





"""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:	List[Any]							      =False
lowerCAmelCase:	Optional[Any]							      =Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
lowerCAmelCase:	Dict							      =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 lowerCamelCase__  :



   def __init__(       self      ,   snake_case=sys.argv[1:]	) ->    Union[str, Any]:







      """simple docstring"""
      lowercase    :			Dict  =       None
      lowercase    :			str  =       None
      lowercase    :			int  =       None
      lowercase    :			Optional[Any]  =       1
      lowercase  ,					lowercase    :			List[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[str]  =       arg
         elif opt in ("-o", "--outfile"):
            lowercase    :			Optional[int]  =       arg
         elif opt in ("-b", "--batch_size"):
            lowercase    :			Tuple  =       int(snake_case	)
         elif opt in ("-s", "--subset_list"):
            lowercase    :			Dict  =       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[int]  =       None

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



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    str:







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



   @property
   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      lowercase    :			Optional[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(snake_case	)	)
         if len(snake_case	) == self.batch:
            lowercase    :			Optional[int]  =       batch
            lowercase    :			Union[str, Any]  =       []
            yield list(map(snake_case      ,   zip(*snake_case	)	)	)

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





   def __call__(       self	) ->    List[Any]:







      """simple docstring"""
      # make writer
      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    :			Any  =       self.preprocess(snake_case	)
         lowercase    :			Optional[int]  =       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    :			str  =       output_dict.pop("""normalized_boxes"""	)
         if not TEST:
            lowercase    :			Optional[Any]  =       np.array(snake_case	)
            lowercase    :			List[Any]  =       self.schema.encode_batch(snake_case	)
            writer.write_batch(snake_case	)
         if TEST:
            break
         # finalizer the writer
      if not TEST:
         lowercase  ,					lowercase    :			Union[str, Any]  =       writer.finalize()
         print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''	)




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


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









"""simple docstring"""
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""MarkupLMFeatureExtractor"""
   __UpperCAmelCase        =					("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
   __UpperCAmelCase        =					True



   def __call__(       self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case = True      ,   snake_case = False      ,   snake_case = None      ,   snake_case = None      ,   snake_case = 0      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = True      ,   snake_case = None      ,   **snake_case      ,   ) ->    BatchEncoding:







      """simple docstring"""
      # first, create nodes and xpaths
      if self.parse_html:
         if html_strings is None:
            raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""	)

         if nodes is not None or xpaths is not None or node_labels is not None:
            raise ValueError(
                """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""	)

         lowercase    :			List[Any]  =       self.feature_extractor(snake_case	)
         lowercase    :			Union[str, Any]  =       features["""nodes"""]
         lowercase    :			List[Any]  =       features["""xpaths"""]
      else:
         if html_strings is not None:
            raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""	)
         if nodes is None or xpaths is None:
            raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""	)

        # # second, apply the tokenizer
      if questions is not None and self.parse_html:
         if isinstance(snake_case      ,   snake_case	):
            lowercase    :			Dict  =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

      lowercase    :			Dict  =       self.tokenizer(
          text=questions if questions is not None else nodes      ,   text_pair=nodes if questions is not None else None      ,   xpaths=snake_case      ,   node_labels=snake_case      ,   add_special_tokens=snake_case      ,   padding=snake_case      ,   truncation=snake_case      ,   max_length=snake_case      ,   stride=snake_case      ,   pad_to_multiple_of=snake_case      ,   return_token_type_ids=snake_case      ,   return_attention_mask=snake_case      ,   return_overflowing_tokens=snake_case      ,   return_special_tokens_mask=snake_case      ,   return_offsets_mapping=snake_case      ,   return_length=snake_case      ,   verbose=snake_case      ,   return_tensors=snake_case      ,   **snake_case      ,   )

      return encoded_inputs



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Optional[Any]:







      """simple docstring"""
      return self.tokenizer.batch_decode(*snake_case      ,   **snake_case	)



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Tuple:







      """simple docstring"""
      return self.tokenizer.decode(*snake_case      ,   **snake_case	)





   @property
   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      lowercase    :			Any  =       self.tokenizer.model_input_names
      return tokenizer_input_names


