





"""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__        =      False
lowerCAmelCase__        =      Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
lowerCAmelCase__        =      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 							_lowerCamelCase   :


						def __init__(self ,				__a=sys.argv[1:]		)			-> Dict:
								UpperCamelCase =						None
								UpperCamelCase =						None
								UpperCamelCase =						None
								UpperCamelCase =						1
								UpperCamelCase ,					UpperCamelCase =						getopt.getopt(__a ,				"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(__a		)
										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(__a		), F"{outputfile}"
								if subset_list is not None:
										with open(os.path.realpath(__a		)		) as f:
												UpperCamelCase =						{self._vqa_file_split()[0] for x in tryload(__a		)}
								else:
										UpperCamelCase =						None

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


						@property
						def      snake_case_ (self		)			-> Optional[int]:
								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(__a		)		)
										if len(__a		) == self.batch:
												UpperCamelCase =						batch
												UpperCamelCase =						[]
												yield list(map(__a ,				zip(*__a		)		)		)

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




						def __call__(self		)			-> str:
								# make writer
								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(__a		)
										UpperCamelCase =						self.model(
										    __a ,				__a ,				scales_yx=__a ,				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(__a		)
												UpperCamelCase =						self.schema.encode_batch(__a		)
												writer.write_batch(__a		)
										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    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		try:
				UpperCamelCase =						json.load(_SCREAMING_SNAKE_CASE     )
				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__":
						lowerCAmelCase__        =      Extract(sys.argv[1:])
						extract()
						if not TEST:
												lowerCAmelCase__        =      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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__        =      {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      ['''DebertaV2TokenizerFast''']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFDebertaV2ForMaskedLM''',
						    '''TFDebertaV2ForQuestionAnswering''',
						    '''TFDebertaV2ForSequenceClassification''',
						    '''TFDebertaV2ForTokenClassification''',
						    '''TFDebertaV2Model''',
						    '''TFDebertaV2PreTrainedModel''',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DebertaV2ForMaskedLM''',
						    '''DebertaV2ForMultipleChoice''',
						    '''DebertaV2ForQuestionAnswering''',
						    '''DebertaV2ForSequenceClassification''',
						    '''DebertaV2ForTokenClassification''',
						    '''DebertaV2Model''',
						    '''DebertaV2PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deberta_va import (
						    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DebertaVaConfig,
						    DebertaVaOnnxConfig,
						)
						from .tokenization_deberta_va import DebertaVaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta_va import (
												    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaVaForMaskedLM,
												    TFDebertaVaForQuestionAnswering,
												    TFDebertaVaForSequenceClassification,
												    TFDebertaVaForTokenClassification,
												    TFDebertaVaModel,
												    TFDebertaVaPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta_va import (
												    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaVaForMaskedLM,
												    DebertaVaForMultipleChoice,
												    DebertaVaForQuestionAnswering,
												    DebertaVaForSequenceClassification,
												    DebertaVaForTokenClassification,
												    DebertaVaModel,
												    DebertaVaPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase__        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






