




'''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
UpperCAmelCase_    :					Dict					      =						False
UpperCAmelCase_    :					Optional[int]					      =						Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCAmelCase_    :					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				:


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



            '''simple docstring'''




            __snake_case         =					None
            __snake_case         =					None
            __snake_case         =					None
            __snake_case         =					1
            __snake_case   ,      __snake_case         =					getopt.getopt(__SCREAMING_SNAKE_CASE      ,					'''i:o:b:s'''      ,					['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']					)
            for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                      __snake_case         =					arg
                 elif opt in ("-o", "--outfile"):
                      __snake_case         =					arg
                 elif opt in ("-b", "--batch_size"):
                      __snake_case         =					int(__SCREAMING_SNAKE_CASE					)
                 elif opt in ("-s", "--subset_list"):
                      __snake_case         =					arg

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

            __snake_case         =					CONFIG
            if torch.cuda.is_available():
                 __snake_case         =					'''cuda'''
            __snake_case         =					os.path.realpath(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					os.path.realpath(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					Preprocess(self.config					)
            __snake_case         =					GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''      ,					config=self.config					)
            __snake_case         =					batch_size if batch_size != 0 else 1
            __snake_case         =					DEFAULT_SCHEMA


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					int(file.split('''.'''					)[0].split('''_'''					)[-1]					)
            __snake_case         =					os.path.join(self.inputdir      ,					__SCREAMING_SNAKE_CASE					)
            return (img_id, filepath)


       @property
       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case         =					[]
            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(__SCREAMING_SNAKE_CASE					)					)
                 if len(__SCREAMING_SNAKE_CASE					) == self.batch:
                      __snake_case         =					batch
                      __snake_case         =					[]
                      yield list(map(__SCREAMING_SNAKE_CASE      ,					zip(*__SCREAMING_SNAKE_CASE					)					)					)

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




       def __call__(				self					) ->					Any:



            '''simple docstring'''




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







def      _UpperCamelCase	(_lowerCamelCase							:       List[str]		)->				Dict:




     '''simple docstring'''





     try:
          __snake_case         =					json.load(_lowerCamelCase		)
          try:
               __snake_case         =					list(data.keys()		)
          except Exception:
               __snake_case         =					[d['''img_id'''] for d in data]
     except Exception:
          try:
               __snake_case         =					eval(stream.read()		)
          except Exception:
               __snake_case         =					stream.read().split('''\n'''		)
     return data


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












'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_    :					Any					      =						{
    '''configuration_retribert''': ['''RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RetriBertConfig'''],
    '''tokenization_retribert''': ['''RetriBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					str					      =						['''RetriBertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					Optional[int]					      =						[
         '''RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RetriBertModel''',
         '''RetriBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
     from .tokenization_retribert import RetriBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_retribert_fast import RetriBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_retribert import (
              RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RetriBertModel,
              RetriBertPreTrainedModel,
          )

else:
     import sys

     UpperCAmelCase_    :					Any					      =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






