'''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
__snake_case   :      Optional[Any]						    =  False
__snake_case   :      Dict						    =  Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__snake_case   :      Optional[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_048), 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    ,     _SCREAMING_SNAKE_CASE=sys.argv[1:] )		->     List[Any]:
           A_     =   None
           A_     =   None
           A_     =   None
           A_     =   1
           A_   ,A_     =   getopt.getopt(A__    ,     '''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(A__ )
               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(A__ ), F'''{outputfile}'''
           if subset_list is not None:
               with open(os.path.realpath(A__ ) ) as f:
                   A_     =   {self._vqa_file_split()[0] for x in tryload(A__ )}
           else:
               A_     =   None

           A_     =   CONFIG
           if torch.cuda.is_available():
               A_     =   '''cuda'''
           A_     =   os.path.realpath(A__ )
           A_     =   os.path.realpath(A__ )
           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    ,     _SCREAMING_SNAKE_CASE )		->     List[str]:
           A_     =   int(file.split('''.''' )[0].split('''_''' )[-1] )
           A_     =   os.path.join(self.inputdir    ,     A__ )
           return (img_id, filepath)

       @property
       def 					__A       (      self )		->     List[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(A__ ) )
               if len(A__ ) == self.batch:
                   A_     =   batch
                   A_     =   []
                   yield list(map(A__    ,     zip(*A__ ) ) )

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

       def __call__(      self )		->     Optional[int]:
           # 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(A__ )
               A_     =   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_     =   output_dict.pop('''normalized_boxes''' )
               if not TEST:
                   A_     =   np.array(A__ )
                   A_     =   self.schema.encode_batch(A__ )
                   writer.write_batch(A__ )
               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      _UpperCAmelCase    (							_UpperCamelCase    :	Any   )	->      Optional[Any]:




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












import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @cached_property
     def        __snake_case      (			self     :      Union[str, Any]	) ->				str:
           return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'	)




     @cached_property
     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'	)




     def        __snake_case      (			self     :      List[str]	) ->				int:
           lowerCamelCase__					     = self.tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      List[str]	) ->				str:
           lowerCamelCase__					     = self.rust_tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      Any	) ->				str:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]




     def        __snake_case      (			self     :      str	) ->				Union[str, Any]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]