
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


__magic_name__  :  Dict     			=   logging.get_logger(__name__)

__magic_name__  :  str     			=   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__magic_name__  :  Tuple     			=   {
    'vocab_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'
        ),
    },
}

__magic_name__  :  Optional[int]     			=   {
    'microsoft/layoutlm-base-uncased': 5_1_2,
    'microsoft/layoutlm-large-uncased': 5_1_2,
}

__magic_name__  :  List[str]     			=   {
    'microsoft/layoutlm-base-uncased': {'do_lower_case': True},
    'microsoft/layoutlm-large-uncased': {'do_lower_case': True},
}


class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  lowerCAmelCase_								=		VOCAB_FILES_NAMES
  lowerCAmelCase_								=		PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_								=		PRETRAINED_INIT_CONFIGURATION
  lowerCAmelCase_								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_								=		LayoutLMTokenizer

  def __init__(					self						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=True						,    __UpperCamelCase="[UNK]"						,    __UpperCamelCase="[SEP]"						,    __UpperCamelCase="[PAD]"						,    __UpperCamelCase="[CLS]"						,    __UpperCamelCase="[MASK]"						,    __UpperCamelCase=True						,    __UpperCamelCase=None						,    **__UpperCamelCase						,    ):
      super().__init__(
          __UpperCamelCase						,    tokenizer_file=__UpperCamelCase						,    do_lower_case=__UpperCamelCase						,    unk_token=__UpperCamelCase						,    sep_token=__UpperCamelCase						,    pad_token=__UpperCamelCase						,    cls_token=__UpperCamelCase						,    mask_token=__UpperCamelCase						,    tokenize_chinese_chars=__UpperCamelCase						,    strip_accents=__UpperCamelCase						,    **__UpperCamelCase						,    )

      A_								=					json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
      if (
          normalizer_state.get("lowercase"						,    __UpperCamelCase					) != do_lower_case
          or normalizer_state.get("strip_accents"						,    __UpperCamelCase					) != strip_accents
          or normalizer_state.get("handle_chinese_chars"						,    __UpperCamelCase					) != tokenize_chinese_chars
      ):
          A_								=					getattr(__UpperCamelCase						,    normalizer_state.pop("type"					)					)
          A_								=					do_lower_case
          A_								=					strip_accents
          A_								=					tokenize_chinese_chars
          A_								=					normalizer_class(**__UpperCamelCase					)

      A_								=					do_lower_case

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase=None					):
      A_								=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

      return output

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None					):
      A_								=					[self.sep_token_id]
      A_								=					[self.cls_token_id]
      if token_ids_a is None:
          return len(cls + token_ids_a + sep					) * [0]
      return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None					):
      A_								=					self._tokenizer.model.save(__UpperCamelCase						,    name=__UpperCamelCase					)
      return tuple(__UpperCamelCase					)








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
__magic_name__  :  Dict     			=   False
__magic_name__  :  Union[str, Any]     			=   Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__magic_name__  :  Optional[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 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase=sys.argv[1:]					):
      A_								=					None
      A_								=					None
      A_								=					None
      A_								=					1
      A_	,	A_								=					getopt.getopt(__UpperCamelCase						,    "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(__UpperCamelCase					)
          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(__UpperCamelCase					), f'{outputfile}'
      if subset_list is not None:
          with open(os.path.realpath(__UpperCamelCase					)					) as f:
              A_								=					{self._vqa_file_split()[0] for x in tryload(__UpperCamelCase					)}
      else:
          A_								=					None

      A_								=					CONFIG
      if torch.cuda.is_available():
          A_								=					"cuda"
      A_								=					os.path.realpath(__UpperCamelCase					)
      A_								=					os.path.realpath(__UpperCamelCase					)
      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 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					int(file.split("."					)[0].split("_"					)[-1]					)
      A_								=					os.path.join(self.inputdir						,    __UpperCamelCase					)
      return (img_id, filepath)

  @property
  def 					lowercase_						(					self					):
      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(__UpperCamelCase					)					)
          if len(__UpperCamelCase					) == self.batch:
              A_								=					batch
              A_								=					[]
              yield list(map(__UpperCamelCase						,    zip(*__UpperCamelCase					)					)					)

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

  def __call__(					self					):
      # 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(__UpperCamelCase					)
          A_								=					self.model(
              __UpperCamelCase						,    __UpperCamelCase						,    scales_yx=__UpperCamelCase						,    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(__UpperCamelCase					)
              A_								=					self.schema.encode_batch(__UpperCamelCase					)
              writer.write_batch(__UpperCamelCase					)
          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        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    try:
        A_								=					json.load(snake_case__ )
        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__":
     __magic_name__  :  List[str]     			=   Extract(sys.argv[1:])
     extract()
     if not TEST:
          __magic_name__  :  List[Any]     			=   datasets.Dataset.from_file(extract.outputfile)
          # wala!
          # print(np.array(dataset[0:2]["roi_features"]).shape)






