




import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






class 			A			(		nn.Linear		):
     """simple docstring"""

     def __init__(    self  :   int	,  A__  :   int	,  A__  :   int	,  A__  :   bool = True	,  A__  :   str = "constant"	,  A__  :   float = 0.0	,  A__  :   str = "topK"	,  )				->       Optional[Any]:
           super(A__	,  self			).__init__(in_features=A__	,  out_features=A__	,  bias=A__			)
           assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
           lowerCamelCase__        =				pruning_method

           if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
                 lowerCamelCase__        =				mask_scale
                 lowerCamelCase__        =				mask_init
                 lowerCamelCase__        =				nn.Parameter(torch.empty(self.weight.size()			)			)
                 self.init_mask()




     def  __snake_case   (    self  :   List[Any]			)				->       Optional[int]:
           if self.mask_init == "constant":
                 init.constant_(self.mask_scores	,  val=self.mask_scale			)
           elif self.mask_init == "uniform":
                 init.uniform_(self.mask_scores	,  a=-self.mask_scale	,  b=self.mask_scale			)
           elif self.mask_init == "kaiming":
                 init.kaiming_uniform_(self.mask_scores	,  a=math.sqrt(5			)			)




     def  __snake_case   (    self  :   Optional[Any]	,  A__  :   torch.tensor	,  A__  :   float			)				->       Tuple:
           # Get the mask
           if self.pruning_method == "topK":
                 lowerCamelCase__        =				TopKBinarizer.apply(self.mask_scores	,  A__			)
           elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                 lowerCamelCase__        =				'sigmoied' in self.pruning_method
                 lowerCamelCase__        =				ThresholdBinarizer.apply(self.mask_scores	,  A__	,  A__			)
           elif self.pruning_method == "magnitude":
                 lowerCamelCase__        =				MagnitudeBinarizer.apply(self.weight	,  A__			)
           elif self.pruning_method == "l0":
                 lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				-0.1, 1.1, 2 / 3
                 if self.training:
                       lowerCamelCase__        =				torch.zeros_like(self.mask_scores			).uniform_().clamp(0.0001	,  0.9999			)
                       lowerCamelCase__        =				torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b			)
                 else:
                       lowerCamelCase__        =				torch.sigmoid(self.mask_scores			)
                 lowerCamelCase__        =				s * (r - l) + l
                 lowerCamelCase__        =				s_bar.clamp(min=0.0	,  max=1.0			)
           # Mask weights with computed mask
           lowerCamelCase__        =				mask * self.weight
           # Compute output (linear layer) with masked weights
           return nn.functional.linear(A__	,  A__	,  self.bias			)





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
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =False
__SCREAMING_SNAKE_CASE    :   int       =Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =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 			A			:
     """simple docstring"""

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

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




     def  __snake_case   (    self  :   Dict	,  A__  :   Any			)				->       Union[str, Any]:
           lowerCamelCase__        =				int(file.split('.'			)[0].split('_'			)[-1]			)
           lowerCamelCase__        =				os.path.join(self.inputdir	,  A__			)
           return (img_id, filepath)




     @property
     def  __snake_case   (    self  :   int			)				->       Optional[Any]:
           lowerCamelCase__        =				[]
           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:
                       lowerCamelCase__        =				batch
                       lowerCamelCase__        =				[]
                       yield list(map(A__	,  zip(*A__			)			)			)

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




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

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			Optional[Any]:


      '''simple docstring'''




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


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