

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, 2048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)



class     lowercase						:



 '''simple docstring'''


 def __init__(self       ,       __a=sys.argv[1:] ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				None
  UpperCAmelCase__							=				None
  UpperCAmelCase__							=				None
  UpperCAmelCase__							=				1
  UpperCAmelCase__				,    UpperCAmelCase__							=				getopt.getopt(__a       ,       'i:o:b:s'       ,       ['inputdir=', 'outfile=', 'batch_size=', 'subset_list='] )
  for opt, arg in opts:
   if opt in ("-i", "--inputdir"):
    UpperCAmelCase__							=				arg
   elif opt in ("-o", "--outfile"):
    UpperCAmelCase__							=				arg
   elif opt in ("-b", "--batch_size"):
    UpperCAmelCase__							=				int(__a )
   elif opt in ("-s", "--subset_list"):
    UpperCAmelCase__							=				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:
    UpperCAmelCase__							=				{self._vqa_file_split()[0] for x in tryload(__a )}
  else:
   UpperCAmelCase__							=				None

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




 def   UpperCamelCase__							(self       ,       __a ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				int(file.split('.' )[0].split('_' )[-1] )
  UpperCAmelCase__							=				os.path.join(self.inputdir       ,       __a )
  return (img_id, filepath)




 @property
 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				[]
  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:
    UpperCAmelCase__							=				batch
    UpperCAmelCase__							=				[]
    yield list(map(__a       ,       zip(*__a ) ) )

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




 def __call__(self ) ->					Optional[int]:






  """simple docstring"""





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







def 						UpperCamelCase_(			snake_case__:  Optional[Any]   )       -> Union[str, Any]:
 try:
  UpperCAmelCase__							=				json.load(snake_case__   )
  try:
   UpperCAmelCase__							=				list(data.keys()   )
  except Exception:
   UpperCAmelCase__							=				[d['img_id'] for d in data]
 except Exception:
  try:
   UpperCAmelCase__							=				eval(stream.read()   )
  except Exception:
   UpperCAmelCase__							=				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)






import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:






 class 		__magic_name__ :






      @staticmethod
      def      UpperCAmelCase_							(							*_lowercase						,		**_lowercase     )->			Tuple:
          pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class 		__magic_name__ (				unittest.TestCase							):
     UpperCamelCase_     :Union[str, Any]									=	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     UpperCamelCase_     :Any									=	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			List[Any]:
         UpperCamelCase_          =		ImageClassificationPipeline(model=_lowercase						,		image_processor=_lowercase						,		top_k=2     )
         UpperCamelCase_          =		[
             Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
             "http://images.cocodataset.org/val2017/000000039769.jpg",
         ]
         return image_classifier, examples






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			List[str]:
         UpperCamelCase_          =		image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"     )

         self.assertEqual(
             _lowercase						,		[
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
             ]						,		)

         import datasets

         UpperCamelCase_          =		datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,		"image"						,		split="test"     )

         # Accepts URL + PIL.Image + lists
         UpperCamelCase_          =		image_classifier(
             [
                 Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 # RGBA
                 dataset[0]["file"],
                 # LA
                 dataset[1]["file"],
                 # L
                 dataset[2]["file"],
             ]     )
         self.assertEqual(
             _lowercase						,		[
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
             ]						,		)






     @require_torch
     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase     )

         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]						,		)

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ]						,		)






     @require_tf
     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase						,		framework="tf"     )

         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]						,		)

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ]						,		)






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_          =		PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         UpperCamelCase_          =		pipeline(
             "image-classification"						,		model="hf-internal-testing/tiny-random-vit"						,		tokenizer=_lowercase     )

         self.assertIs(image_classifier.tokenizer						,		_lowercase     )






     @slow
     @require_torch
     def      UpperCAmelCase_							(							self     )->			int:
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-conv"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.4_385, "label": "tabby, tabby cat"},
                 {"score": 0.321, "label": "tiger cat"},
                 {"score": 0.0_502, "label": "Egyptian cat"},
                 {"score": 0.0_137, "label": "crib, cot"},
                 {"score": 0.007, "label": "radiator"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-fourier"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.5_658, "label": "tabby, tabby cat"},
                 {"score": 0.1_309, "label": "tiger cat"},
                 {"score": 0.0_722, "label": "Egyptian cat"},
                 {"score": 0.0_707, "label": "remote control, remote"},
                 {"score": 0.0_082, "label": "computer keyboard, keypad"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-learned"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.3_022, "label": "tabby, tabby cat"},
                 {"score": 0.2_362, "label": "Egyptian cat"},
                 {"score": 0.1_856, "label": "tiger cat"},
                 {"score": 0.0_324, "label": "remote control, remote"},
                 {"score": 0.0_096, "label": "quilt, comforter, comfort, puff"},
             ]						,		)
