
def  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase					)  ->    Tuple:
		'''simple docstring'''

		if start == end:
				return start if lst[start] > item else start + 1
		if start > end:
				return start

		UpperCamelCase            =	(start + end) // 2
		if lst[mid] < item:
				return binary_search(lowercase       ,			lowercase       ,			mid + 1       ,			lowercase					)
		elif lst[mid] > item:
				return binary_search(lowercase       ,			lowercase       ,			lowercase       ,			mid - 1					)
		else:
				return mid


def  A			(   lowercase					)  ->    Tuple:
		'''simple docstring'''

		UpperCamelCase            =	len(lowercase					)

		for index in range(1       ,			lowercase					):
				UpperCamelCase            =	lst[index]
				UpperCamelCase            =	binary_search(lowercase       ,			lowercase       ,			0       ,			index - 1					)
				UpperCamelCase            =	lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

		return lst


def  A			(   lowercase       ,			lowercase					)  ->    Any:
		'''simple docstring'''

		if not left:
				return right

		if not right:
				return left

		if left[0] < right[0]:
				return [left[0], *merge(left[1:]       ,			lowercase					)]

		return [right[0], *merge(lowercase       ,			right[1:]					)]


def  A			(   lowercase					)  ->    List[str]:
		'''simple docstring'''

		UpperCamelCase            =	len(lowercase					)
		UpperCamelCase            =	[], []
		UpperCamelCase            =	[lst[0]]
		UpperCamelCase            =	[]
		UpperCamelCase            =	1
		while i < length:
				if lst[i] < lst[i - 1]:
						runs.append(lowercase					)
						UpperCamelCase            =	[lst[i]]
				else:
						new_run.append(lst[i]					)
				i += 1
		runs.append(lowercase					)

		for run in runs:
				sorted_runs.append(insertion_sort(lowercase					)					)
		for run in sorted_runs:
				UpperCamelCase            =	merge(lowercase       ,			lowercase					)

		return sorted_array


def  A			(   )  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	[5, 9, 10, 3, -4, 5, 178, 92, 46, -18, 0, 7]
		UpperCamelCase            =	tim_sort(lowercase					)
		print(lowercase					)


if __name__ == "__main__":
					main()


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
__snake_case					:							Optional[int]           =							False
__snake_case					:							Union[str, Any]           =							Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__snake_case					:							Optional[int]           =							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__       :

						'''simple docstring'''



						def __init__(  self  , A=sys.argv[1:]	)  ->Optional[Any]:
									UpperCAmelCase__		:Optional[Any] 		=    None
									UpperCAmelCase__		:str 		=    None
									UpperCAmelCase__		:Optional[Any] 		=    None
									UpperCAmelCase__		:List[Any] 		=    1
									UpperCAmelCase__		,       UpperCAmelCase__		:int 		=    getopt.getopt(A  , 'i:o:b:s'  , ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']	)
									for opt, arg in opts:
												if opt in ("-i", "--inputdir"):
															UpperCAmelCase__		:Optional[int] 		=    arg
												elif opt in ("-o", "--outfile"):
															UpperCAmelCase__		:str 		=    arg
												elif opt in ("-b", "--batch_size"):
															UpperCAmelCase__		:List[Any] 		=    int(A	)
												elif opt in ("-s", "--subset_list"):
															UpperCAmelCase__		:str 		=    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__		:Any 		=    {self._vqa_file_split()[0] for x in tryload(A	)}
									else:
												UpperCAmelCase__		:Any 		=    None

									UpperCAmelCase__		:List[str] 		=    CONFIG
									if torch.cuda.is_available():
												UpperCAmelCase__		:Optional[Any] 		=    'cuda'
									UpperCAmelCase__		:Dict 		=    os.path.realpath(A	)
									UpperCAmelCase__		:List[Any] 		=    os.path.realpath(A	)
									UpperCAmelCase__		:List[Any] 		=    Preprocess(self.config	)
									UpperCAmelCase__		:Union[str, Any] 		=    GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'  , config=self.config	)
									UpperCAmelCase__		:Tuple 		=    batch_size if batch_size != 0 else 1
									UpperCAmelCase__		:List[str] 		=    DEFAULT_SCHEMA


						def    A__       (  self  , A	)  ->Optional[Any]:
									UpperCAmelCase__		:List[str] 		=    int(file.split('.'	)[0].split('_'	)[-1]	)
									UpperCAmelCase__		:Any 		=    os.path.join(self.inputdir  , A	)
									return (img_id, filepath)


						@property
						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:str 		=    []
									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__		:Optional[Any] 		=    batch
															UpperCAmelCase__		:Tuple 		=    []
															yield list(map(A  , zip(*A	)	)	)

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


						def __call__(  self	)  ->List[Any]:
									# make writer
									if not TEST:
												UpperCAmelCase__		:List[Any] 		=    datasets.ArrowWriter(features=self.schema  , path=self.outputfile	)
									# do file generator
									for i, (img_ids, filepaths) in enumerate(self.file_generator	):
												UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__		:Any 		=    self.preprocess(A	)
												UpperCAmelCase__		:Tuple 		=    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__		:Dict 		=    output_dict.pop('normalized_boxes'	)
												if not TEST:
															UpperCAmelCase__		:Union[str, Any] 		=    np.array(A	)
															UpperCAmelCase__		:Any 		=    self.schema.encode_batch(A	)
															writer.write_batch(A	)
												if TEST:
															break
												# finalizer the writer
									if not TEST:
												UpperCAmelCase__		,       UpperCAmelCase__		:Optional[Any] 		=    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__		:List[Any] 		=    json.load(SCREAMING_SNAKE_CASE							)
						try:
									UpperCAmelCase__		:Optional[Any] 		=    list(data.keys()							)
						except Exception:
									UpperCAmelCase__		:List[str] 		=    [d['img_id'] for d in data]
			except Exception:
						try:
									UpperCAmelCase__		:str 		=    eval(stream.read()							)
						except Exception:
									UpperCAmelCase__		:Optional[Any] 		=    stream.read().split('\n'							)
			return data


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





