
"""simple docstring"""


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



							"""simple docstring"""






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





										"""simple docstring"""

										lowerCamelCase__			:  Union[str, Any]  =None
										lowerCamelCase__			:  Optional[int]  =None
										lowerCamelCase__			:  Any  =None
										lowerCamelCase__			:  Tuple  =1
										lowerCamelCase__ ,       lowerCamelCase__			:  int  =getopt.getopt(lowerCamelCase_  ,							'i:o:b:s'  ,							['inputdir=', 'outfile=', 'batch_size=', 'subset_list='] )
										for opt, arg in opts:
													if opt in ("-i", "--inputdir"):
																lowerCamelCase__			:  str  =arg
													elif opt in ("-o", "--outfile"):
																lowerCamelCase__			:  Optional[int]  =arg
													elif opt in ("-b", "--batch_size"):
																lowerCamelCase__			:  Optional[int]  =int(lowerCamelCase_ )
													elif opt in ("-s", "--subset_list"):
																lowerCamelCase__			:  Union[str, Any]  =arg

										assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
										assert outputfile is not None and not os.path.isfile(lowerCamelCase_ ), f"""{outputfile}"""
										if subset_list is not None:
													with open(os.path.realpath(lowerCamelCase_ ) ) as f:
																lowerCamelCase__			:  List[str]  ={self._vqa_file_split()[0] for x in tryload(lowerCamelCase_ )}
										else:
													lowerCamelCase__			:  Dict  =None

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





							def    UpperCAmelCase__							(						self						:str  ,							lowerCamelCase_						:Optional[int] ):





										"""simple docstring"""

										lowerCamelCase__			:  List[str]  =int(file.split('.' )[0].split('_' )[-1] )
										lowerCamelCase__			:  Any  =os.path.join(self.inputdir  ,							lowerCamelCase_ )
										return (img_id, filepath)





							@property
							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  Union[str, 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(lowerCamelCase_ ) )
													if len(lowerCamelCase_ ) == self.batch:
																lowerCamelCase__			:  Optional[int]  =batch
																lowerCamelCase__			:  List[Any]  =[]
																yield list(map(lowerCamelCase_  ,							zip(*lowerCamelCase_ ) ) )

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





							def __call__(						self						:int ):





										"""simple docstring"""

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






def      lowerCAmelCase_     (	snake_case_	:    Optional[Any]    )     ->Union[str, Any]:
			try:
						lowerCamelCase__			:  Union[str, Any]  =json.load(a_    )
						try:
									lowerCamelCase__			:  Optional[int]  =list(data.keys()    )
						except Exception:
									lowerCamelCase__			:  Optional[int]  =[d['img_id'] for d in data]
			except Exception:
						try:
									lowerCamelCase__			:  Tuple  =eval(stream.read()    )
						except Exception:
									lowerCamelCase__			:  Tuple  =stream.read().split('\n'    )
			return data


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








"""simple docstring"""


# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue






def      lowerCAmelCase_     (	snake_case_	:    List[Any]    )     ->Optional[int]:
			lowerCamelCase__			:  Optional[int]  =Queue()
			lowerCamelCase__			:  Any  =[False] * len(snake_case_    )
			lowerCamelCase__			:  Optional[Any]  =[-1] * len(snake_case_    )

			def bfs():
						while not queue.empty():
									lowerCamelCase__			:  List[Any]  =queue.get()
									lowerCamelCase__			:  int  =True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															lowerCamelCase__			:  Union[str, Any]  =1 - color[u]
															queue.put(snake_case_    )

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(snake_case_    )    ):
						if not visited[i]:
									queue.put(snake_case_    )
									lowerCamelCase__			:  Tuple  =0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
							# Adjacency List of graph
							print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))