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





							def __init__(					self    :  Dict	,					_UpperCAmelCase    :  Any=sys.argv[1:]							)		->						int:



											"""simple docstring"""


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

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





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  List[str]							)		->						List[str]:



											"""simple docstring"""


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





							@property
							def      a__					(					self    :  Optional[Any]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   []
											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:
																			__lowercase								=   batch
																			__lowercase								=   []
																			yield list(map(_UpperCAmelCase	,					zip(*_UpperCAmelCase							)							)							)

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





							def __call__(					self    :  Union[str, Any]							)		->						Optional[int]:



											"""simple docstring"""


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






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int     )	->		Dict:
				try:
								__lowercase								=   json.load(SCREAMING_SNAKE_CASE     )
								try:
												__lowercase								=   list(data.keys()     )
								except Exception:
												__lowercase								=   [d['img_id'] for d in data]
				except Exception:
								try:
												__lowercase								=   eval(stream.read()     )
								except Exception:
												__lowercase								=   stream.read().split('\n'     )
				return data


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







def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Union[str, Any]       ,				SCREAMING_SNAKE_CASE			: Any     )	->		Tuple:
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				__lowercase								=   (boundary[1] - boundary[0]) / steps
				__lowercase								=   boundary[0]
				__lowercase								=   boundary[1]
				__lowercase								=   make_points(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				__lowercase								=   0.0
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				__lowercase								=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE     )
								cnt += 1
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				return y




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: Dict       ,				SCREAMING_SNAKE_CASE			: int     )	->		Any:
				__lowercase								=   a + h
				while x < (b - h):
								yield x
								__lowercase								=   x + h




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any     )	->		List[Any]:  # enter your function here
				__lowercase								=   (x - 0) * (x - 0)
				return y




def 				__SCREAMING_SNAKE_CASE  (						)	->		List[str]:
				__lowercase								=   0.0  # Lower bound of integration
				__lowercase								=   1.0  # Upper bound of integration
				__lowercase								=   10.0  # define number of steps or resolution
				__lowercase								=   [a, b]  # define boundary of integration
				__lowercase								=   method_a(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				print(F"""y = {y}"""     )


if __name__ == "__main__":
							main()





