





'''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					:						Optional[Any]					     =							False
_lowerCamelCase					:						Tuple					     =							Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_lowerCamelCase					:						List[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, 2048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)



class 				__UpperCAmelCase	:







		'''simple docstring'''







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

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







		@property
		def 							A  (self    :   Union[str, Any]   ):
			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(_lowerCAmelCase   )   )
				if len(_lowerCAmelCase   ) == self.batch:
					A					=   batch
					A					=   []
					yield list(map(_lowerCAmelCase   ,   zip(*_lowerCAmelCase   )   )   )

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







		def __call__(self    :   Union[str, Any]   ):
			# 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(_lowerCAmelCase   )
				A					=   self.model(
				    _lowerCAmelCase   ,   _lowerCAmelCase   ,   scales_yx=_lowerCAmelCase   ,   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(_lowerCAmelCase   )
					A					=   self.schema.encode_batch(_lowerCAmelCase   )
					writer.write_batch(_lowerCAmelCase   )
				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  __a			(  UpperCAmelCase     )   ->Optional[int]:



	"""simple docstring"""







	try:
		A					=   json.load(UpperCAmelCase     )
		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)








'''simple docstring'''


import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)

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

_lowerCamelCase					:						str					     =							{
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
}

_lowerCamelCase					:						Union[str, Any]					     =							{
    'openai-gpt': 512,
}




def  __a			(  UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   text.strip()
	if not text:
		return []
	A					=   text.split()
	return tokens



class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def __init__(self    :   Optional[int]   ,   _lowerCAmelCase    :   int=True   ,   _lowerCAmelCase    :   List[str]=None   ,   _lowerCAmelCase    :   str=True   ,   _lowerCAmelCase    :   int=None   ,   _lowerCAmelCase    :   Optional[Any]=True   ,   ):
			if never_split is None:
				A					=   []
			A					=   do_lower_case
			A					=   set(_lowerCAmelCase   )
			A					=   tokenize_chinese_chars
			A					=   strip_accents
			A					=   do_split_on_punc







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   Any   ,   _lowerCAmelCase    :   Any=None   ):
			A					=   self.never_split.union(set(_lowerCAmelCase   )   ) if never_split else self.never_split
			A					=   self._clean_text(_lowerCAmelCase   )

			# This was added on November 1st, 2018 for the multilingual and Chinese
			# models. This is also applied to the English models now, but it doesn't
			# matter since the English models were not trained on any Chinese data
			# and generally don't have any Chinese data in them (there are Chinese
			# characters in the vocabulary because Wikipedia does have some Chinese
			# words in the English Wikipedia.).
			if self.tokenize_chinese_chars:
				A					=   self._tokenize_chinese_chars(_lowerCAmelCase   )
			# prevents treating the same character with different unicode codepoints as different characters
			A					=   unicodedata.normalize("""NFC"""   ,   _lowerCAmelCase   )
			A					=   whitespace_tokenize(_lowerCAmelCase   )
			A					=   []
			for token in orig_tokens:
				if token not in never_split:
					if self.do_lower_case:
						A					=   token.lower()
						if self.strip_accents is not False:
							A					=   self._run_strip_accents(_lowerCAmelCase   )
					elif self.strip_accents:
						A					=   self._run_strip_accents(_lowerCAmelCase   )
				split_tokens.extend(self._run_split_on_punc(_lowerCAmelCase   ,   _lowerCAmelCase   )   )

			A					=   whitespace_tokenize(""" """.join(_lowerCAmelCase   )   )
			return output_tokens







		def 							A  (self    :   Optional[Any]   ,   _lowerCAmelCase    :   Optional[int]   ):
			A					=   unicodedata.normalize("""NFD"""   ,   _lowerCAmelCase   )
			A					=   []
			for char in text:
				A					=   unicodedata.category(_lowerCAmelCase   )
				if cat == "Mn":
					continue
				output.append(_lowerCAmelCase   )
			return "".join(_lowerCAmelCase   )







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[str]=None   ):
			if not self.do_split_on_punc or (never_split is not None and text in never_split):
				return [text]
			A					=   list(_lowerCAmelCase   )
			A					=   0
			A					=   True
			A					=   []
			while i < len(_lowerCAmelCase   ):
				A					=   chars[i]
				if _is_punctuation(_lowerCAmelCase   ):
					output.append([char]   )
					A					=   True
				else:
					if start_new_word:
						output.append([]   )
					A					=   False
					output[-1].append(_lowerCAmelCase   )
				i += 1

			return ["".join(_lowerCAmelCase   ) for x in output]







		def 							A  (self    :   int   ,   _lowerCAmelCase    :   Any   ):
			A					=   []
			for char in text:
				A					=   ord(_lowerCAmelCase   )
				if self._is_chinese_char(_lowerCAmelCase   ):
					output.append(""" """   )
					output.append(_lowerCAmelCase   )
					output.append(""" """   )
				else:
					output.append(_lowerCAmelCase   )
			return "".join(_lowerCAmelCase   )







		def 							A  (self    :   Dict   ,   _lowerCAmelCase    :   Any   ):
			# This defines a "chinese character" as anything in the CJK Unicode block:
			#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
			#
			# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
			# despite its name. The modern Korean Hangul alphabet is a different block,
			# as is Japanese Hiragana and Katakana. Those alphabets are used to write
			# space-separated words, so they are not treated specially and handled
			# like the all of the other languages.
			if (
			    (cp >= 0X4_e00 and cp <= 0X9_fff)
			    or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
			    or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
			    or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
			    or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
			    or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
			    or (cp >= 0Xf_900 and cp <= 0Xf_aff)
			    or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
			):  #
				return True

			return False







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   Any   ):
			A					=   []
			for char in text:
				A					=   ord(_lowerCAmelCase   )
				if cp == 0 or cp == 0Xf_ffd or _is_control(_lowerCAmelCase   ):
					continue
				if _is_whitespace(_lowerCAmelCase   ):
					output.append(""" """   )
				else:
					output.append(_lowerCAmelCase   )
			return "".join(_lowerCAmelCase   )






def  __a			(  UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   set()
	A					=   word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)     )
		A					=   char
	return pairs




def  __a			(  UpperCAmelCase     )   ->List[str]:



	"""simple docstring"""







	A					=   text.replace("""—"""							,      """-"""     )
	A					=   text.replace("""–"""							,      """-"""     )
	A					=   text.replace("""―"""							,      """-"""     )
	A					=   text.replace("""…"""							,      """..."""     )
	A					=   text.replace("""´"""							,      """'"""     )
	A					=   re.sub(R"""(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"""							,      R""" \1 """							,      UpperCAmelCase     )
	A					=   re.sub(R"""\s*\n\s*"""							,      """ \n """							,      UpperCAmelCase     )
	A					=   re.sub(R"""[^\S\n]+"""							,      """ """							,      UpperCAmelCase     )
	return text.strip()



class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     VOCAB_FILES_NAMES
		__lowerCAmelCase								=     PRETRAINED_VOCAB_FILES_MAP
		__lowerCAmelCase								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowerCAmelCase								=     ['''input_ids''', '''attention_mask''']







		def __init__(self    :   Dict   ,   _lowerCAmelCase    :   Optional[int]   ,   _lowerCAmelCase    :   List[Any]   ,   _lowerCAmelCase    :   Dict="<unk>"   ,   **_lowerCAmelCase    :   Dict   ):
			super().__init__(unk_token=_lowerCAmelCase   ,   **_lowerCAmelCase   )

			try:
				import ftfy
				from spacy.lang.en import English

				A					=   English()
				A					=   _nlp.tokenizer
				A					=   ftfy.fix_text
			except ImportError:
				logger.warning("""ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."""   )
				A					=   BasicTokenizer(do_lower_case=_lowerCAmelCase   )
				A					=   None

			with open(_lowerCAmelCase   ,   encoding="""utf-8"""   ) as vocab_handle:
				A					=   json.load(_lowerCAmelCase   )
			A					=   {v: k for k, v in self.encoder.items()}
			with open(_lowerCAmelCase   ,   encoding="""utf-8"""   ) as merges_handle:
				A					=   merges_handle.read().split("""\n"""   )[1:-1]
			A					=   [tuple(merge.split()   ) for merge in merges]
			A					=   dict(zip(_lowerCAmelCase   ,   range(len(_lowerCAmelCase   )   )   )   )
			A					=   {}







		@property
		def 							A  (self    :   Dict   ):
			return True







		@property
		def 							A  (self    :   Dict   ):
			return len(self.encoder   )







		def 							A  (self    :   str   ):
			return dict(self.encoder   ,   **self.added_tokens_encoder   )







		def 							A  (self    :   Optional[Any]   ,   _lowerCAmelCase    :   List[Any]   ):
			A					=   tuple(token[:-1]   ) + (token[-1] + """</w>""",)
			if token in self.cache:
				return self.cache[token]
			A					=   get_pairs(_lowerCAmelCase   )

			if not pairs:
				return token + "</w>"

			while True:
				A					=   min(_lowerCAmelCase   ,   key=lambda _lowerCAmelCase   : self.bpe_ranks.get(_lowerCAmelCase   ,   float("""inf"""   )   )   )
				if bigram not in self.bpe_ranks:
					break
				A	,     A					=   bigram
				A					=   []
				A					=   0
				while i < len(_lowerCAmelCase   ):
					try:
						A					=   word.index(_lowerCAmelCase   ,   _lowerCAmelCase   )
					except ValueError:
						new_word.extend(word[i:]   )
						break
					else:
						new_word.extend(word[i:j]   )
						A					=   j

					if word[i] == first and i < len(_lowerCAmelCase   ) - 1 and word[i + 1] == second:
						new_word.append(first + second   )
						i += 2
					else:
						new_word.append(word[i]   )
						i += 1
				A					=   tuple(_lowerCAmelCase   )
				A					=   new_word
				if len(_lowerCAmelCase   ) == 1:
					break
				else:
					A					=   get_pairs(_lowerCAmelCase   )
			A					=   """ """.join(_lowerCAmelCase   )
			if word == "\n  </w>":
				A					=   """\n</w>"""
			A					=   word
			return word







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   Optional[int]   ):
			A					=   []
			if self.fix_text is None:
				# Using BERT's BasicTokenizer
				A					=   self.nlp.tokenize(_lowerCAmelCase   )
				for token in text:
					split_tokens.extend(list(self.bpe(_lowerCAmelCase   ).split(""" """   )   )   )
			else:
				# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
				A					=   self.nlp(text_standardize(self.fix_text(_lowerCAmelCase   )   )   )
				for token in text:
					split_tokens.extend(list(self.bpe(token.text.lower()   ).split(""" """   )   )   )
			return split_tokens







		def 							A  (self    :   int   ,   _lowerCAmelCase    :   Dict   ):
			return self.encoder.get(_lowerCAmelCase   ,   self.encoder.get(self.unk_token   )   )







		def 							A  (self    :   Dict   ,   _lowerCAmelCase    :   Tuple   ):
			return self.decoder.get(_lowerCAmelCase   ,   self.unk_token   )







		def 							A  (self    :   Optional[int]   ,   _lowerCAmelCase    :   int   ):
			A					=   """""".join(_lowerCAmelCase   ).replace("""</w>"""   ,   """ """   ).strip()
			return out_string







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   str   ,   _lowerCAmelCase    :   Optional[str] = None   ):
			if not os.path.isdir(_lowerCAmelCase   ):
				logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
				return
			A					=   os.path.join(
			    _lowerCAmelCase   ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
			A					=   os.path.join(
			    _lowerCAmelCase   ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]   )

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

			A					=   0
			with open(_lowerCAmelCase   ,   """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 _lowerCAmelCase   : 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!"""   )
						A					=   token_index
					writer.write(""" """.join(_lowerCAmelCase   ) + """\n"""   )
					index += 1

			return vocab_file, merge_file

