

'''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				:   Optional[Any]      				=  False
lowerCamelCase				:   Optional[Any]      				=  Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
lowerCamelCase				:   List[str]      				=  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_0_4_8), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)







class 							A__					:





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=None
       _SCREAMING_SNAKE_CASE												=None
       _SCREAMING_SNAKE_CASE												=None
       _SCREAMING_SNAKE_CASE												=1
       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=getopt.getopt(_a							,      'i:o:b:s'							,      ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']				)
       for opt, arg in opts:
            if opt in ("-i", "--inputdir"):
                 _SCREAMING_SNAKE_CASE												=arg
            elif opt in ("-o", "--outfile"):
                 _SCREAMING_SNAKE_CASE												=arg
            elif opt in ("-b", "--batch_size"):
                 _SCREAMING_SNAKE_CASE												=int(_a				)
            elif opt in ("-s", "--subset_list"):
                 _SCREAMING_SNAKE_CASE												=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:
                 _SCREAMING_SNAKE_CASE												={self._vqa_file_split()[0] for x in tryload(_a				)}
       else:
            _SCREAMING_SNAKE_CASE												=None

       _SCREAMING_SNAKE_CASE												=CONFIG
       if torch.cuda.is_available():
            _SCREAMING_SNAKE_CASE												='cuda'
       _SCREAMING_SNAKE_CASE												=os.path.realpath(_a				)
       _SCREAMING_SNAKE_CASE												=os.path.realpath(_a				)
       _SCREAMING_SNAKE_CASE												=Preprocess(self.config				)
       _SCREAMING_SNAKE_CASE												=GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'							,      config=self.config				)
       _SCREAMING_SNAKE_CASE												=batch_size if batch_size != 0 else 1
       _SCREAMING_SNAKE_CASE												=DEFAULT_SCHEMA





  def 		A						(	self   :			Dict							,      _a   :			Optional[int]				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=int(file.split('.'				)[0].split('_'				)[-1]				)
       _SCREAMING_SNAKE_CASE												=os.path.join(self.inputdir							,      _a				)
       return (img_id, filepath)





  @property
  def 		A						(	self   :			List[str]				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=[]
       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:
                 _SCREAMING_SNAKE_CASE												=batch
                 _SCREAMING_SNAKE_CASE												=[]
                 yield list(map(_a							,      zip(*_a				)				)				)

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





  def __call__(	self   :			Dict				)			->					List[Any]:





       '''simple docstring'''






       if not TEST:
            _SCREAMING_SNAKE_CASE												=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												=self.preprocess(_a				)
            _SCREAMING_SNAKE_CASE												=self.model(
                _a							,      _a							,      scales_yx=_a							,      padding='max_detections'							,      max_detections=self.config.MAX_DETECTIONS							,      pad_value=0							,      return_tensors='np'							,      location='cpu'							,      )
            _SCREAMING_SNAKE_CASE												=output_dict.pop('normalized_boxes'				)
            if not TEST:
                 _SCREAMING_SNAKE_CASE												=np.array(_a				)
                 _SCREAMING_SNAKE_CASE												=self.schema.encode_batch(_a				)
                 writer.write_batch(_a				)
            if TEST:
                 break
            # finalizer the writer
       if not TEST:
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=writer.finalize()
            print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"				)


def  _lowerCAmelCase      (		_UpperCamelCase		:  str       )   ->    List[str]:
     """simple docstring"""




     try:
          _SCREAMING_SNAKE_CASE												=json.load(_UpperCamelCase       )
          try:
               _SCREAMING_SNAKE_CASE												=list(data.keys()       )
          except Exception:
               _SCREAMING_SNAKE_CASE												=[d['img_id'] for d in data]
     except Exception:
          try:
               _SCREAMING_SNAKE_CASE												=eval(stream.read()       )
          except Exception:
               _SCREAMING_SNAKE_CASE												=stream.read().split('\n'       )
     return data


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



'''simple docstring'''
def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  Optional[Any]       )   ->    Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=len(_UpperCamelCase       )
     # Create a stack and push index of fist element to it
     _SCREAMING_SNAKE_CASE												=[]
     st.append(0       )

     # Span value of first element is always 1
     _SCREAMING_SNAKE_CASE												=1

     # Calculate span values for rest of the elements
     for i in range(1							,				_UpperCamelCase       ):
          # Pop elements from stack while stack is not
          # empty and top of stack is smaller than price[i]
          while len(_UpperCamelCase       ) > 0 and price[st[0]] <= price[i]:
               st.pop()

          # If stack becomes empty, then price[i] is greater
          # than all elements on left of it, i.e. price[0],
          # price[1], ..price[i-1]. Else the price[i]  is
          # greater than elements after top of stack
          _SCREAMING_SNAKE_CASE												=i + 1 if len(_UpperCamelCase       ) <= 0 else (i - st[0])

          # Push this element to stack
          st.append(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Any							,				_UpperCamelCase		:  str       )   ->    Any:
     """simple docstring"""




     for i in range(0							,				_UpperCamelCase       ):
          print(arr[i]							,				end=' '       )


# Driver program to test above function
lowerCamelCase				:   str      				=  [1_0, 4, 5, 9_0, 1_2_0, 8_0]
lowerCamelCase				:   str      				=  [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)







print_array(S, len(price))
