
'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase							:			int				={
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:			Any				=["LayoutLMTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:			Dict				=[
      "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "LayoutLMForMaskedLM",
      "LayoutLMForSequenceClassification",
      "LayoutLMForTokenClassification",
      "LayoutLMForQuestionAnswering",
      "LayoutLMModel",
      "LayoutLMPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:			str				=[
      "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFLayoutLMForMaskedLM",
      "TFLayoutLMForSequenceClassification",
      "TFLayoutLMForTokenClassification",
      "TFLayoutLMForQuestionAnswering",
      "TFLayoutLMMainLayer",
      "TFLayoutLMModel",
      "TFLayoutLMPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
  from .tokenization_layoutlm import LayoutLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )

else:
  import sys

  __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''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
__lowerCAmelCase							:			str				=False
__lowerCAmelCase							:			Union[str, Any]				=Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
__lowerCAmelCase							:			int				=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      UpperCAmelCase			:
 def __init__(       self   :List[str]		, lowercase_   :List[Any]=sys.argv[1:]							)->							Dict:
     A__       =    None
     A__       =    None
     A__       =    None
     A__       =    1
     A__,			A__       =    getopt.getopt(lowercase_		, "i:o:b:s"		, ["inputdir=", "outfile=", "batch_size=", "subset_list="]							)
     for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
             A__       =    arg
         elif opt in ("-o", "--outfile"):
             A__       =    arg
         elif opt in ("-b", "--batch_size"):
             A__       =    int(lowercase_							)
         elif opt in ("-s", "--subset_list"):
             A__       =    arg

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

     A__       =    CONFIG
     if torch.cuda.is_available():
         A__       =    "cuda"
     A__       =    os.path.realpath(lowercase_							)
     A__       =    os.path.realpath(lowercase_							)
     A__       =    Preprocess(self.config							)
     A__       =    GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"		, config=self.config							)
     A__       =    batch_size if batch_size != 0 else 1
     A__       =    DEFAULT_SCHEMA
 def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Optional[Any]							)->							Dict:
     A__       =    int(file.split("."							)[0].split("_"							)[-1]							)
     A__       =    os.path.join(self.inputdir		, lowercase_							)
     return (img_id, filepath)
 @property
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Optional[int]:
     A__       =    []
     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(lowercase_							)							)
         if len(lowercase_							) == self.batch:
             A__       =    batch
             A__       =    []
             yield list(map(lowercase_		, zip(*lowercase_							)							)							)

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



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





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int] ):
    try:
        A__       =    json.load(_lowerCamelCase )
        try:
            A__       =    list(data.keys() )
        except Exception:
            A__       =    [d["img_id"] for d in data]
    except Exception:
        try:
            A__       =    eval(stream.read() )
        except Exception:
            A__       =    stream.read().split("\n" )
    return data


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



