





'''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
_lowerCamelCase					:						Union[str, Any]					     =							False
_lowerCamelCase					:						List[str]					     =							Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_lowerCamelCase					:						Dict					     =							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, 2048), 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    :   Optional[int]   ,   _lowerCAmelCase    :   Any=sys.argv[1:]   ):
			A					=   None
			A					=   None
			A					=   None
			A					=   1
			A	,     A					=   getopt.getopt(_lowerCAmelCase   ,   """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(_lowerCAmelCase   )
				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(_lowerCAmelCase   ), F"""{outputfile}"""
			if subset_list is not None:
				with open(os.path.realpath(_lowerCAmelCase   )   ) as f:
					A					=   {self._vqa_file_split()[0] for x in tryload(_lowerCAmelCase   )}
			else:
				A					=   None

			A					=   CONFIG
			if torch.cuda.is_available():
				A					=   """cuda"""
			A					=   os.path.realpath(_lowerCAmelCase   )
			A					=   os.path.realpath(_lowerCAmelCase   )
			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    :   Optional[Any]   ,   _lowerCAmelCase    :   int   ):
			A					=   int(file.split("""."""   )[0].split("""_"""   )[-1]   )
			A					=   os.path.join(self.inputdir   ,   _lowerCAmelCase   )
			return (img_id, filepath)







		@property
		def 							A  (self    :   List[str]   ):
			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(_lowerCAmelCase   )   )
				if len(_lowerCAmelCase   ) == self.batch:
					A					=   batch
					A					=   []
					yield list(map(_lowerCAmelCase   ,   zip(*_lowerCAmelCase   )   )   )

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







		def __call__(self    :   Any   ):
			# 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(_lowerCAmelCase   )
				A					=   self.model(
				    _lowerCAmelCase   ,   _lowerCAmelCase   ,   scales_yx=_lowerCAmelCase   ,   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(_lowerCAmelCase   )
					A					=   self.schema.encode_batch(_lowerCAmelCase   )
					writer.write_batch(_lowerCAmelCase   )
				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  __a			(  UpperCAmelCase     )   ->str:



	"""simple docstring"""







	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__":
				_lowerCamelCase					:						Optional[int]					     =							Extract(sys.argv[1:])
				extract()
				if not TEST:
								_lowerCamelCase					:						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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCamelCase					:						str					     =							{
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						List[str]					     =							['ErnieMTokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						Union[str, Any]					     =							[
				    'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ErnieMForMultipleChoice',
				    'ErnieMForQuestionAnswering',
				    'ErnieMForSequenceClassification',
				    'ErnieMForTokenClassification',
				    'ErnieMModel',
				    'ErnieMPreTrainedModel',
				    'ErnieMForInformationExtraction',
				]


if TYPE_CHECKING:
				from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ernie_m import ErnieMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ernie_m import (
								    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ErnieMForInformationExtraction,
								    ErnieMForMultipleChoice,
								    ErnieMForQuestionAnswering,
								    ErnieMForSequenceClassification,
								    ErnieMForTokenClassification,
								    ErnieMModel,
								    ErnieMPreTrainedModel,
								)


else:
				import sys

				_lowerCamelCase					:						int					     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

