

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





       """simple docstring"""


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





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							None
        lowerCAmelCase__      :							str    =							None
        lowerCAmelCase__      :							int    =							None
        lowerCAmelCase__      :							Any    =							1
        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							getopt.getopt(snake_case ,				"i:o:b:s" ,				["inputdir=", "outfile=", "batch_size=", "subset_list="]      )
        for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
          lowerCAmelCase__      :							List[str]    =							arg
         elif opt in ("-o", "--outfile"):
          lowerCAmelCase__      :							Optional[int]    =							arg
         elif opt in ("-b", "--batch_size"):
          lowerCAmelCase__      :							Optional[int]    =							int(snake_case      )
         elif opt in ("-s", "--subset_list"):
          lowerCAmelCase__      :							Optional[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:
          lowerCAmelCase__      :							Union[str, Any]    =							{self._vqa_file_split()[0] for x in tryload(snake_case      )}
        else:
         lowerCAmelCase__      :							Optional[int]    =							None

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							int(file.split("."      )[0].split("_"      )[-1]      )
        lowerCAmelCase__      :							str    =							os.path.join(self.inputdir ,				snake_case      )
        return (img_id, filepath)






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							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(snake_case      )      )
         if len(snake_case      ) == self.batch:
          lowerCAmelCase__      :							str    =							batch
          lowerCAmelCase__      :							str    =							[]
          yield list(map(snake_case ,				zip(*snake_case      )      )      )

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






       def __call__(      self      ):





        """simple docstring"""

        if not TEST:
         lowerCAmelCase__      :							Tuple    =							datasets.ArrowWriter(features=self.schema ,				path=self.outputfile      )
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
         lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Union[str, Any]    =							self.preprocess(snake_case      )
         lowerCAmelCase__      :							List[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" ,				)
         lowerCAmelCase__      :							Any    =							output_dict.pop("normalized_boxes"      )
         if not TEST:
          lowerCAmelCase__      :							Optional[int]    =							np.array(snake_case      )
          lowerCAmelCase__      :							Optional[int]    =							self.schema.encode_batch(snake_case      )
          writer.write_batch(snake_case      )
         if TEST:
          break
         # finalizer the writer
        if not TEST:
         lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							writer.finalize()
         print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""      )




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 try:
  lowerCAmelCase__      :							Optional[int]    =							json.load(lowercase__      )
  try:
   lowerCAmelCase__      :							str    =							list(data.keys()      )
  except Exception:
   lowerCAmelCase__      :							str    =							[d["img_id"] for d in data]
 except Exception:
  try:
   lowerCAmelCase__      :							int    =							eval(stream.read()      )
  except Exception:
   lowerCAmelCase__      :							List[str]    =							stream.read().split("\n"      )
 return data


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









"""simple docstring"""

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_UpperCamelCase		    =						logging.get_logger(__name__)






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Tuple          =	['input_values', 'attention_mask']






       def __init__(      self ,				snake_case=1 ,				snake_case=16_000 ,				snake_case=0.0 ,				snake_case=False ,				snake_case=True ,				**snake_case ,				):





        """simple docstring"""

        super().__init__(feature_size=snake_case ,				sampling_rate=snake_case ,				padding_value=snake_case ,				**snake_case      )
        lowerCAmelCase__      :							Dict    =							return_attention_mask
        lowerCAmelCase__      :							List[str]    =							do_normalize






       @staticmethod
       def  SCREAMING_SNAKE_CASE_  (      snake_case ,				snake_case ,				snake_case = 0.0      ):





        """simple docstring"""

        if attention_mask is not None:
         lowerCAmelCase__      :							List[str]    =							np.array(snake_case ,				np.intaa      )
         lowerCAmelCase__      :							str    =							[]

         for vector, length in zip(snake_case ,				attention_mask.sum(-1      )      ):
          lowerCAmelCase__      :							Any    =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7      )
          if length < normed_slice.shape[0]:
           lowerCAmelCase__      :							List[Any]    =							padding_value

          normed_input_values.append(snake_case      )
        else:
         lowerCAmelCase__      :							List[Any]    =							[(x - x.mean()) / np.sqrt(x.var() + 1e-7      ) for x in input_values]

        return normed_input_values






       def __call__(      self ,				snake_case ,				snake_case = False ,				snake_case = None ,				snake_case = False ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
          raise ValueError(
              F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
              F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
              F""" {self.sampling_rate} and not {sampling_rate}."""      )
        else:
         logger.warning(
             "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
             "Failing to do so can result in silent errors that might be hard to debug."      )

        lowerCAmelCase__      :							List[str]    =							isinstance(snake_case ,				np.ndarray      ) and len(raw_speech.shape      ) > 1
        if is_batched_numpy and len(raw_speech.shape      ) > 2:
         raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""      )
        lowerCAmelCase__      :							Tuple    =							is_batched_numpy or (
            isinstance(snake_case ,				(list, tuple)      ) and (isinstance(raw_speech[0] ,				(np.ndarray, tuple, list)      ))
        )

        # always return batch
        if not is_batched:
         lowerCAmelCase__      :							Tuple    =							[raw_speech]

        # convert into correct format for padding
        lowerCAmelCase__      :							Tuple    =							BatchFeature({"input_values": raw_speech}      )

        lowerCAmelCase__      :							Any    =							self.pad(
            snake_case ,				padding=snake_case ,				max_length=snake_case ,				truncation=snake_case ,				pad_to_multiple_of=snake_case ,				return_attention_mask=snake_case ,				)

        # convert input values to correct format
        lowerCAmelCase__      :							List[Any]    =							padded_inputs["input_values"]
        if not isinstance(input_values[0] ,				np.ndarray      ):
         lowerCAmelCase__      :							Any    =							[np.asarray(snake_case ,				dtype=np.floataa      ) for array in input_values]
        elif (
            not isinstance(snake_case ,				np.ndarray      )
            and isinstance(input_values[0] ,				np.ndarray      )
            and input_values[0].dtype is np.dtype(np.floataa      )
        ):
         lowerCAmelCase__      :							str    =							[array.astype(np.floataa      ) for array in input_values]
        elif isinstance(snake_case ,				np.ndarray      ) and input_values.dtype is np.dtype(np.floataa      ):
         lowerCAmelCase__      :							List[Any]    =							input_values.astype(np.floataa      )

        # convert attention_mask to correct format
        lowerCAmelCase__      :							int    =							padded_inputs.get("attention_mask"      )
        if attention_mask is not None:
         lowerCAmelCase__      :							Tuple    =							[np.asarray(snake_case ,				dtype=np.intaa      ) for array in attention_mask]

        # zero-mean and unit-variance normalization
        if self.do_normalize:
         lowerCAmelCase__      :							List[Any]    =							(
             attention_mask
             if self._get_padding_strategies(snake_case ,				max_length=snake_case      ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         lowerCAmelCase__      :							Union[str, Any]    =							self.zero_mean_unit_var_norm(
             padded_inputs["input_values"] ,				attention_mask=snake_case ,				padding_value=self.padding_value      )

        if return_tensors is not None:
         lowerCAmelCase__      :							Optional[Any]    =							padded_inputs.convert_to_tensors(snake_case      )

        return padded_inputs






