




'''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
a		:  Union[str, Any]   			=			False
a		:  List[Any]   			=			Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
a		:  Union[str, 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     UpperCamelCase					:



  def __init__( self		,    __lowerCAmelCase=sys.argv[1:]   ):







      '''simple docstring'''


      _a							:       List[Any]       					=			None
      _a							:       Union[str, Any]       					=			None
      _a							:       Union[str, Any]       					=			None
      _a							:       List[Any]       					=			1
      _a							:       str       					=			getopt.getopt(__a		,    """i:o:b:s"""		,    ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]   )
      for opt, arg in opts:
          if opt in ("-i", "--inputdir"):
              _a							:       List[str]       					=			arg
          elif opt in ("-o", "--outfile"):
              _a							:       Any       					=			arg
          elif opt in ("-b", "--batch_size"):
              _a							:       Optional[Any]       					=			int(__a   )
          elif opt in ("-s", "--subset_list"):
              _a							:       Any       					=			arg

      assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
      assert outputfile is not None and not os.path.isfile(__a   ), f'''{outputfile}'''
      if subset_list is not None:
          with open(os.path.realpath(__a   )   ) as f:
              _a							:       int       					=			{self._vqa_file_split()[0] for x in tryload(__a   )}
      else:
          _a							:       Tuple       					=			None

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



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			int(file.split("""."""   )[0].split("""_"""   )[-1]   )
      _a							:       int       					=			os.path.join(self.inputdir		,    __a   )
      return (img_id, filepath)



  @property
  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Optional[Any]       					=			[]
      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(__a   )   )
          if len(__a   ) == self.batch:
              _a							:       List[Any]       					=			batch
              _a							:       str       					=			[]
              yield list(map(__a		,    zip(*__a   )   )   )

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



  def __call__( self   ):







      '''simple docstring'''


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






def UpperCAmelCase						(					UpperCAmelCase_	):
    try:
        _a							:       List[Any]       					=			json.load(_SCREAMING_SNAKE_CASE	)
        try:
            _a							:       Optional[Any]       					=			list(data.keys()	)
        except Exception:
            _a							:       List[Any]       					=			[d['img_id'] for d in data]
    except Exception:
        try:
            _a							:       List[str]       					=			eval(stream.read()	)
        except Exception:
            _a							:       Union[str, Any]       					=			stream.read().split("""\n"""	)
    return data


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








'''simple docstring'''





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbartaa import MBartaaTokenizer
else:
  a		:  str   			=			None


a		:  List[str]   			=			logging.get_logger(__name__)

a		:  Dict   			=			{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:  Optional[Any]   			=			{
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

a		:  Any   			=			{
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# fmt: off
a		:  Dict   			=			['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']




class     UpperCamelCase					(  snake_case__					):
  a    :str      					=				VOCAB_FILES_NAMES
  a    :int      					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a    :Dict      					=				PRETRAINED_VOCAB_FILES_MAP
  a    :List[str]      					=				['input_ids', 'attention_mask']
  a    :Optional[Any]      					=				MBartaaTokenizer

  a    :List[int]      					=				[]
  a    :List[int]      					=				[]



  def __init__( self		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase="</s>"		,    __lowerCAmelCase="</s>"		,    __lowerCAmelCase="<s>"		,    __lowerCAmelCase="<unk>"		,    __lowerCAmelCase="<pad>"		,    __lowerCAmelCase="<mask>"		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       List[str]       					=			AddedToken(__lowerCAmelCase		,    lstrip=__lowerCAmelCase		,    rstrip=__lowerCAmelCase   ) if isinstance(__lowerCAmelCase		,    __lowerCAmelCase   ) else mask_token

      _a							:       List[str]       					=			kwargs.get("""additional_special_tokens"""		,    []   )
      kwargs["additional_special_tokens"] += [
          code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          __lowerCAmelCase		,    src_lang=__lowerCAmelCase		,    tgt_lang=__lowerCAmelCase		,    tokenizer_file=__lowerCAmelCase		,    eos_token=__lowerCAmelCase		,    sep_token=__lowerCAmelCase		,    cls_token=__lowerCAmelCase		,    unk_token=__lowerCAmelCase		,    pad_token=__lowerCAmelCase		,    mask_token=__lowerCAmelCase		,    **__lowerCAmelCase		,    )

      _a							:       Optional[int]       					=			vocab_file
      _a							:       int       					=			False if not self.vocab_file else True

      _a							:       str       					=			{
          lang_code: self.convert_tokens_to_ids(__lowerCAmelCase   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      _a							:       Dict       					=			src_lang if src_lang is not None else """en_XX"""
      _a							:       Tuple       					=			tgt_lang
      _a							:       Tuple       					=			self.lang_code_to_id[self._src_lang]
      self.set_src_lang_special_tokens(self._src_lang   )



  @property
  def 						a_       ( self   ):







      '''simple docstring'''


      return self._src_lang



  @src_lang.setter
  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       int       					=			new_src_lang
      self.set_src_lang_special_tokens(self._src_lang   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = "en_XX"		,    __lowerCAmelCase = None		,    __lowerCAmelCase = "ro_RO"		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       List[Any]       					=			src_lang
      _a							:       Any       					=			tgt_lang
      return super().prepare_seqaseq_batch(__lowerCAmelCase		,    __lowerCAmelCase		,    **__lowerCAmelCase   )



  def 						a_       ( self   ):







      '''simple docstring'''


      return self.set_src_lang_special_tokens(self.src_lang   )



  def 						a_       ( self   ):







      '''simple docstring'''


      return self.set_tgt_lang_special_tokens(self.tgt_lang   )



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Union[str, Any]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			[self.cur_lang_code_id]
      _a							:       int       					=			[self.eos_token_id]

      _a							:       Any       					=			self.convert_ids_to_tokens(self.prefix_tokens   )
      _a							:       Dict       					=			self.convert_ids_to_tokens(self.suffix_tokens   )

      _a							:       Union[str, Any]       					=			processors.TemplateProcessing(
          single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str		,    pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str		,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,    self.prefix_tokens + self.suffix_tokens   )   )		,    )



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       List[str]       					=			[self.cur_lang_code_id]
      _a							:       Tuple       					=			[self.eos_token_id]

      _a							:       List[Any]       					=			self.convert_ids_to_tokens(self.prefix_tokens   )
      _a							:       int       					=			self.convert_ids_to_tokens(self.suffix_tokens   )

      _a							:       str       					=			processors.TemplateProcessing(
          single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str		,    pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str		,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,    self.prefix_tokens + self.suffix_tokens   )   )		,    )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    **__lowerCAmelCase   ):







      '''simple docstring'''


      if src_lang is None or tgt_lang is None:
          raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""   )
      _a							:       List[Any]       					=			src_lang
      _a							:       List[str]       					=			self(__lowerCAmelCase		,    add_special_tokens=__lowerCAmelCase		,    return_tensors=__lowerCAmelCase		,    **__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       Tuple       					=			tgt_lang_id
      return inputs



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      if not self.can_save_slow_tokenizer:
          raise ValueError(
              """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
              """tokenizer."""   )

      if not os.path.isdir(__lowerCAmelCase   ):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
          return
      _a							:       Union[str, Any]       					=			os.path.join(
          __lowerCAmelCase		,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(__lowerCAmelCase   ):
          copyfile(self.vocab_file		,    __lowerCAmelCase   )

      return (out_vocab_file,)


