

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
A__							=				False
A__							=				Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
A__							=				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					:







	def __init__( self:			int	,			__UpperCamelCase:			Union[str, Any]=sys.argv[1:]   ):

						'''simple docstring'''





						__magic_name__				=     None
						__magic_name__				=     None
						__magic_name__				=     None
						__magic_name__				=     1
						__magic_name__,						__magic_name__				=     getopt.getopt(__UpperCamelCase	,			'i:o:b:s'	,			['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']   )
						for opt, arg in opts:
											if opt in ("-i", "--inputdir"):
																__magic_name__				=     arg
											elif opt in ("-o", "--outfile"):
																__magic_name__				=     arg
											elif opt in ("-b", "--batch_size"):
																__magic_name__				=     int(__UpperCamelCase   )
											elif opt in ("-s", "--subset_list"):
																__magic_name__				=     arg

						assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
						assert outputfile is not None and not os.path.isfile(__UpperCamelCase   ), F'{outputfile}'
						if subset_list is not None:
											with open(os.path.realpath(__UpperCamelCase   )   ) as f:
																__magic_name__				=     {self._vqa_file_split()[0] for x in tryload(__UpperCamelCase   )}
						else:
											__magic_name__				=     None

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     int(file.split('.'   )[0].split('_'   )[-1]   )
						__magic_name__				=     os.path.join(self.inputdir	,			__UpperCamelCase   )
						return (img_id, filepath)







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						__magic_name__				=     []
						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(__UpperCamelCase   )   )
											if len(__UpperCamelCase   ) == self.batch:
																__magic_name__				=     batch
																__magic_name__				=     []
																yield list(map(__UpperCamelCase	,			zip(*__UpperCamelCase   )   )   )

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







	def __call__( self:			Any   ):

						'''simple docstring'''





						if not TEST:
											__magic_name__				=     datasets.ArrowWriter(features=self.schema	,			path=self.outputfile   )
						# do file generator
						for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
											__magic_name__,						__magic_name__,						__magic_name__				=     self.preprocess(__UpperCamelCase   )
											__magic_name__				=     self.model(
											    __UpperCamelCase	,			__UpperCamelCase	,			scales_yx=__UpperCamelCase	,			padding='max_detections'	,			max_detections=self.config.MAX_DETECTIONS	,			pad_value=0	,			return_tensors='np'	,			location='cpu'	,			)
											__magic_name__				=     output_dict.pop('normalized_boxes'   )
											if not TEST:
																__magic_name__				=     np.array(__UpperCamelCase   )
																__magic_name__				=     self.schema.encode_batch(__UpperCamelCase   )
																writer.write_batch(__UpperCamelCase   )
											if TEST:
																break
											# finalizer the writer
						if not TEST:
											__magic_name__,						__magic_name__				=     writer.finalize()
											print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'   )




def        _lowercase  (					a_					:       Optional[Any]					)					->						str:




					'''simple docstring'''







					try:
										__magic_name__				=     json.load(a_					)
										try:
															__magic_name__				=     list(data.keys()					)
										except Exception:
															__magic_name__				=     [d['img_id'] for d in data]
					except Exception:
										try:
															__magic_name__				=     eval(stream.read()					)
										except Exception:
															__magic_name__				=     stream.read().split('\n'					)
					return data


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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__							=				{"configuration_pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				["PegasusTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				["PegasusTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PegasusForCausalLM",
						    "PegasusForConditionalGeneration",
						    "PegasusModel",
						    "PegasusPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "TFPegasusForConditionalGeneration",
						    "TFPegasusModel",
						    "TFPegasusPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "FlaxPegasusForConditionalGeneration",
						    "FlaxPegasusModel",
						    "FlaxPegasusPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus import PegasusTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus_fast import PegasusTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_pegasus import (
												    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PegasusForCausalLM,
												    PegasusForConditionalGeneration,
												    PegasusModel,
												    PegasusPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_pegasus import (
												    FlaxPegasusForConditionalGeneration,
												    FlaxPegasusModel,
												    FlaxPegasusPreTrainedModel,
												)

else:
						import sys

						A__							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




