






"""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
UpperCamelCase					=     False
UpperCamelCase					=     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
UpperCamelCase					=     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, 20_48), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)







class 						_a  :





   '''simple docstring'''






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




          '''simple docstring'''




          lowercase__:							List[str]         =     None
          lowercase__:							str         =     None
          lowercase__:							Tuple         =     None
          lowercase__:							str         =     1
          lowercase__    ,      lowercase__:							Optional[Any]         =     getopt.getopt(_SCREAMING_SNAKE_CASE					, "i:o:b:s"					, ["inputdir=", "outfile=", "batch_size=", "subset_list="])
          for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                        lowercase__:							List[str]         =     arg
                 elif opt in ("-o", "--outfile"):
                        lowercase__:							Optional[Any]         =     arg
                 elif opt in ("-b", "--batch_size"):
                        lowercase__:							Any         =     int(_SCREAMING_SNAKE_CASE)
                 elif opt in ("-s", "--subset_list"):
                        lowercase__:							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(_SCREAMING_SNAKE_CASE), F"""{outputfile}"""
          if subset_list is not None:
                 with open(os.path.realpath(_SCREAMING_SNAKE_CASE)) as f:
                        lowercase__:							List[Any]         =     {self._vqa_file_split()[0] for x in tryload(_SCREAMING_SNAKE_CASE)}
          else:
                 lowercase__:							List[str]         =     None

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





   def __lowercase							(       self					, UpperCAmelCase_)			->   Optional[int]:




          '''simple docstring'''




          lowercase__:							str         =     int(file.split(".")[0].split("_")[-1])
          lowercase__:							Any         =     os.path.join(self.inputdir					, _SCREAMING_SNAKE_CASE)
          return (img_id, filepath)





   @property
   def __lowercase							(       self)			->   Any:




          '''simple docstring'''




          lowercase__:							int         =     []
          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(_SCREAMING_SNAKE_CASE))
                 if len(_SCREAMING_SNAKE_CASE) == self.batch:
                        lowercase__:							Tuple         =     batch
                        lowercase__:							Any         =     []
                        yield list(map(_SCREAMING_SNAKE_CASE					, zip(*_SCREAMING_SNAKE_CASE)))

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





   def __call__(       self)			->   Tuple:




          '''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(_SCREAMING_SNAKE_CASE)
                 lowercase__:							Any         =     self.model(
                     _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, scales_yx=_SCREAMING_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[Any]         =     np.array(_SCREAMING_SNAKE_CASE)
                        lowercase__:							Tuple         =     self.schema.encode_batch(_SCREAMING_SNAKE_CASE)
                        writer.write_batch(_SCREAMING_SNAKE_CASE)
                 if TEST:
                        break
                 # finalizer the writer
          if not TEST:
                 lowercase__    ,      lowercase__:							List[Any]         =     writer.finalize()
                 print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb""")







def 							A(    snake_case_						):
       """simple docstring"""





       try:
              lowercase__:							int         =     json.load(UpperCamelCase_						)
              try:
                     lowercase__:							Optional[int]         =     list(data.keys()						)
              except Exception:
                     lowercase__:							List[Any]         =     [d["img_id"] for d in data]
       except Exception:
              try:
                     lowercase__:							List[Any]         =     eval(stream.read()						)
              except Exception:
                     lowercase__:							Optional[int]         =     stream.read().split("\n"						)
       return data


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













from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'mnaylor/mega-base-wikitext': 'https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json',
}



class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       Union[str, Any]       = '''mega'''

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=30522       , _SCREAMING_SNAKE_CASE:			Optional[Any]=128       , _SCREAMING_SNAKE_CASE:			Any=4       , _SCREAMING_SNAKE_CASE:			Tuple=256       , _SCREAMING_SNAKE_CASE:			List[str]=16       , _SCREAMING_SNAKE_CASE:			Dict=True       , _SCREAMING_SNAKE_CASE:			Optional[int]=64       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=False       , _SCREAMING_SNAKE_CASE:			List[Any]=-1       , _SCREAMING_SNAKE_CASE:			Tuple=None       , _SCREAMING_SNAKE_CASE:			Optional[Any]=True       , _SCREAMING_SNAKE_CASE:			Optional[int]="scalenorm"       , _SCREAMING_SNAKE_CASE:			int=True       , _SCREAMING_SNAKE_CASE:			List[str]="silu"       , _SCREAMING_SNAKE_CASE:			int="softmax"       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[int]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Tuple=False       , _SCREAMING_SNAKE_CASE:			List[Any]=True       , _SCREAMING_SNAKE_CASE:			Tuple=256       , _SCREAMING_SNAKE_CASE:			Any=True       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2048       , _SCREAMING_SNAKE_CASE:			Any=False       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2       , _SCREAMING_SNAKE_CASE:			List[str]=0.02       , _SCREAMING_SNAKE_CASE:			Tuple=0.2       , _SCREAMING_SNAKE_CASE:			Optional[int]=0.02       , _SCREAMING_SNAKE_CASE:			Tuple=1.0       , _SCREAMING_SNAKE_CASE:			List[str]=1       , _SCREAMING_SNAKE_CASE:			List[Any]=0       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2       , _SCREAMING_SNAKE_CASE:			Optional[int]="rotary"       , _SCREAMING_SNAKE_CASE:			Optional[Any]=None       , _SCREAMING_SNAKE_CASE:			Any=True       , _SCREAMING_SNAKE_CASE:			Optional[Any]=True       , **_SCREAMING_SNAKE_CASE:			Union[str, Any]       , )							->			Optional[Any]:




              """simple docstring"""


              super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       , bos_token_id=_SCREAMING_SNAKE_CASE       , eos_token_id=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	vocab_size
              UpperCamelCase_ 							=	hidden_size
              UpperCamelCase_ 							=	num_hidden_layers
              UpperCamelCase_ 							=	activation
              UpperCamelCase_ 							=	attention_activation
              UpperCamelCase_ 							=	intermediate_size
              UpperCamelCase_ 							=	ema_projection_size
              UpperCamelCase_ 							=	bidirectional
              UpperCamelCase_ 							=	shared_representation_size
              UpperCamelCase_ 							=	use_chunking
              UpperCamelCase_ 							=	chunk_size
              UpperCamelCase_ 							=	truncation
              UpperCamelCase_ 							=	normalize_before_mega
              UpperCamelCase_ 							=	normalization_type
              UpperCamelCase_ 							=	norm_affine
              UpperCamelCase_ 							=	dropout_prob
              UpperCamelCase_ 							=	hidden_dropout_prob
              UpperCamelCase_ 							=	attention_probs_dropout_prob
              UpperCamelCase_ 							=	use_feature_dropout
              UpperCamelCase_ 							=	use_normalized_ffn
              UpperCamelCase_ 							=	nffn_hidden_size
              UpperCamelCase_ 							=	normalize_before_ffn
              UpperCamelCase_ 							=	nffn_activation_dropout_prob
              UpperCamelCase_ 							=	max_positions
              UpperCamelCase_ 							=	add_token_type_embeddings
              UpperCamelCase_ 							=	type_vocab_size
              UpperCamelCase_ 							=	initializer_range
              UpperCamelCase_ 							=	ema_delta_alpha_range
              UpperCamelCase_ 							=	ema_beta_range
              UpperCamelCase_ 							=	ema_gamma_omega_range
              UpperCamelCase_ 							=	relative_positional_bias
              UpperCamelCase_ 							=	use_cache
              UpperCamelCase_ 							=	classifier_dropout
              UpperCamelCase_ 							=	add_lm_hidden_dense_layer
              UpperCamelCase_ 							=	1  # not used but required by Hugging Face



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       @property
       def lowercase			(		self:			Optional[int]		)							->			Mapping[str, Mapping[int, str]]:




              """simple docstring"""


              if self.task == "multiple-choice":
                     UpperCamelCase_ 							=	{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     UpperCamelCase_ 							=	{0: "batch", 1: "sequence"}
              return OrderedDict(
                  [
                      ("input_ids", dynamic_axis),
                      ("attention_mask", dynamic_axis),
                  ]		)

