


'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

snake_case__     							= logging.get_logger(__name__)

snake_case__     							= {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

snake_case__     							= {
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
}

snake_case__     							= {
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}
@lru_cache()
def  __SCREAMING_SNAKE_CASE       (				)       ->  Dict:
						__magic_name__				:							Optional[int]				=      (
						    list(range(ord('!'				)      ,						ord('~'				) + 1				)				) + list(range(ord('¡'				)      ,						ord('¬'				) + 1				)				) + list(range(ord('®'				)      ,						ord('ÿ'				) + 1				)				)
						)
						__magic_name__				:							Any				=      bs[:]
						__magic_name__				:							List[Any]				=      0
						for b in range(2**8				):
												if b not in bs:
																		bs.append(lowercase__				)
																		cs.append(2**8 + n				)
																		n += 1
						__magic_name__				:							Dict				=      [chr(lowercase__				) for n in cs]
						return dict(zip(lowercase__      ,						lowercase__				)				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple				)       ->  Tuple:
						__magic_name__				:							Dict				=      set()
						__magic_name__				:							List[str]				=      word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)				)
												__magic_name__				:							Any				=      char
						return pairs



class     snake_case__  (      __lowerCamelCase  ):
				__lowerCAmelCase		=      VOCAB_FILES_NAMES
				__lowerCAmelCase		=      PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase		=      ['input_ids', 'attention_mask']
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase="replace"     ,      _UpperCAmelCase="<|endoftext|>"     ,      _UpperCAmelCase="<|endoftext|>"     ,      _UpperCAmelCase="<|endoftext|>"     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      AddedToken(_UpperCAmelCase     ,      lstrip=_UpperCAmelCase     ,      rstrip=_UpperCAmelCase      ) if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ) else bos_token
										__magic_name__				:							List[str]				=      AddedToken(_UpperCAmelCase     ,      lstrip=_UpperCAmelCase     ,      rstrip=_UpperCAmelCase      ) if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ) else eos_token
										__magic_name__				:							Dict				=      AddedToken(_UpperCAmelCase     ,      lstrip=_UpperCAmelCase     ,      rstrip=_UpperCAmelCase      ) if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ) else unk_token
										__magic_name__				:							Any				=      AddedToken(_UpperCAmelCase     ,      lstrip=_UpperCAmelCase     ,      rstrip=_UpperCAmelCase      ) if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ) else pad_token
										super().__init__(
										    errors=_UpperCAmelCase     ,      unk_token=_UpperCAmelCase     ,      bos_token=_UpperCAmelCase     ,      eos_token=_UpperCAmelCase     ,      pad_token=_UpperCAmelCase     ,      add_prefix_space=_UpperCAmelCase     ,      add_bos_token=_UpperCAmelCase     ,      **_UpperCAmelCase     ,      )
										__magic_name__				:							Dict				=      add_bos_token

										with open(_UpperCAmelCase     ,      encoding='utf-8'      ) as vocab_handle:
																__magic_name__				:							Dict				=      json.load(_UpperCAmelCase      )
										__magic_name__				:							Optional[int]				=      {v: k for k, v in self.encoder.items()}
										__magic_name__				:							Any				=      errors  # how to handle errors in decoding
										__magic_name__				:							List[str]				=      bytes_to_unicode()
										__magic_name__				:							int				=      {v: k for k, v in self.byte_encoder.items()}
										with open(_UpperCAmelCase     ,      encoding='utf-8'      ) as merges_handle:
																__magic_name__				:							List[str]				=      merges_handle.read().split('\n'      )[1:-1]
										__magic_name__				:							int				=      [tuple(merge.split()      ) for merge in bpe_merges]
										__magic_name__				:							Any				=      dict(zip(_UpperCAmelCase     ,      range(len(_UpperCAmelCase      )      )      )      )
										__magic_name__				:							Optional[Any]				=      {}
										__magic_name__				:							Any				=      add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										__magic_name__				:							int				=      re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'      )
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return len(self.encoder      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return dict(self.encoder     ,      **self.added_tokens_encoder      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if token in self.cache:
																return self.cache[token]
										__magic_name__				:							Tuple				=      tuple(_UpperCAmelCase      )
										__magic_name__				:							Union[str, Any]				=      get_pairs(_UpperCAmelCase      )

										if not pairs:
																return token

										while True:
																__magic_name__				:							str				=      min(_UpperCAmelCase     ,      key=lambda _UpperCAmelCase      : self.bpe_ranks.get(_UpperCAmelCase     ,      float('inf'      )      )      )
																if bigram not in self.bpe_ranks:
																						break
																__magic_name__		, __magic_name__				:							List[str]				=      bigram
																__magic_name__				:							List[Any]				=      []
																__magic_name__				:							Dict				=      0
																while i < len(_UpperCAmelCase      ):
																						try:
																												__magic_name__				:							List[str]				=      word.index(_UpperCAmelCase     ,      _UpperCAmelCase      )
																						except ValueError:
																												new_word.extend(word[i:]      )
																												break
																						else:
																												new_word.extend(word[i:j]      )
																												__magic_name__				:							Tuple				=      j

																						if word[i] == first and i < len(_UpperCAmelCase      ) - 1 and word[i + 1] == second:
																												new_word.append(first + second      )
																												i += 2
																						else:
																												new_word.append(word[i]      )
																												i += 1
																__magic_name__				:							str				=      tuple(_UpperCAmelCase      )
																__magic_name__				:							List[Any]				=      new_word
																if len(_UpperCAmelCase      ) == 1:
																						break
																else:
																						__magic_name__				:							Optional[Any]				=      get_pairs(_UpperCAmelCase      )
										__magic_name__				:							Union[str, Any]				=      ' '.join(_UpperCAmelCase      )
										__magic_name__				:							int				=      word
										return word
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




										if self.add_bos_token:
																__magic_name__				:							List[str]				=      [self.bos_token_id]
										else:
																__magic_name__				:							Union[str, Any]				=      []

										__magic_name__				:							Any				=      bos_token_ids + token_ids_a

										if token_ids_a is None:
																return output

										return output + bos_token_ids + token_ids_a
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = False      ):




										"""simple docstring"""




										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_UpperCAmelCase     ,      token_ids_a=_UpperCAmelCase     ,      already_has_special_tokens=_UpperCAmelCase      )

										if not self.add_bos_token:
																return super().get_special_tokens_mask(
																    token_ids_a=_UpperCAmelCase     ,      token_ids_a=_UpperCAmelCase     ,      already_has_special_tokens=_UpperCAmelCase      )

										if token_ids_a is None:
																return [1] + ([0] * len(_UpperCAmelCase      ))
										return [1] + ([0] * len(_UpperCAmelCase      )) + [1] + ([0] * len(_UpperCAmelCase      ))
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      []
										for token in re.findall(self.pat     ,      _UpperCAmelCase      ):
																__magic_name__				:							str				=      ''.join(
																    self.byte_encoder[b] for b in token.encode('utf-8'      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCAmelCase      ).split(' '      )      )
										return bpe_tokens
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return self.encoder.get(_UpperCAmelCase     ,      self.encoder.get(self.unk_token      )      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return self.decoder.get(_UpperCAmelCase      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      ''.join(_UpperCAmelCase      )
										__magic_name__				:							Any				=      bytearray([self.byte_decoder[c] for c in text]      ).decode('utf-8'     ,      errors=self.errors      )
										return text
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None      ):




										"""simple docstring"""




										if not os.path.isdir(_UpperCAmelCase      ):
																logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
																return
										__magic_name__				:							Optional[int]				=      os.path.join(
										    _UpperCAmelCase     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
										__magic_name__				:							List[str]				=      os.path.join(
										    _UpperCAmelCase     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

										with open(_UpperCAmelCase     ,      'w'     ,      encoding='utf-8'      ) as f:
																f.write(json.dumps(self.encoder     ,      indent=2     ,      sort_keys=_UpperCAmelCase     ,      ensure_ascii=_UpperCAmelCase      ) + '\n'      )

										__magic_name__				:							str				=      0
										with open(_UpperCAmelCase     ,      'w'     ,      encoding='utf-8'      ) as writer:
																writer.write('#version: 0.2\n'      )
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,      key=lambda _UpperCAmelCase      : kv[1]      ):
																						if index != token_index:
																												logger.warning(
																												    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																												    ' Please check that the tokenizer is not corrupted!'      )
																												__magic_name__				:							List[str]				=      token_index
																						writer.write(' '.join(_UpperCAmelCase      ) + '\n'      )
																						index += 1

										return vocab_file, merge_file
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=False     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      kwargs.pop('add_prefix_space'     ,      self.add_prefix_space      )
										if is_split_into_words or add_prefix_space:
																__magic_name__				:							int				=      ' ' + text
										return (text, kwargs)





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(_UpperCAmelCase     ,      add_special_tokens=_UpperCAmelCase      ) + [self.eos_token_id]      )
										if len(_UpperCAmelCase      ) > self.model_max_length:
																__magic_name__				:							Tuple				=      input_ids[-self.model_max_length :]
										return input_ids






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
snake_case__     							= False
snake_case__     							= Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
snake_case__     							= 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     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase=sys.argv[1:]      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      None
										__magic_name__				:							int				=      None
										__magic_name__				:							Optional[int]				=      None
										__magic_name__				:							Optional[int]				=      1
										__magic_name__		, __magic_name__				:							int				=      getopt.getopt(_UpperCAmelCase     ,      'i:o:b:s'     ,      ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']      )
										for opt, arg in opts:
																if opt in ("-i", "--inputdir"):
																						__magic_name__				:							List[str]				=      arg
																elif opt in ("-o", "--outfile"):
																						__magic_name__				:							Union[str, Any]				=      arg
																elif opt in ("-b", "--batch_size"):
																						__magic_name__				:							Any				=      int(_UpperCAmelCase      )
																elif opt in ("-s", "--subset_list"):
																						__magic_name__				:							List[Any]				=      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:
																						__magic_name__				:							str				=      {self._vqa_file_split()[0] for x in tryload(_UpperCAmelCase      )}
										else:
																__magic_name__				:							Optional[int]				=      None

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




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      int(file.split('.'      )[0].split('_'      )[-1]      )
										__magic_name__				:							Union[str, Any]				=      os.path.join(self.inputdir     ,      _UpperCAmelCase      )
										return (img_id, filepath)
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      []
										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:
																						__magic_name__				:							int				=      batch
																						__magic_name__				:							Union[str, Any]				=      []
																						yield list(map(_UpperCAmelCase     ,      zip(*_UpperCAmelCase      )      )      )

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





				def __call__(  self      ):




										"""simple docstring"""




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






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple				)       ->  Union[str, Any]:
						try:
												__magic_name__				:							List[str]				=      json.load(lowercase__				)
												try:
																		__magic_name__				:							Dict				=      list(data.keys()				)
												except Exception:
																		__magic_name__				:							int				=      [d['img_id'] for d in data]
						except Exception:
												try:
																		__magic_name__				:							str				=      eval(stream.read()				)
												except Exception:
																		__magic_name__				:							Any				=      stream.read().split('\n'				)
						return data


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


