



"""simple docstring"""






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				['image_processor', 'tokenizer']
					_a       =				'LayoutLMv2ImageProcessor'
					_a       =				('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



					def __init__(			self				:    Optional[Any],    lowerCamelCase				:    Any=None,    lowerCamelCase				:    int=None,    **lowerCamelCase				:    Any	)->   Optional[int]:
								lowerCamelCase__						:						List[str]						      =None
								if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.''',    lowerCamelCase,    )
											lowerCamelCase__						:						Dict						      =kwargs.pop('''feature_extractor'''	)

								lowerCamelCase__						:						Tuple						      =image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''	)
								if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''	)

								super().__init__(lowerCamelCase,    lowerCamelCase	)



					def __call__(			self				:    Union[str, Any],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,    lowerCamelCase				:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,    lowerCamelCase				:    Union[List[List[int]], List[List[List[int]]]] = None,    lowerCamelCase				:    Optional[Union[List[int], List[List[int]]]] = None,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    Union[bool, str, PaddingStrategy] = False,    lowerCamelCase				:    Union[bool, str, TruncationStrategy] = False,    lowerCamelCase				:    Optional[int] = None,    lowerCamelCase				:    int = 0,    lowerCamelCase				:    Optional[int] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    Optional[Union[str, TensorType]] = None,    **lowerCamelCase				:    Optional[Any],    )->   BatchEncoding:
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
											raise ValueError(
											    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''	)

								if self.image_processor.apply_ocr and (word_labels is not None):
											raise ValueError(
											    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''	)

								if return_overflowing_tokens is True and return_offsets_mapping is False:
											raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''	)

								# first, apply the image processor
								lowerCamelCase__						:						Dict						      =self.image_processor(images=lowerCamelCase,    return_tensors=lowerCamelCase	)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
											if isinstance(lowerCamelCase,    lowerCamelCase	):
														lowerCamelCase__						:						List[str]						      =[text]  # add batch dimension (as the image processor always adds a batch dimension)
											lowerCamelCase__						:						int						      =features['''words''']

								lowerCamelCase__						:						Tuple						      =self.tokenizer(
								    text=text if text is not None else features['''words'''],    text_pair=text_pair if text_pair is not None else None,    boxes=boxes if boxes is not None else features['''boxes'''],    word_labels=lowerCamelCase,    add_special_tokens=lowerCamelCase,    padding=lowerCamelCase,    truncation=lowerCamelCase,    max_length=lowerCamelCase,    stride=lowerCamelCase,    pad_to_multiple_of=lowerCamelCase,    return_token_type_ids=lowerCamelCase,    return_attention_mask=lowerCamelCase,    return_overflowing_tokens=lowerCamelCase,    return_special_tokens_mask=lowerCamelCase,    return_offsets_mapping=lowerCamelCase,    return_length=lowerCamelCase,    verbose=lowerCamelCase,    return_tensors=lowerCamelCase,    **lowerCamelCase,    )

								# add pixel values
								lowerCamelCase__						:						List[Any]						      =features.pop('''pixel_values'''	)
								if return_overflowing_tokens is True:
											lowerCamelCase__						:						Union[str, Any]						      =self.get_overflowing_images(lowerCamelCase,    encoded_inputs['''overflow_to_sample_mapping''']	)
								lowerCamelCase__						:						Optional[int]						      =images

								return encoded_inputs



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Dict	)->   Optional[int]:
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								lowerCamelCase__						:						Optional[int]						      =[]
								for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]	)

								if len(lowerCamelCase	) != len(lowerCamelCase	):
											raise ValueError(
											    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
											    F''' {len(lowerCamelCase	)} and {len(lowerCamelCase	)}'''	)

								return images_with_overflow



					def 						snake_case			(			self				:    Tuple,    *lowerCamelCase				:    List[str],    **lowerCamelCase				:    str	)->   List[Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    Tuple,    *lowerCamelCase				:    Dict,    **lowerCamelCase				:    Tuple	)->   Any:
								return self.tokenizer.decode(*lowerCamelCase,    **lowerCamelCase	)



					@property
					def 						snake_case			(			self				:    List[str]	)->   Tuple:
								return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



					@property
					def 						snake_case			(			self				:    List[Any]	)->   List[str]:
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',    lowerCamelCase,    )
								return self.image_processor_class




					@property
					def 						snake_case			(			self				:    Optional[Any]	)->   Optional[Any]:
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',    lowerCamelCase,    )
								return self.image_processor






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)






