

"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_UpperCamelCase		    =						logging.get_logger(__name__)


if is_vision_available():
    import PIL






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Union[str, Any]          =	['pixel_values']






       def __init__(      self ,				snake_case = True ,				snake_case = None ,				snake_case = PILImageResampling.BILINEAR ,				snake_case = True ,				snake_case = False ,				snake_case = True ,				snake_case = True ,				snake_case = 1 / 255 ,				snake_case = True ,				snake_case = None ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        super().__init__(**snake_case      )

        lowerCAmelCase__      :							int    =							size if size is not None else {"height": 2_560, "width": 1_920}
        if isinstance(snake_case ,				(tuple, list)      ):
         # The previous feature extractor size parameter was in (width, height) format
         lowerCAmelCase__      :							Optional[Any]    =							size[::-1]
        lowerCAmelCase__      :							str    =							get_size_dict(snake_case      )

        lowerCAmelCase__      :							Dict    =							do_resize
        lowerCAmelCase__      :							Tuple    =							size
        lowerCAmelCase__      :							str    =							resample
        lowerCAmelCase__      :							str    =							do_thumbnail
        lowerCAmelCase__      :							List[Any]    =							do_align_long_axis
        lowerCAmelCase__      :							List[Any]    =							do_pad
        lowerCAmelCase__      :							List[str]    =							do_rescale
        lowerCAmelCase__      :							Optional[int]    =							rescale_factor
        lowerCAmelCase__      :							Dict    =							do_normalize
        lowerCAmelCase__      :							Tuple    =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowerCAmelCase__      :							Union[str, Any]    =							image_std if image_std is not None else IMAGENET_STANDARD_STD






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case = None      ):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							get_image_size(snake_case      )
        lowerCAmelCase__ ,	lowerCAmelCase__      :							Tuple    =							size["height"], size["width"]

        if (output_width < output_height and input_width > input_height) or (
            output_width > output_height and input_width < input_height
        ):
         lowerCAmelCase__      :							Tuple    =							np.rotaa(snake_case ,				3      )

        if data_format is not None:
         lowerCAmelCase__      :							List[Any]    =							to_channel_dimension_format(snake_case ,				snake_case      )

        return image






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case = False ,				snake_case = None ,				):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Optional[Any]    =							size["height"], size["width"]
        lowerCAmelCase__ ,	lowerCAmelCase__      :							Tuple    =							get_image_size(snake_case      )

        lowerCAmelCase__      :							Optional[Any]    =							output_width - input_width
        lowerCAmelCase__      :							List[Any]    =							output_height - input_height

        if random_padding:
         lowerCAmelCase__      :							Union[str, Any]    =							np.random.randint(low=0 ,				high=delta_height + 1      )
         lowerCAmelCase__      :							Optional[Any]    =							np.random.randint(low=0 ,				high=delta_width + 1      )
        else:
         lowerCAmelCase__      :							Optional[Any]    =							delta_height // 2
         lowerCAmelCase__      :							str    =							delta_width // 2

        lowerCAmelCase__      :							Any    =							delta_height - pad_top
        lowerCAmelCase__      :							Any    =							delta_width - pad_left

        lowerCAmelCase__      :							Tuple    =							((pad_top, pad_bottom), (pad_left, pad_right))
        return pad(snake_case ,				snake_case ,				data_format=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				*snake_case ,				**snake_case      ):





        """simple docstring"""

        logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."      )
        return self.pad_image(*snake_case ,				**snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case = PILImageResampling.BICUBIC ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Union[str, Any]    =							get_image_size(snake_case      )
        lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							size["height"], size["width"]

        # We always resize to the smallest of either the input or output size.
        lowerCAmelCase__      :							Optional[Any]    =							min(snake_case ,				snake_case      )
        lowerCAmelCase__      :							Optional[int]    =							min(snake_case ,				snake_case      )

        if height == input_height and width == input_width:
         return image

        if input_height > input_width:
         lowerCAmelCase__      :							Tuple    =							int(input_width * height / input_height      )
        elif input_width > input_height:
         lowerCAmelCase__      :							Dict    =							int(input_height * width / input_width      )

        return resize(
            snake_case ,				size=(height, width) ,				resample=snake_case ,				reducing_gap=2.0 ,				data_format=snake_case ,				**snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case = PILImageResampling.BICUBIC ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							get_size_dict(snake_case      )
        lowerCAmelCase__      :							Any    =							min(size["height"] ,				size["width"]      )
        lowerCAmelCase__      :							Union[str, Any]    =							get_resize_output_image_size(snake_case ,				size=snake_case ,				default_to_square=snake_case      )
        lowerCAmelCase__      :							Tuple    =							resize(snake_case ,				size=snake_case ,				resample=snake_case ,				data_format=snake_case ,				**snake_case      )
        return resized_image






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        return rescale(snake_case ,				scale=snake_case ,				data_format=snake_case ,				**snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        return normalize(snake_case ,				mean=snake_case ,				std=snake_case ,				data_format=snake_case ,				**snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = False ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = ChannelDimension.FIRST ,				**snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__      :							Optional[Any]    =							size if size is not None else self.size
        if isinstance(snake_case ,				(tuple, list)      ):
         # Previous feature extractor had size in (width, height) format
         lowerCAmelCase__      :							List[str]    =							size[::-1]
        lowerCAmelCase__      :							List[Any]    =							get_size_dict(snake_case      )
        lowerCAmelCase__      :							str    =							resample if resample is not None else self.resample
        lowerCAmelCase__      :							Any    =							do_thumbnail if do_thumbnail is not None else self.do_thumbnail
        lowerCAmelCase__      :							Any    =							do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
        lowerCAmelCase__      :							Optional[int]    =							do_pad if do_pad is not None else self.do_pad
        lowerCAmelCase__      :							Union[str, Any]    =							do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__      :							str    =							rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__      :							List[str]    =							do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__      :							Optional[int]    =							image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__      :							List[str]    =							image_std if image_std is not None else self.image_std

        lowerCAmelCase__      :							Optional[Any]    =							make_list_of_images(snake_case      )

        if not valid_images(snake_case      ):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."      )

        if do_resize and size is None:
         raise ValueError("Size must be specified if do_resize is True."      )

        if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."      )

        if do_pad and size is None:
         raise ValueError("Size must be specified if do_pad is True."      )

        if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."      )

        # All transformations expect numpy arrays.
        lowerCAmelCase__      :							Tuple    =							[to_numpy_array(snake_case      ) for image in images]

        if do_align_long_axis:
         lowerCAmelCase__      :							int    =							[self.align_long_axis(snake_case ,				size=snake_case      ) for image in images]

        if do_resize:
         lowerCAmelCase__      :							Union[str, Any]    =							[self.resize(image=snake_case ,				size=snake_case ,				resample=snake_case      ) for image in images]

        if do_thumbnail:
         lowerCAmelCase__      :							Any    =							[self.thumbnail(image=snake_case ,				size=snake_case      ) for image in images]

        if do_pad:
         lowerCAmelCase__      :							Tuple    =							[self.pad_image(image=snake_case ,				size=snake_case ,				random_padding=snake_case      ) for image in images]

        if do_rescale:
         lowerCAmelCase__      :							Union[str, Any]    =							[self.rescale(image=snake_case ,				scale=snake_case      ) for image in images]

        if do_normalize:
         lowerCAmelCase__      :							Dict    =							[self.normalize(image=snake_case ,				mean=snake_case ,				std=snake_case      ) for image in images]

        lowerCAmelCase__      :							Optional[int]    =							[to_channel_dimension_format(snake_case ,				snake_case      ) for image in images]

        lowerCAmelCase__      :							Dict    =							{"pixel_values": images}
        return BatchFeature(data=snake_case ,				tensor_type=snake_case      )









"""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)






