ,						lowercase__  :T      ,						lowercase__  :Type[T]				)       ->  Any:
						...
@overload
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Callable[[T], Any]      ,						lowercase__  :dict      ,						lowercase__  :Type[T]				)       ->  dict:
						...
@overload
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Callable[[T], Any]      ,						lowercase__  :list      ,						lowercase__  :Type[T]				)       ->  list:
						...
@overload
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Callable[[T], Any]      ,						lowercase__  :tuple      ,						lowercase__  :Type[T]				)       ->  tuple:
						...
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]      ,						lowercase__  :Tuple      ,						lowercase__  :str				)       ->  List[str]:
						if isinstance(lowercase__      ,						lowercase__				):
												return dict_map(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						lowercase__				):
												return [tree_map(lowercase__      ,						lowercase__      ,						lowercase__				) for x in tree]
						elif isinstance(lowercase__      ,						lowercase__				):
												return tuple(tree_map(lowercase__      ,						lowercase__      ,						lowercase__				) for x in tree				)
						elif isinstance(lowercase__      ,						lowercase__				):
												return fn(lowercase__				)
						else:
												print(type(lowercase__				)				)
												raise ValueError('Not supported'				)

snake_case__     							= partial(tree_map, leaf_type=torch.Tensor)






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
snake_case__     							= False
snake_case__     							= Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
snake_case__     							= 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     ,      _UpperCAmelCase=sys.argv[1:]      ):




										"""simple docstring"""




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

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




										"""simple docstring"""




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




										"""simple docstring"""




										__magic_name__				:							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(_UpperCAmelCase      )      )
																if len(_UpperCAmelCase      ) == self.batch:
																						__magic_name__				:							int				=      batch
																						__magic_name__				:							Union[str, Any]				=      []
																						yield list(map(_UpperCAmelCase     ,      zip(*_UpperCAmelCase      )      )      )

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





				def __call__(  self      ):




										"""simple docstring"""




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






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple				)       ->  Union[str, Any]:
						try:
												__magic_name__				:							List[str]				=      json.load(lowercase__				)
												try:
																		__magic_name__				:							Dict				=      list(data.keys()				)
												except Exception:
																		__magic_name__				:							int				=      [d['img_id'] for d in data]
						except Exception:
												try:
																		__magic_name__				:							str				=      eval(stream.read()				)
												except Exception:
																		__magic_name__				:							Any				=      stream.read().split('\n'				)
						return data


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


