






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												=      False
UpperCamelCase												=      Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCamelCase												=      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  __UpperCAmelCase						:







			def __init__(      self:    Optional[int] ,      UpperCAmelCase_:    Any=sys.argv[1:]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    None
									_SCREAMING_SNAKE_CASE       =    None
									_SCREAMING_SNAKE_CASE       =    None
									_SCREAMING_SNAKE_CASE       =    1
									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    getopt.getopt(UpperCAmelCase_ ,      """i:o:b:s""" ,      ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]	)
									for opt, arg in opts:
															if opt in ("-i", "--inputdir"):
																					_SCREAMING_SNAKE_CASE       =    arg
															elif opt in ("-o", "--outfile"):
																					_SCREAMING_SNAKE_CASE       =    arg
															elif opt in ("-b", "--batch_size"):
																					_SCREAMING_SNAKE_CASE       =    int(UpperCAmelCase_	)
															elif opt in ("-s", "--subset_list"):
																					_SCREAMING_SNAKE_CASE       =    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:
																					_SCREAMING_SNAKE_CASE       =    {self._vqa_file_split()[0] for x in tryload(UpperCAmelCase_	)}
									else:
															_SCREAMING_SNAKE_CASE       =    None

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







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




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







			@property
			def 				UpperCamelCase (      self:    Optional[Any]	):







									'''simple docstring'''




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

def 			__lowerCamelCase     (						snake_case__						)				->    Optional[Any]:






						"""simple docstring"""





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


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









from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.27.0''')):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import (
		    AudioLDMPipeline,
		)
else:
		from .pipeline_audioldm import AudioLDMPipeline

