


'''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
_A      =     False
_A      =     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_A      =     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      SCREAMING_SNAKE_CASE_	:




			def __init__(      self					,      lowercase=sys.argv[1:]       )			->						List[Any]:


								'''simple docstring'''






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

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

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




			def 			_snake_case   (      self					,      lowercase       )			->						Tuple:


								'''simple docstring'''






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




			@property
			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = []
								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(__snake_case       )       )
													if len(__snake_case       ) == self.batch:
																		__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = batch
																		__SCREAMING_SNAKE_CASE					:							Dict			   = []
																		yield list(map(__snake_case					,      zip(*__snake_case       )       )       )

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




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


								'''simple docstring'''






								if not TEST:
													__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = datasets.ArrowWriter(features=self.schema					,      path=self.outputfile       )
								# do file generator
								for i, (img_ids, filepaths) in enumerate(self.file_generator       ):
													__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							str			   = self.preprocess(__snake_case       )
													__SCREAMING_SNAKE_CASE					:							Any			   = self.model(
													    __snake_case					,      __snake_case					,      scales_yx=__snake_case					,      padding='''max_detections'''					,      max_detections=self.config.MAX_DETECTIONS					,      pad_value=0					,      return_tensors='''np'''					,      location='''cpu'''					,      )
													__SCREAMING_SNAKE_CASE					:							Tuple			   = output_dict.pop('''normalized_boxes'''       )
													if not TEST:
																		__SCREAMING_SNAKE_CASE					:							Tuple			   = np.array(__snake_case       )
																		__SCREAMING_SNAKE_CASE					:							Tuple			   = self.schema.encode_batch(__snake_case       )
																		writer.write_batch(__snake_case       )
													if TEST:
																		break
													# finalizer the writer
								if not TEST:
													__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Optional[Any]			   = writer.finalize()
													print(f"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""       )




def A_					(				__SCREAMING_SNAKE_CASE :					int       )     ->  Optional[Any]:






					try:
										__SCREAMING_SNAKE_CASE					:							Any			   = json.load(UpperCamelCase_       )
										try:
															__SCREAMING_SNAKE_CASE					:							List[str]			   = list(data.keys()       )
										except Exception:
															__SCREAMING_SNAKE_CASE					:							str			   = [d['''img_id'''] for d in data]
					except Exception:
										try:
															__SCREAMING_SNAKE_CASE					:							Tuple			   = eval(stream.read()       )
										except Exception:
															__SCREAMING_SNAKE_CASE					:							int			   = stream.read().split('''\n'''       )
					return data


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




'''simple docstring'''






from .balanced_parentheses import balanced_parentheses
from .stack import Stack


def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  int:
					return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(__SCREAMING_SNAKE_CASE	,   -1       )


def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  str:
					if not balanced_parentheses(__SCREAMING_SNAKE_CASE       ):
										raise ValueError('''Mismatched parentheses'''       )
					__SCREAMING_SNAKE_CASE					:							Stack[str]			   = Stack()
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = []
					for char in expression_str:
										if char.isalpha() or char.isdigit():
															postfix.append(__SCREAMING_SNAKE_CASE       )
										elif char == "(":
															stack.push(__SCREAMING_SNAKE_CASE       )
										elif char == ")":
															while not stack.is_empty() and stack.peek() != "(":
																				postfix.append(stack.pop()       )
															stack.pop()
										else:
															while not stack.is_empty() and precedence(__SCREAMING_SNAKE_CASE       ) <= precedence(stack.peek()       ):
																				postfix.append(stack.pop()       )
															stack.push(__SCREAMING_SNAKE_CASE       )
					while not stack.is_empty():
										postfix.append(stack.pop()       )
					return " ".join(__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						_A      =     """a+b*(c^d-e)^(f+g*h)-i"""

						print("""Infix to Postfix Notation demonstration:\n""")
						print("""Infix notation: """ + expression)
						print("""Postfix notation: """ + infix_to_postfix(expression))


