
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__				:   Optional[Any]   							=			False
UpperCAmelCase__				:   Optional[int]   							=			Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
UpperCAmelCase__				:   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, 2_0_4_8), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)



class      lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self       ,	SCREAMING_SNAKE_CASE__=sys.argv[1:]   ) ->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       None
											SCREAMING_SNAKE_CASE__						:	Tuple									=       None
											SCREAMING_SNAKE_CASE__						:	int									=       None
											SCREAMING_SNAKE_CASE__						:	Any									=       1
											SCREAMING_SNAKE_CASE__						:	Dict									=       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"):
																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       arg
																		elif opt in ("-o", "--outfile"):
																									SCREAMING_SNAKE_CASE__						:	str									=       arg
																		elif opt in ("-b", "--batch_size"):
																									SCREAMING_SNAKE_CASE__						:	List[Any]									=       int(SCREAMING_SNAKE_CASE__   )
																		elif opt in ("-s", "--subset_list"):
																									SCREAMING_SNAKE_CASE__						:	Any									=       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:
																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE__   )}
											else:
																		SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       None

											SCREAMING_SNAKE_CASE__						:	List[str]									=       CONFIG
											if torch.cuda.is_available():
																		SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       "cuda"
											SCREAMING_SNAKE_CASE__						:	Tuple									=       os.path.realpath(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	List[str]									=       os.path.realpath(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	str									=       Preprocess(self.config   )
											SCREAMING_SNAKE_CASE__						:	int									=       GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""       ,	config=self.config   )
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       batch_size if batch_size != 0 else 1
											SCREAMING_SNAKE_CASE__						:	Dict									=       DEFAULT_SCHEMA



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Dict									=       int(file.split("""."""   )[0].split("""_"""   )[-1]   )
											SCREAMING_SNAKE_CASE__						:	Any									=       os.path.join(self.inputdir       ,	SCREAMING_SNAKE_CASE__   )
											return (img_id, filepath)



				@property
				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       []
											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:
																									SCREAMING_SNAKE_CASE__						:	Any									=       batch
																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       []
																									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:
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       datasets.ArrowWriter(features=self.schema       ,	path=self.outputfile   )
											# do file generator
											for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       self.preprocess(SCREAMING_SNAKE_CASE__   )
																		SCREAMING_SNAKE_CASE__						:	Dict									=       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"""       ,	)
																		SCREAMING_SNAKE_CASE__						:	Any									=       output_dict.pop("""normalized_boxes"""   )
																		if not TEST:
																									SCREAMING_SNAKE_CASE__						:	List[Any]									=       np.array(SCREAMING_SNAKE_CASE__   )
																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.schema.encode_batch(SCREAMING_SNAKE_CASE__   )
																									writer.write_batch(SCREAMING_SNAKE_CASE__   )
																		if TEST:
																									break
																		# finalizer the writer
											if not TEST:
																		SCREAMING_SNAKE_CASE__						:	str									=       writer.finalize()
																		print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''   )






def lowercase_  (       _snake_case						):
							try:
														SCREAMING_SNAKE_CASE__						:	List[str]									=       json.load(_snake_case						)
														try:
																					SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       list(data.keys()						)
														except Exception:
																					SCREAMING_SNAKE_CASE__						:	Any									=       [d["img_id"] for d in data]
							except Exception:
														try:
																					SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       eval(stream.read()						)
														except Exception:
																					SCREAMING_SNAKE_CASE__						:	Any									=       stream.read().split("""\n"""						)
							return data


if __name__ == "__main__":
					UpperCAmelCase__				:   Optional[int]   							=			Extract(sys.argv[1:])
					extract()
					if not TEST:
										UpperCAmelCase__				:   Optional[int]   							=			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_torch_available


UpperCAmelCase__				:   Optional[Any]   							=			{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Tuple   							=			[
					    'UMT5EncoderModel',
					    'UMT5ForConditionalGeneration',
					    'UMT5ForQuestionAnswering',
					    'UMT5Model',
					    'UMT5PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_umta import UMTaConfig, UMTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_umta import (
										    UMTaEncoderModel,
										    UMTaForConditionalGeneration,
										    UMTaForQuestionAnswering,
										    UMTaModel,
										    UMTaPreTrainedModel,
										)
else:
					import sys

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




