





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										=					False
_UpperCAmelCase										=					Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_UpperCAmelCase										=					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:			List[str]       , _SCREAMING_SNAKE_CASE:			Any=sys.argv[1:]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	1
              UpperCamelCase_							,			UpperCamelCase_ 							=	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"):
                            UpperCamelCase_ 							=	arg
                     elif opt in ("-o", "--outfile"):
                            UpperCamelCase_ 							=	arg
                     elif opt in ("-b", "--batch_size"):
                            UpperCamelCase_ 							=	int(_SCREAMING_SNAKE_CASE		)
                     elif opt in ("-s", "--subset_list"):
                            UpperCamelCase_ 							=	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:
                            UpperCamelCase_ 							=	{self._vqa_file_split()[0] for x in tryload(_SCREAMING_SNAKE_CASE		)}
              else:
                     UpperCamelCase_ 							=	None

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

       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			List[Any]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	int(file.split("."		)[0].split("_"		)[-1]		)
              UpperCamelCase_ 							=	os.path.join(self.inputdir       , _SCREAMING_SNAKE_CASE		)
              return (img_id, filepath)

       @property
       def lowercase			(		self:			Union[str, Any]		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	[]
              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:
                            UpperCamelCase_ 							=	batch
                            UpperCamelCase_ 							=	[]
                            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:			List[str]		)							->			Tuple:




              """simple docstring"""


              if not TEST:
                     UpperCamelCase_ 							=	datasets.ArrowWriter(features=self.schema       , path=self.outputfile		)
              # do file generator
              for i, (img_ids, filepaths) in enumerate(self.file_generator		):
                     UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	self.preprocess(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	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"       , )
                     UpperCamelCase_ 							=	output_dict.pop("normalized_boxes"		)
                     if not TEST:
                            UpperCamelCase_ 							=	np.array(_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_ 							=	self.schema.encode_batch(_SCREAMING_SNAKE_CASE		)
                            writer.write_batch(_SCREAMING_SNAKE_CASE		)
                     if TEST:
                            break
                     # finalizer the writer
              if not TEST:
                     UpperCamelCase_							,			UpperCamelCase_ 							=	writer.finalize()
                     print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''		)




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Tuple:
       try:
              UpperCamelCase_ 							=	json.load(UpperCamelCase_	)
              try:
                     UpperCamelCase_ 							=	list(data.keys()	)
              except Exception:
                     UpperCamelCase_ 							=	[d["img_id"] for d in data]
       except Exception:
              try:
                     UpperCamelCase_ 							=	eval(stream.read()	)
              except Exception:
                     UpperCamelCase_ 							=	stream.read().split("\n"	)
       return data


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




import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


lowercase_									=    set()
lowercase_									=    get_logger(__name__)
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[str] = None )				-> Optional[int]:





    '''simple docstring'''






    def decorator(SCREAMING_SNAKE_CASE__     :      Callable ):
        global _emitted_deprecation_warnings

        if inspect.isclass(SCREAMING_SNAKE_CASE__ ):
            A__        =  deprecated_class_or_function.__init__
            A__        =  deprecated_class_or_function.__name__
        else:
            A__        =  deprecated_class_or_function
            A__        =  deprecated_function.__name__
            # Support deprecating __init__ class method: class name instead
            A__        =  name if name != '__init__' else deprecated_function.__qualname__.split('.' )[-2]

        A__        =  (
            f'{name} is deprecated and will be removed in the next major version of datasets.' + f' {help_message}'
            if help_message
            else ''
        )

        @wraps(SCREAMING_SNAKE_CASE__ )
        def wrapper(*SCREAMING_SNAKE_CASE__     :      Tuple       ,     **SCREAMING_SNAKE_CASE__     :      Any ):
            A__        =  hash(SCREAMING_SNAKE_CASE__ )
            if func_hash not in _emitted_deprecation_warnings:
                warnings.warn(SCREAMING_SNAKE_CASE__       ,     category=SCREAMING_SNAKE_CASE__       ,     stacklevel=2 )
                _emitted_deprecation_warnings.add(SCREAMING_SNAKE_CASE__ )
            return deprecated_function(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__ )

        A__        =  'deprecated'

        if inspect.isclass(SCREAMING_SNAKE_CASE__ ):
            A__        =  wrapper
            return deprecated_class_or_function
        else:
            return wrapper

    return decorator


class 					A		(				enum.EnumMeta					):







    """simple docstring"""





    def __getattribute__( cls					:			Optional[Any],lowercase_					:			int						)->       Optional[Any]:





        '''simple docstring'''







        A__        =  super().__getattribute__(lowercase_						)
        if isinstance(lowercase_,enum.Enum						) and obj._on_access:
            obj._on_access()
        return obj


    def __getitem__( cls					:			int,lowercase_					:			Any						)->       Tuple:





        '''simple docstring'''







        A__        =  super().__getitem__(lowercase_						)
        if member._on_access:
            member._on_access()
        return member


    def __call__( cls					:			Optional[int],lowercase_					:			Tuple,lowercase_					:			List[Any]=None,*, lowercase_					:			Any=None,lowercase_					:			Optional[Any]=None,lowercase_					:			Any=None,lowercase_					:			Optional[int]=1						)->       List[str]:





        '''simple docstring'''







        A__        =  super().__call__(lowercase_,lowercase_,module=lowercase_,qualname=lowercase_,type=lowercase_,start=lowercase_						)
        if isinstance(lowercase_,enum.Enum						) and obj._on_access:
            obj._on_access()
        return obj




class 					A		(				enum.Enum       ,				metaclass=_UpperCAmelCase					):







    """simple docstring"""





    def __new__( cls					:			Any,lowercase_					:			str						)->       Any:





        '''simple docstring'''







        A__        =  object.__new__(cls						)
        A__        =  value
        A__        =  member.deprecate
        return member


    @property
    def 		snake_case__	( self					:			List[Any]						)->       List[Any]:





        '''simple docstring'''







        return ""


    def 		snake_case__	( self					:			List[str]						)->       Dict:





        '''simple docstring'''







        A__        =  F' {self.help_message}' if self.help_message else ''
        warnings.warn(
            F'\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'
            + help_message,lowercase_,stacklevel=3,)

