



def     __lowerCamelCase							(   __magic_name__						:	list[list[int]]    ):
							if len(__magic_name__    ) == 0 or not all(len(__magic_name__    ) == 2 for pair in coordinates    ):
														raise ValueError("The program cannot work out a fitting polynomial."    )

							if len({tuple(__magic_name__    ) for pair in coordinates}    ) != len(__magic_name__    ):
														raise ValueError("The program cannot work out a fitting polynomial."    )

							a__:							List[Any]									={x for x, _ in coordinates}
							if len(__magic_name__    ) == 1:
														return F"x={coordinates[0][0]}"

							if len(__magic_name__    ) != len(__magic_name__    ):
														raise ValueError("The program cannot work out a fitting polynomial."    )

							a__:							int									=len(__magic_name__    )

							a__:							str									=0
							a__:							list[list[float]]									=[]
							# put the x and x to the power values in a matrix
							while count_of_line < x:
														a__:							Optional[int]									=0
														a__:							str									=coordinates[count_of_line][0]
														a__:							list[float]									=[]
														while count_in_line < x:
																					count_line.append(a ** (x - (count_in_line + 1))    )
																					count_in_line += 1
														matrix.append(__magic_name__    )
														count_of_line += 1

							a__:							Tuple									=0
							# put the y values into a vector
							a__:							list[float]									=[]
							while count_of_line < x:
														vector.append(coordinates[count_of_line][1]    )
														count_of_line += 1

							a__:							Dict									=0

							while count < x:
														a__:							List[Any]									=0
														while zahlen < x:
																					if count == zahlen:
																												zahlen += 1
																					if zahlen == x:
																												break
																					a__:							Optional[int]									=matrix[zahlen][count] / matrix[count][count]
																					for counting_columns, item in enumerate(matrix[count]    ):
																												# manipulating all the values in the matrix
																												matrix[zahlen][counting_columns] -= item * bruch
																					# manipulating the values in the vector
																					vector[zahlen] -= vector[count] * bruch
																					zahlen += 1
														count += 1

							a__:							Optional[Any]									=0
							# make solutions
							a__:							list[str]									=[]
							while count < x:
														solution.append(str(vector[count] / matrix[count][count]    )    )
														count += 1

							a__:							int									=0
							a__:							Optional[int]									="f(x)="

							while count < x:
														a__:							list[str]									=solution[count].split("E"    )
														if len(__magic_name__    ) > 1:
																					a__:							Optional[Any]									=F"{remove_e[0]}*10^{remove_e[1]}"
														solved += F"x^{x - (count + 1)}*{solution[count]}"
														if count + 1 != x:
																					solved += "+"
														count += 1

							return solved


if __name__ == "__main__":
		print(points_to_polynomial([]))
		print(points_to_polynomial([[]]))
		print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
		print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
		print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
		print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
		print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
		print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
		print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
		print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))










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, 20_48), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)


class 						lowerCamelCase__			:


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

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


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Dict      ):
													a__:							Tuple									=int(file.split("."      )[0].split("_"      )[-1]      )
													a__:							int									=os.path.join(self.inputdir   ,							_a      )
													return (img_id, filepath)


						@property
						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							List[Any]									=[]
													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__:							List[Any]									=batch
																											a__:							Union[str, Any]									=[]
																											yield list(map(_a   ,							zip(*_a      )      )      )

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







						def __call__(			self    :	List[str]      ):
													# make writer
													if not TEST:
																				a__:							int									=datasets.ArrowWriter(features=self.schema   ,							path=self.outputfile      )
													# do file generator
													for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
																				a__				,      a__				,      a__:							Dict									=self.preprocess(_a      )
																				a__:							Optional[Any]									=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__:							Dict									=output_dict.pop("normalized_boxes"      )
																				if not TEST:
																											a__:							Tuple									=np.array(_a      )
																											a__:							Union[str, Any]									=self.schema.encode_batch(_a      )
																											writer.write_batch(_a      )
																				if TEST:
																											break
																				# finalizer the writer
													if not TEST:
																				a__				,      a__:							Any									=writer.finalize()
																				print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"      )





def     __lowerCamelCase							(   __magic_name__						:	Union[str, Any]    ):
							try:
														a__:							Any									=json.load(__magic_name__    )
														try:
																					a__:							List[str]									=list(data.keys()    )
														except Exception:
																					a__:							Union[str, Any]									=[d["img_id"] for d in data]
							except Exception:
														try:
																					a__:							Dict									=eval(stream.read()    )
														except Exception:
																					a__:							int									=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)





