
import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def     _a						(							snake_case__: str ):


				'''simple docstring'''
				lowercase_					  = min(re.search('''^ +'''						,       snake_case__ ).end() if t.startswith(''' ''' ) else 0 for t in string.splitlines() )
				return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(snake_case__ )] )


__a        = '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

__a        = '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


__a        = '\\n# Begin of markdown\n\nSome cool dataset card\n'


class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (	self  :      Dict	)	->					List[Any]:
					with tempfile.TemporaryDirectory() as tmp_dir:
									lowercase_					  = Path(SCREAMING_SNAKE_CASE_	) / '''README.md'''
									with open(SCREAMING_SNAKE_CASE_	,							'''w+'''	) as readme_file:
													readme_file.write(SCREAMING_SNAKE_CASE_	)
									lowercase_					  = DatasetMetadata.from_readme(SCREAMING_SNAKE_CASE_	)
									self.assertDictEqual(SCREAMING_SNAKE_CASE_	,							{'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}	)

									with open(SCREAMING_SNAKE_CASE_	,							'''w+'''	) as readme_file:
													readme_file.write(SCREAMING_SNAKE_CASE_	)
									lowercase_					  = DatasetMetadata.from_readme(SCREAMING_SNAKE_CASE_	)
									self.assertDictEqual(SCREAMING_SNAKE_CASE_	,							{}	)

									with open(SCREAMING_SNAKE_CASE_	,							'''w+'''	) as readme_file:
													readme_file.write(SCREAMING_SNAKE_CASE_	)
									lowercase_					  = DatasetMetadata.from_readme(SCREAMING_SNAKE_CASE_	)
									self.assertEqual(SCREAMING_SNAKE_CASE_	,							{}	)




	def 						_lowercase  (	self  :      List[Any]	)	->					Dict:
					lowercase_					  = _dedent(
					    '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''	)
					assert DatasetMetadata.from_yaml_string(SCREAMING_SNAKE_CASE_	)

					lowercase_					  = _dedent(
					    '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''	)
					with self.assertRaises(SCREAMING_SNAKE_CASE_	):
									DatasetMetadata.from_yaml_string(SCREAMING_SNAKE_CASE_	)

					lowercase_					  = _dedent(
					    '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''	)
					assert DatasetMetadata.from_yaml_string(SCREAMING_SNAKE_CASE_	)






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        = False
__a        = Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__a        = 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 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=sys.argv[1:]	)	->					int:
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = 1
					lowercase_  ,     lowercase_					  = getopt.getopt(SCREAMING_SNAKE_CASE_	,							'''i:o:b:s'''	,							['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']	)
					for opt, arg in opts:
									if opt in ("-i", "--inputdir"):
													lowercase_					  = arg
									elif opt in ("-o", "--outfile"):
													lowercase_					  = arg
									elif opt in ("-b", "--batch_size"):
													lowercase_					  = int(SCREAMING_SNAKE_CASE_	)
									elif opt in ("-s", "--subset_list"):
													lowercase_					  = arg

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

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

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




	def __call__(	self  :      Any	)	->					List[str]:
					# make writer
					if not TEST:
									lowercase_					  = datasets.ArrowWriter(features=self.schema	,							path=self.outputfile	)
					# do file generator
					for i, (img_ids, filepaths) in enumerate(self.file_generator	):
									lowercase_  ,     lowercase_  ,     lowercase_					  = self.preprocess(SCREAMING_SNAKE_CASE_	)
									lowercase_					  = self.model(
									    SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							scales_yx=SCREAMING_SNAKE_CASE_	,							padding='''max_detections'''	,							max_detections=self.config.MAX_DETECTIONS	,							pad_value=0	,							return_tensors='''np'''	,							location='''cpu'''	,							)
									lowercase_					  = output_dict.pop('''normalized_boxes'''	)
									if not TEST:
													lowercase_					  = np.array(SCREAMING_SNAKE_CASE_	)
													lowercase_					  = self.schema.encode_batch(SCREAMING_SNAKE_CASE_	)
													writer.write_batch(SCREAMING_SNAKE_CASE_	)
									if TEST:
													break
									# finalizer the writer
					if not TEST:
									lowercase_  ,     lowercase_					  = writer.finalize()
									print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''	)




def     a						(							snake_case__: Optional[Any] ):


				'''simple docstring'''
				try:
								lowercase_					  = json.load(snake_case__ )
								try:
												lowercase_					  = list(data.keys() )
								except Exception:
												lowercase_					  = [d['''img_id'''] for d in data]
				except Exception:
								try:
												lowercase_					  = eval(stream.read() )
								except Exception:
												lowercase_					  = stream.read().split('''\n''' )
				return data


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




