






'''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
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, 2_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    Tuple				,   a:    Dict=sys.argv[1:])  ->List[Any]:






								'''simple docstring'''


								a_				     =   None
								a_				     =   None
								a_				     =   None
								a_				     =   1
								a_     ,       a_				     =   getopt.getopt(a				,   "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(a)
														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(a), f"""{outputfile}"""
								if subset_list is not None:
														with open(os.path.realpath(a)) as f:
																				a_				     =   {self._vqa_file_split()[0] for x in tryload(a)}
								else:
														a_				     =   None

								a_				     =   CONFIG
								if torch.cuda.is_available():
														a_				     =   "cuda"
								a_				     =   os.path.realpath(a)
								a_				     =   os.path.realpath(a)
								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 _lowerCAmelCase     (    self:    List[Any]				,   a:    Dict)  ->Optional[int]:






								'''simple docstring'''


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



		@property
		def _lowerCAmelCase     (    self:    Optional[Any])  ->str:






								'''simple docstring'''


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

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




		def __call__(    self:    Optional[Any])  ->List[Any]:






								'''simple docstring'''


								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(a)
														a_				     =   self.model(
														    a				,   a				,   scales_yx=a				,   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(a)
																				a_				     =   self.schema.encode_batch(a)
																				writer.write_batch(a)
														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 			__UpperCAmelCase				(lowercase__				)		->				Union[str, Any]:

						'''simple docstring'''


						try:
												a_				     =   json.load(lowercase__				)
												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__":
							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)











'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_      = logging.get_logger(__name__)

a_      = {
    'google/mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'
}





class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):
		_UpperCAmelCase         =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		_UpperCAmelCase         ='''mobilebert'''



		def __init__(    self:    Union[str, Any]				,   a:    str=3_05_22				,   a:    List[str]=5_12				,   a:    List[str]=24				,   a:    Dict=4				,   a:    List[str]=5_12				,   a:    Optional[int]="relu"				,   a:    Dict=0.0				,   a:    Union[str, Any]=0.1				,   a:    int=5_12				,   a:    Optional[Any]=2				,   a:    List[Any]=0.02				,   a:    Optional[Any]=1e-12				,   a:    List[Any]=0				,   a:    Optional[int]=1_28				,   a:    Union[str, Any]=True				,   a:    Dict=True				,   a:    List[Any]=1_28				,   a:    Any=False				,   a:    Any=True				,   a:    Dict=4				,   a:    List[Any]="no_norm"				,   a:    Any=True				,   a:    Tuple=None				,   **a:    Any				,   )  ->Any:






								'''simple docstring'''


								super().__init__(pad_token_id=a				,   **a)

								a_				     =   vocab_size
								a_				     =   hidden_size
								a_				     =   num_hidden_layers
								a_				     =   num_attention_heads
								a_				     =   hidden_act
								a_				     =   intermediate_size
								a_				     =   hidden_dropout_prob
								a_				     =   attention_probs_dropout_prob
								a_				     =   max_position_embeddings
								a_				     =   type_vocab_size
								a_				     =   initializer_range
								a_				     =   layer_norm_eps
								a_				     =   embedding_size
								a_				     =   trigram_input
								a_				     =   use_bottleneck
								a_				     =   intra_bottleneck_size
								a_				     =   use_bottleneck_attention
								a_				     =   key_query_shared_bottleneck
								a_				     =   num_feedforward_networks
								a_				     =   normalization_type
								a_				     =   classifier_activation

								if self.use_bottleneck:
														a_				     =   intra_bottleneck_size
								else:
														a_				     =   hidden_size

								a_				     =   classifier_dropout





class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):



		@property
		def _lowerCAmelCase     (    self:    Optional[int])  ->Mapping[str, Mapping[int, str]]:






								'''simple docstring'''


								if self.task == "multiple-choice":
														a_				     =   {0: "batch", 1: "choice", 2: "sequence"}
								else:
														a_				     =   {0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ])



