





'''simple docstring'''

import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  import PIL

  from transformers import FlavaImageProcessor
  from transformers.image_utils import PILImageResampling
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )
else:
  __lowerCAmelCase							:			List[str]				=None






class      UpperCAmelCase			( unittest.TestCase       ):
 def __init__(       self   :List[Any]		, lowercase_   :Optional[Any]		, lowercase_   :Optional[Any]=7		, lowercase_   :Tuple=3		, lowercase_   :List[str]=30		, lowercase_   :Union[str, Any]=4_00		, lowercase_   :Dict=True		, lowercase_   :Tuple=None		, lowercase_   :Dict=True		, lowercase_   :List[str]=None		, lowercase_   :Optional[int]=None		, lowercase_   :Any=True		, lowercase_   :Union[str, Any]=1 / 2_55		, lowercase_   :Optional[int]=True		, lowercase_   :Any=FLAVA_IMAGE_MEAN		, lowercase_   :Union[str, Any]=FLAVA_IMAGE_STD		, lowercase_   :Any=14		, lowercase_   :str=75		, lowercase_   :Optional[int]=None		, lowercase_   :Tuple=16		, lowercase_   :str=0.3		, lowercase_   :Optional[Any]=None		, lowercase_   :str=True		, lowercase_   :Tuple=None		, lowercase_   :List[Any]=None		, lowercase_   :str=True		, lowercase_   :List[Any]=None		, lowercase_   :str=True		, lowercase_   :Optional[int]=True		, lowercase_   :int=FLAVA_CODEBOOK_MEAN		, lowercase_   :Optional[Any]=FLAVA_CODEBOOK_STD		, )->							Union[str, Any]:
     A__       =    size if size is not None else {"height": 2_24, "width": 2_24}
     A__       =    crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
     A__       =    codebook_size if codebook_size is not None else {"height": 1_12, "width": 1_12}
     A__       =    codebook_crop_size if codebook_crop_size is not None else {"height": 1_12, "width": 1_12}

     A__       =    parent
     A__       =    batch_size
     A__       =    num_channels
     A__       =    do_resize
     A__       =    do_rescale
     A__       =    rescale_factor
     A__       =    min_resolution
     A__       =    max_resolution
     A__       =    size
     A__       =    resample if resample is not None else PILImageResampling.BICUBIC
     A__       =    do_normalize
     A__       =    image_mean
     A__       =    image_std
     A__       =    do_center_crop
     A__       =    crop_size

     A__       =    input_size_patches
     A__       =    total_mask_patches
     A__       =    mask_group_max_patches
     A__       =    mask_group_min_patches
     A__       =    mask_group_min_aspect_ratio
     A__       =    mask_group_max_aspect_ratio

     A__       =    codebook_do_resize
     A__       =    codebook_size
     A__       =    codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
     A__       =    codebook_do_center_crop
     A__       =    codebook_crop_size
     A__       =    codebook_do_map_pixels
     A__       =    codebook_do_normalize
     A__       =    codebook_image_mean
     A__       =    codebook_image_std
 def   UpperCAmelCase_							(       self   :int							)->							Optional[int]:
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "resample": self.resample,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "input_size_patches": self.input_size_patches,
         "total_mask_patches": self.total_mask_patches,
         "mask_group_max_patches": self.mask_group_max_patches,
         "mask_group_min_patches": self.mask_group_min_patches,
         "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
         "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
         "codebook_do_resize": self.codebook_do_resize,
         "codebook_size": self.codebook_size,
         "codebook_resample": self.codebook_resample,
         "codebook_do_center_crop": self.codebook_do_center_crop,
         "codebook_crop_size": self.codebook_crop_size,
         "codebook_do_map_pixels": self.codebook_do_map_pixels,
         "codebook_do_normalize": self.codebook_do_normalize,
         "codebook_image_mean": self.codebook_image_mean,
         "codebook_image_std": self.codebook_image_std,
     }
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							Optional[int]:
     return (self.size["height"], self.size["width"])
 def   UpperCAmelCase_							(       self   :Any							)->							Optional[Any]:
     return (
         (self.input_size_patches, self.input_size_patches)
         if not isinstance(self.input_size_patches		, lowercase_							)
         else self.input_size_patches
     )



 def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
     return (self.codebook_size["height"], self.codebook_size["width"])






@require_torch
@require_vision
class      UpperCAmelCase			( UpperCamelCase__    , unittest.TestCase       ):
 __lowercase     	=       FlavaImageProcessor if is_vision_available() else None
 __lowercase     	=       None
 def   UpperCAmelCase_							(       self   :Any							)->							int:
     A__       =    FlavaImageProcessingTester(self							)
 @property
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							int:
     return self.image_processor_tester.prepare_image_processor_dict()
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							Optional[Any]:
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     self.assertTrue(hasattr(lowercase_		, "image_mean"							)							)
     self.assertTrue(hasattr(lowercase_		, "image_std"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_normalize"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_resize"							)							)
     self.assertTrue(hasattr(lowercase_		, "resample"							)							)
     self.assertTrue(hasattr(lowercase_		, "crop_size"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_center_crop"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_rescale"							)							)
     self.assertTrue(hasattr(lowercase_		, "rescale_factor"							)							)
     self.assertTrue(hasattr(lowercase_		, "masking_generator"							)							)
     self.assertTrue(hasattr(lowercase_		, "codebook_do_resize"							)							)
     self.assertTrue(hasattr(lowercase_		, "codebook_size"							)							)
     self.assertTrue(hasattr(lowercase_		, "codebook_resample"							)							)
     self.assertTrue(hasattr(lowercase_		, "codebook_do_center_crop"							)							)
     self.assertTrue(hasattr(lowercase_		, "codebook_crop_size"							)							)
     self.assertTrue(hasattr(lowercase_		, "codebook_do_map_pixels"							)							)
     self.assertTrue(hasattr(lowercase_		, "codebook_do_normalize"							)							)
     self.assertTrue(hasattr(lowercase_		, "codebook_image_mean"							)							)
     self.assertTrue(hasattr(lowercase_		, "codebook_image_std"							)							)
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							Tuple:
     A__       =    self.image_processing_class.from_dict(self.image_processor_dict							)
     self.assertEqual(image_processor.size		, {"height": 2_24, "width": 2_24}							)
     self.assertEqual(image_processor.crop_size		, {"height": 2_24, "width": 2_24}							)
     self.assertEqual(image_processor.codebook_size		, {"height": 1_12, "width": 1_12}							)
     self.assertEqual(image_processor.codebook_crop_size		, {"height": 1_12, "width": 1_12}							)

     A__       =    self.image_processing_class.from_dict(
         self.image_processor_dict		, size=42		, crop_size=84		, codebook_size=33		, codebook_crop_size=66							)
     self.assertEqual(image_processor.size		, {"height": 42, "width": 42}							)
     self.assertEqual(image_processor.crop_size		, {"height": 84, "width": 84}							)
     self.assertEqual(image_processor.codebook_size		, {"height": 33, "width": 33}							)
     self.assertEqual(image_processor.codebook_crop_size		, {"height": 66, "width": 66}							)
 def   UpperCAmelCase_							(       self   :List[str]							)->							List[Any]:
     pass
 def   UpperCAmelCase_							(       self   :Dict							)->							Dict:
     # Initialize image_processing
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     # create random PIL images
     A__       =    prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase_							)
     for image in image_inputs:
         self.assertIsInstance(lowercase_		, PIL.Image.Image							)

     # Test not batched input
     A__       =    image_processing(image_inputs[0]		, return_tensors="pt"							)

     # Test no bool masked pos
     self.assertFalse("bool_masked_pos" in encoded_images							)

     A__,			A__       =    self.image_processor_tester.get_expected_image_size()

     self.assertEqual(
         encoded_images.pixel_values.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )

     # Test batched
     A__       =    image_processing(lowercase_		, return_tensors="pt"							)
     A__,			A__       =    self.image_processor_tester.get_expected_image_size()

     # Test no bool masked pos
     self.assertFalse("bool_masked_pos" in encoded_images							)

     self.assertEqual(
         encoded_images.pixel_values.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )		, )
 def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :Dict		, lowercase_   :int							)->							Union[str, Any]:
     # Initialize image_processing
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     # create random tensors
     A__       =    prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase_		, **lowercase_							)
     for image in image_inputs:
         self.assertIsInstance(lowercase_		, lowercase_							)

     # Test not batched input
     A__       =    image_processing(image_inputs[0]		, return_tensors="pt"							)

     A__,			A__       =    self.image_processor_tester.get_expected_image_size()
     self.assertEqual(
         encoded_images.pixel_values.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )

     A__       =    image_processing(lowercase_		, return_image_mask=lowercase_		, return_tensors="pt"							)

     A__,			A__       =    self.image_processor_tester.get_expected_image_size()
     self.assertEqual(
         encoded_images.pixel_values.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )		, )

     A__,			A__       =    self.image_processor_tester.get_expected_mask_size()
     self.assertEqual(
         encoded_images.bool_masked_pos.shape		, (
             self.image_processor_tester.batch_size,
             expected_height,
             expected_width,
         )		, )

     # Test batched
     A__       =    image_processing(lowercase_		, return_tensors="pt"							).pixel_values

     A__,			A__       =    self.image_processor_tester.get_expected_image_size()
     self.assertEqual(
         encoded_images.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )		, )

     # Test masking
     A__       =    image_processing(lowercase_		, return_image_mask=lowercase_		, return_tensors="pt"							)

     A__,			A__       =    self.image_processor_tester.get_expected_image_size()
     self.assertEqual(
         encoded_images.pixel_values.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )		, )

     A__,			A__       =    self.image_processor_tester.get_expected_mask_size()
     self.assertEqual(
         encoded_images.bool_masked_pos.shape		, (
             self.image_processor_tester.batch_size,
             expected_height,
             expected_width,
         )		, )
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							Union[str, Any]:
     self._test_call_framework(np.ndarray		, prepare_kwargs={"numpify": True}							)
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Optional[int]:
     self._test_call_framework(torch.Tensor		, prepare_kwargs={"torchify": True}							)
 def   UpperCAmelCase_							(       self   :str							)->							Tuple:
     # Initialize image_processing
     random.seed(12_34							)
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     A__       =    prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase_		, torchify=lowercase_							)

     # Test not batched input
     A__       =    image_processing(image_inputs[0]		, return_image_mask=lowercase_		, return_tensors="pt"							)
     self.assertEqual(encoded_images.bool_masked_pos.sum().item()		, 75							)



 def   UpperCAmelCase_							(       self   :Any							)->							List[str]:
     # Initialize image_processing
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     # create random PIL images
     A__       =    prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase_							)
     for image in image_inputs:
         self.assertIsInstance(lowercase_		, PIL.Image.Image							)

     # Test not batched input
     A__       =    image_processing(image_inputs[0]		, return_codebook_pixels=lowercase_		, return_tensors="pt"							)
     A__,			A__       =    self.image_processor_tester.get_expected_codebook_image_size()
     self.assertEqual(
         encoded_images.codebook_pixel_values.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )

     # Test batched
     A__       =    image_processing(lowercase_		, return_codebook_pixels=lowercase_		, return_tensors="pt"							)
     A__,			A__       =    self.image_processor_tester.get_expected_codebook_image_size()
     self.assertEqual(
         encoded_images.codebook_pixel_values.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )		, )










'''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							:			str				=False
__lowerCAmelCase							:			Union[str, Any]				=Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
__lowerCAmelCase							:			int				=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      UpperCAmelCase			:
 def __init__(       self   :List[str]		, lowercase_   :List[Any]=sys.argv[1:]							)->							Dict:
     A__       =    None
     A__       =    None
     A__       =    None
     A__       =    1
     A__,			A__       =    getopt.getopt(lowercase_		, "i:o:b:s"		, ["inputdir=", "outfile=", "batch_size=", "subset_list="]							)
     for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
             A__       =    arg
         elif opt in ("-o", "--outfile"):
             A__       =    arg
         elif opt in ("-b", "--batch_size"):
             A__       =    int(lowercase_							)
         elif opt in ("-s", "--subset_list"):
             A__       =    arg

     assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
     assert outputfile is not None and not os.path.isfile(lowercase_							), F"{outputfile}"
     if subset_list is not None:
         with open(os.path.realpath(lowercase_							)							) as f:
             A__       =    {self._vqa_file_split()[0] for x in tryload(lowercase_							)}
     else:
         A__       =    None

     A__       =    CONFIG
     if torch.cuda.is_available():
         A__       =    "cuda"
     A__       =    os.path.realpath(lowercase_							)
     A__       =    os.path.realpath(lowercase_							)
     A__       =    Preprocess(self.config							)
     A__       =    GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"		, config=self.config							)
     A__       =    batch_size if batch_size != 0 else 1
     A__       =    DEFAULT_SCHEMA
 def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Optional[Any]							)->							Dict:
     A__       =    int(file.split("."							)[0].split("_"							)[-1]							)
     A__       =    os.path.join(self.inputdir		, lowercase_							)
     return (img_id, filepath)
 @property
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Optional[int]:
     A__       =    []
     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(lowercase_							)							)
         if len(lowercase_							) == self.batch:
             A__       =    batch
             A__       =    []
             yield list(map(lowercase_		, zip(*lowercase_							)							)							)

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



 def __call__(       self   :Optional[int]							)->							List[str]:
     # make writer
     if not TEST:
         A__       =    datasets.ArrowWriter(features=self.schema		, path=self.outputfile							)
     # do file generator
     for i, (img_ids, filepaths) in enumerate(self.file_generator							):
         A__,			A__,			A__       =    self.preprocess(lowercase_							)
         A__       =    self.model(
             lowercase_		, lowercase_		, scales_yx=lowercase_		, padding="max_detections"		, max_detections=self.config.MAX_DETECTIONS		, pad_value=0		, return_tensors="np"		, location="cpu"		, )
         A__       =    output_dict.pop("normalized_boxes"							)
         if not TEST:
             A__       =    np.array(lowercase_							)
             A__       =    self.schema.encode_batch(lowercase_							)
             writer.write_batch(lowercase_							)
         if TEST:
             break
         # finalizer the writer
     if not TEST:
         A__,			A__       =    writer.finalize()
         print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"							)





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int] ):
    try:
        A__       =    json.load(_lowerCamelCase )
        try:
            A__       =    list(data.keys() )
        except Exception:
            A__       =    [d["img_id"] for d in data]
    except Exception:
        try:
            A__       =    eval(stream.read() )
        except Exception:
            A__       =    stream.read().split("\n" )
    return data


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



