


'''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_  :    int      =			False
a_  :    Any      =			Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
a_  :    List[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, 2048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)





class 					snake_case   :





		"""simple docstring"""




		def __init__(   self      ,  UpperCamelCase=sys.argv[1:]					):







			"""simple docstring"""
			lowerCamelCase_								=  None
			lowerCamelCase_								=  None
			lowerCamelCase_								=  None
			lowerCamelCase_								=  1
			lowerCamelCase_			,lowerCamelCase_								=  getopt.getopt(__UpperCamelCase      ,  "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(__UpperCamelCase					)
				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(__UpperCamelCase					), f'''{outputfile}'''
			if subset_list is not None:
				with open(os.path.realpath(__UpperCamelCase					)					) as f:
					lowerCamelCase_								=  {self._vqa_file_split()[0] for x in tryload(__UpperCamelCase					)}
			else:
				lowerCamelCase_								=  None

			lowerCamelCase_								=  CONFIG
			if torch.cuda.is_available():
				lowerCamelCase_								=  "cuda"
			lowerCamelCase_								=  os.path.realpath(__UpperCamelCase					)
			lowerCamelCase_								=  os.path.realpath(__UpperCamelCase					)
			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      ,  UpperCamelCase					):







			"""simple docstring"""
			lowerCamelCase_								=  int(file.split("."					)[0].split("_"					)[-1]					)
			lowerCamelCase_								=  os.path.join(self.inputdir      ,  __UpperCamelCase					)
			return (img_id, filepath)






		@property
		def   snake_case		(   self					):







			"""simple docstring"""
			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(__UpperCamelCase					)					)
				if len(__UpperCamelCase					) == self.batch:
					lowerCamelCase_								=  batch
					lowerCamelCase_								=  []
					yield list(map(__UpperCamelCase      ,  zip(*__UpperCamelCase					)					)					)

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







		def __call__(   self					):







			"""simple docstring"""
			# 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(__UpperCamelCase					)
				lowerCamelCase_								=  self.model(
				    __UpperCamelCase      ,  __UpperCamelCase      ,  scales_yx=__UpperCamelCase      ,  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(__UpperCamelCase					)
					lowerCamelCase_								=  self.schema.encode_batch(__UpperCamelCase					)
					writer.write_batch(__UpperCamelCase					)
				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 >> 20} mb'''					)





def __snake_case (				UpperCAmelCase_						:						Optional[Any]		):







	try:
		lowerCamelCase_								=  json.load(lowercase_		)
		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__":
					a_  :    Dict      =			Extract(sys.argv[1:])
					extract()
					if not TEST:
										a_  :    Any      =			datasets.Dataset.from_file(extract.outputfile)
										# wala!
										# print(np.array(dataset[0:2]["roi_features"]).shape)







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_  :    Optional[Any]      =			logging.get_logger(__name__)

a_  :    Tuple      =			{
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  "electra"






  def __init__(   self      ,  UpperCamelCase=3_0522      ,  UpperCamelCase=128      ,  UpperCamelCase=256      ,  UpperCamelCase=12      ,  UpperCamelCase=4      ,  UpperCamelCase=1024      ,  UpperCamelCase="gelu"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0.1      ,  UpperCamelCase=512      ,  UpperCamelCase=2      ,  UpperCamelCase=0.02      ,  UpperCamelCase=1e-12      ,  UpperCamelCase="first"      ,  UpperCamelCase=True      ,  UpperCamelCase="gelu"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0      ,  UpperCamelCase="absolute"      ,  UpperCamelCase=True      ,  UpperCamelCase=None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   super().__init__(pad_token_id=UpperCamelCase      ,  **UpperCamelCase					)

   lowerCamelCase_								=  vocab_size
   lowerCamelCase_								=  embedding_size
   lowerCamelCase_								=  hidden_size
   lowerCamelCase_								=  num_hidden_layers
   lowerCamelCase_								=  num_attention_heads
   lowerCamelCase_								=  intermediate_size
   lowerCamelCase_								=  hidden_act
   lowerCamelCase_								=  hidden_dropout_prob
   lowerCamelCase_								=  attention_probs_dropout_prob
   lowerCamelCase_								=  max_position_embeddings
   lowerCamelCase_								=  type_vocab_size
   lowerCamelCase_								=  initializer_range
   lowerCamelCase_								=  layer_norm_eps

   lowerCamelCase_								=  summary_type
   lowerCamelCase_								=  summary_use_proj
   lowerCamelCase_								=  summary_activation
   lowerCamelCase_								=  summary_last_dropout
   lowerCamelCase_								=  position_embedding_type
   lowerCamelCase_								=  use_cache
   lowerCamelCase_								=  classifier_dropout





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  @property
  def   snake_case		(   self					):







   """simple docstring"""
   if self.task == "multiple-choice":
    lowerCamelCase_								=  {0: "batch", 1: "choice", 2: "sequence"}
   else:
    lowerCamelCase_								=  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
           ("token_type_ids", dynamic_axis),
       ]					)



