code: 
  
  
  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
  _SCREAMING_SNAKE_CASE    :				List[Any]       =					False
  _SCREAMING_SNAKE_CASE    :				Any       =					Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
  _SCREAMING_SNAKE_CASE    :				Tuple       =					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, 20_48), dtype="float32"),
              "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
              "preds_per_image": datasets.Value(dtype="int32"),
          }
      )
  )
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case=sys.argv[1:]					):
           snake_case					=       None
           snake_case					=       None
           snake_case					=       None
           snake_case					=       1
           snake_case				,						snake_case					=       getopt.getopt(__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(__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(__snake_case					), F'''{outputfile}'''
           if subset_list is not None:
                with open(os.path.realpath(__snake_case					)					) as f:
                     snake_case					=       {self._vqa_file_split()[0] for x in tryload(__snake_case					)}
           else:
                snake_case					=       None
  
           snake_case					=       CONFIG
           if torch.cuda.is_available():
                snake_case					=       '''cuda'''
           snake_case					=       os.path.realpath(__snake_case					)
           snake_case					=       os.path.realpath(__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 	a_    ( self    ,      __snake_case					):
           snake_case					=       int(file.split('''.'''					)[0].split('''_'''					)[-1]					)
           snake_case					=       os.path.join(self.inputdir    ,      __snake_case					)
           return (img_id, filepath)
      @property
      def 	a_    ( self					):
           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(__snake_case					)					)
                if len(__snake_case					) == self.batch:
                     snake_case					=       batch
                     snake_case					=       []
                     yield list(map(__snake_case    ,      zip(*__snake_case					)					)					)
  
           for i in range(1					):
                yield list(map(__snake_case    ,      zip(*__snake_case					)					)					)
  
  
  
  
      def __call__( self					):
           # make writer
           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(__snake_case					)
                snake_case					=       self.model(
                    __snake_case    ,      __snake_case    ,      scales_yx=__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(__snake_case					)
                     snake_case					=       self.schema.encode_batch(__snake_case					)
                     writer.write_batch(__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 >> 2_0} mb'''					)
  
  
  
  
  
  
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       try:
            snake_case					=       json.load(UpperCamelCase_       )
            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__":
   _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					Extract(sys.argv[1:])
   extract()
   if not TEST:
    _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
style_context: 
  
  
  import argparse
  import datetime
  import json
  import time
  import warnings
  from logging import getLogger
  from pathlib import Path
  from typing import Dict, List
  
  import torch
  from tqdm import tqdm
  
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
  from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
  
  
  _SCREAMING_SNAKE_CASE    :				str       =					getLogger(__name__)
  
  
  _SCREAMING_SNAKE_CASE    :				int       =					"cuda" if torch.cuda.is_available() else "cpu"
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_ = 8							,UpperCamelCase_ = DEFAULT_DEVICE							,UpperCamelCase_=False							,UpperCamelCase_="summarization"							,UpperCamelCase_=None							,**UpperCamelCase_							,):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       Path(UpperCamelCase_       ).open('''w'''							,encoding='''utf-8'''       )
       snake_case					=       str(UpperCamelCase_       )
       snake_case					=       AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_       ).to(UpperCamelCase_       )
       if fpaa:
            snake_case					=       model.half()
  
       snake_case					=       AutoTokenizer.from_pretrained(UpperCamelCase_       )
       logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''       )  # if this is wrong, check config.model_type.
  
       snake_case					=       time.time()
       # update config with task specific params
       use_task_specific_params(UpperCamelCase_							,UpperCamelCase_       )
       if prefix is None:
            snake_case					=       prefix or getattr(model.config							,'''prefix'''							,''''''       ) or ''''''
       for examples_chunk in tqdm(list(chunks(UpperCamelCase_							,UpperCamelCase_       )       )       ):
            snake_case					=       [prefix + text for text in examples_chunk]
            snake_case					=       tokenizer(UpperCamelCase_							,return_tensors='''pt'''							,truncation=UpperCamelCase_							,padding='''longest'''       ).to(UpperCamelCase_       )
            snake_case					=       model.generate(
                input_ids=batch.input_ids							,attention_mask=batch.attention_mask							,**UpperCamelCase_							,)
            snake_case					=       tokenizer.batch_decode(UpperCamelCase_							,skip_special_tokens=UpperCamelCase_							,clean_up_tokenization_spaces=UpperCamelCase_       )
            for hypothesis in dec:
                 fout.write(hypothesis + '''\n'''       )
                 fout.flush()
       fout.close()
       snake_case					=       int(time.time() - start_time       )  # seconds
       snake_case					=       len(UpperCamelCase_       )
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,4       )}
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''       )
  def     UpperCAmelCase__		(UpperCamelCase_=True       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       argparse.ArgumentParser()
       parser.add_argument('''model_name'''							,type=UpperCamelCase_							,help='''like facebook/bart-large-cnn,t5-base, etc.'''       )
       parser.add_argument('''input_path'''							,type=UpperCamelCase_							,help='''like cnn_dm/test.source'''       )
       parser.add_argument('''save_path'''							,type=UpperCamelCase_							,help='''where to save summaries'''       )
       parser.add_argument('''--reference_path'''							,type=UpperCamelCase_							,required=UpperCamelCase_							,help='''like cnn_dm/test.target'''       )
       parser.add_argument('''--score_path'''							,type=UpperCamelCase_							,required=UpperCamelCase_							,default='''metrics.json'''							,help='''where to save metrics'''       )
       parser.add_argument('''--device'''							,type=UpperCamelCase_							,required=UpperCamelCase_							,default=UpperCamelCase_							,help='''cuda, cuda:1, cpu etc.'''       )
       parser.add_argument(
           '''--prefix'''							,type=UpperCamelCase_							,required=UpperCamelCase_							,default=UpperCamelCase_							,help='''will be added to the begininng of src examples'''       )
       parser.add_argument('''--task'''							,type=UpperCamelCase_							,default='''summarization'''							,help='''used for task_specific_params + metrics'''       )
       parser.add_argument('''--bs'''							,type=UpperCamelCase_							,default=8							,required=UpperCamelCase_							,help='''batch size'''       )
       parser.add_argument(
           '''--n_obs'''							,type=UpperCamelCase_							,default=-1							,required=UpperCamelCase_							,help='''How many observations. Defaults to all.'''       )
       parser.add_argument('''--fp16'''							,action='''store_true'''       )
       parser.add_argument('''--dump-args'''							,action='''store_true'''							,help='''print the custom hparams with the results'''       )
       parser.add_argument(
           '''--info'''							,nargs='''?'''							,type=UpperCamelCase_							,const=datetime_now()							,help=(
               '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
               ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
           )							,)
       # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
       snake_case				,						snake_case					=       parser.parse_known_args()
       snake_case					=       parse_numeric_n_bool_cl_kwargs(UpperCamelCase_       )
       if parsed_args and verbose:
            print(F'''parsed the following generate kwargs: {parsed_args}'''       )
       with open(args.input_path       ) as f:
            snake_case					=       [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
            snake_case					=       examples[: args.n_obs]
       Path(args.save_path       ).parent.mkdir(exist_ok=UpperCamelCase_       )
       if args.reference_path is None and Path(args.score_path       ).exists():
            warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''       )
       snake_case					=       generate_summaries_or_translations(
           UpperCamelCase_							,args.save_path							,args.model_name							,batch_size=args.bs							,device=args.device							,fpaa=args.fpaa							,task=args.task							,prefix=args.prefix							,**UpperCamelCase_							,)
  
       if args.reference_path is None:
            return {}
  
       # Compute scores
       snake_case					=       calculate_bleu if '''translation''' in args.task else calculate_rouge
       snake_case					=       [x.rstrip() for x in open(args.save_path       ).readlines()]
       snake_case					=       [x.rstrip() for x in open(args.reference_path       ).readlines()][: len(UpperCamelCase_       )]
       snake_case					=       score_fn(UpperCamelCase_							,UpperCamelCase_       )
       scores.update(UpperCamelCase_       )
  
       if args.dump_args:
            scores.update(UpperCamelCase_       )
       if args.info:
            snake_case					=       args.info
  
       if verbose:
            print(UpperCamelCase_       )
  
       if args.score_path is not None:
            json.dump(UpperCamelCase_							,open(args.score_path							,'''w'''       )       )
  
       return scores
  
  
  if __name__ == "__main__":
   # Usage for MT:
   # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
   run_generate(verbose=True)
  
  
  
  
  