




import math




class        snake_case__				:
				"""simple docstring"""



				def __init__( self							:	int   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	Tuple				)   ->	Optional[Any]:
							a       =     x
							a       =     y
							a       =     step_size
							a       =     function_to_optimize





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	int:
							return self.function(self.x   , self.y				)





				def 				__UpperCAmelCase				( self							:	int				)   ->	List[Any]:
							a       =     self.step_size
							return [
							    SearchProblem(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , self.function				)
							    for x, y in (
							        (self.x - step_size, self.y - step_size),
							        (self.x - step_size, self.y),
							        (self.x - step_size, self.y + step_size),
							        (self.x, self.y - step_size),
							        (self.x, self.y + step_size),
							        (self.x + step_size, self.y - step_size),
							        (self.x + step_size, self.y),
							        (self.x + step_size, self.y + step_size),
							    )
							]





				def __hash__( self							:	Tuple				)   ->	List[str]:
							return hash(str(self				)				)





				def __eq__( self							:	Dict   , __lowerCamelCase							:	int				)   ->	Optional[int]:
							if isinstance(__lowerCamelCase   , __lowerCamelCase				):
										return hash(str(self				)				) == hash(str(__lowerCamelCase				)				)
							return False





				def __str__( self							:	Union[str, Any]				)   ->	List[Any]:
							return f"""x: {self.x} y: {self.y}"""




def      __magic_name__							(					A						:   Dict,    A						:   Union[str, Any] = True,    A						:   List[str] = math.inf,    A						:   str = -math.inf,    A						:   List[Any] = math.inf,    A						:   Optional[int] = -math.inf,    A						:   Dict = False,    A						:   Dict = 10000,    ):






			'''simple docstring'''







			a       =     search_prob
			a       =     []  # list to store the current score at each iteration
			a       =     0
			a       =     False
			a       =     set()
			while not solution_found and iterations < max_iter:
						visited.add(A  )
						iterations += 1
						a       =     current_state.score()
						scores.append(A  )
						a       =     current_state.get_neighbors()
						a       =     -math.inf
						a       =     math.inf
						a       =     None  # to hold the next best neighbor
						for neighbor in neighbors:
									if neighbor in visited:
												continue  # do not want to visit the same state again
									if (
									    neighbor.x > max_x
									    or neighbor.x < min_x
									    or neighbor.y > max_y
									    or neighbor.y < min_y
									):
												continue  # neighbor outside our bounds
									a       =     neighbor.score() - current_score
									if find_max:  # finding max
												# going to direction with greatest ascent
												if change > max_change and change > 0:
															a       =     change
															a       =     neighbor
									else:  # finding min
												# to direction with greatest descent
												if change < min_change and change < 0:
															a       =     change
															a       =     neighbor
						if next_state is not None:
									# we found at least one neighbor which improved the current state
									a       =     next_state
						else:
									# since we have no neighbor that improves the solution we stop the search
									a       =     True

			if visualization:
						from matplotlib import pyplot as plt

						plt.plot(range(A  ),    A  )
						plt.xlabel("Iterations"  )
						plt.ylabel("Function values"  )
						plt.show()

			return current_state


if __name__ == "__main__":
		import doctest

		doctest.testmod()




		def      __magic_name__							(					A						:   Optional[Any],    A						:   Tuple  ):






					'''simple docstring'''







					return (x**2) + (y**2)

		# starting the problem with initial coordinates (3, 4)
		__lowerCAmelCase						:							str			     =						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		__lowerCAmelCase						:							Any			     =						hill_climbing(prob, find_max=False)
		print(
		    'The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '
		    F'''{local_min.score()}'''
		)

		# starting the problem with initial coordinates (12, 47)
		__lowerCAmelCase						:							List[Any]			     =						SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		__lowerCAmelCase						:							str			     =						hill_climbing(
		    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
		)
		print(
		    'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
		    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
		)




		def      __magic_name__							(					A						:   Optional[int],    A						:   List[Any]  ):






					'''simple docstring'''







					return (3 * x**2) - (6 * y)

		__lowerCAmelCase						:							Union[str, Any]			     =						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		__lowerCAmelCase						:							List[Any]			     =						hill_climbing(prob, find_max=True)






		print(
		    'The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '
		    F'''{local_min.score()}'''
		)








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)





