




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    :				Any    						=	False
__A    :				Tuple    						=	Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__A    :				Optional[Any]    						=	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       UpperCAmelCase_   :




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						Optional[int]=sys.argv[1:]      )      ->       int:
										SCREAMING_SNAKE_CASE     =       None
										SCREAMING_SNAKE_CASE     =       None
										SCREAMING_SNAKE_CASE     =       None
										SCREAMING_SNAKE_CASE     =       1
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       getopt.getopt(a							,  """i:o:b:s"""							,  ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]      )
										for opt, arg in opts:
													if opt in ("-i", "--inputdir"):
																SCREAMING_SNAKE_CASE     =       arg
													elif opt in ("-o", "--outfile"):
																SCREAMING_SNAKE_CASE     =       arg
													elif opt in ("-b", "--batch_size"):
																SCREAMING_SNAKE_CASE     =       int(a      )
													elif opt in ("-s", "--subset_list"):
																SCREAMING_SNAKE_CASE     =       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:
																SCREAMING_SNAKE_CASE     =       {self._vqa_file_split()[0] for x in tryload(a      )}
										else:
													SCREAMING_SNAKE_CASE     =       None

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





							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						List[Any]      )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       int(file.split("""."""      )[0].split("""_"""      )[-1]      )
										SCREAMING_SNAKE_CASE     =       os.path.join(self.inputdir							,  a      )
										return (img_id, filepath)





							@property
							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       []
										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:
																SCREAMING_SNAKE_CASE     =       batch
																SCREAMING_SNAKE_CASE     =       []
																yield list(map(a							,  zip(*a      )      )      )

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







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





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


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












import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


__A    :				Union[str, Any]    						=	get_logger(__name__)
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			return TestCommand(
			    args.dataset       ,						args.name       ,						args.cache_dir       ,						args.data_dir       ,						args.all_configs       ,						args.save_info or args.save_infos       ,						args.ignore_verifications       ,						args.force_redownload       ,						args.clear_cache       ,						)




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							a__    	=  False  # to tell pytest it's not a test class





							@staticmethod
							def  _UpperCAmelCase  (       a							:						ArgumentParser      )      ->       str:
										SCREAMING_SNAKE_CASE     =       parser.add_parser("""test"""							,  help="""Test dataset implementation."""      )
										test_parser.add_argument("""--name"""							,  type=a							,  default=a							,  help="""Dataset processing name"""      )
										test_parser.add_argument(
										    """--cache_dir"""							,  type=a							,  default=a							,  help="""Cache directory where the datasets are stored."""							,  )
										test_parser.add_argument(
										    """--data_dir"""							,  type=a							,  default=a							,  help="""Can be used to specify a manual directory to get the files from."""							,  )
										test_parser.add_argument("""--all_configs"""							,  action="""store_true"""							,  help="""Test all dataset configurations"""      )
										test_parser.add_argument(
										    """--save_info"""							,  action="""store_true"""							,  help="""Save the dataset infos in the dataset card (README.md)"""      )
										test_parser.add_argument(
										    """--ignore_verifications"""							,  action="""store_true"""							,  help="""Run the test without checksums and splits checks."""							,  )
										test_parser.add_argument("""--force_redownload"""							,  action="""store_true"""							,  help="""Force dataset redownload"""      )
										test_parser.add_argument(
										    """--clear_cache"""							,  action="""store_true"""							,  help="""Remove downloaded files and cached datasets after each config test"""							,  )
										# aliases
										test_parser.add_argument("""--save_infos"""							,  action="""store_true"""							,  help="""alias to save_info"""      )
										test_parser.add_argument("""dataset"""							,  type=a							,  help="""Name of the dataset to download"""      )
										test_parser.set_defaults(func=_test_command_factory      )





							def __init__(       self							:						List[Any]							,  a							:						str							,  a							:						str							,  a							:						str							,  a							:						str							,  a							:						bool							,  a							:						bool							,  a							:						bool							,  a							:						bool							,  a							:						bool							,  )      ->       List[Any]:
										SCREAMING_SNAKE_CASE     =       dataset
										SCREAMING_SNAKE_CASE     =       name
										SCREAMING_SNAKE_CASE     =       cache_dir
										SCREAMING_SNAKE_CASE     =       data_dir
										SCREAMING_SNAKE_CASE     =       all_configs
										SCREAMING_SNAKE_CASE     =       save_infos
										SCREAMING_SNAKE_CASE     =       ignore_verifications
										SCREAMING_SNAKE_CASE     =       force_redownload
										SCREAMING_SNAKE_CASE     =       clear_cache
										if clear_cache and not cache_dir:
													print(
													    """When --clear_cache is used, specifying a cache directory is mandatory.\n"""
													    """The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"""
													    """Please provide a --cache_dir that will be used to test the dataset script."""      )
													exit(1      )
										if save_infos:
													SCREAMING_SNAKE_CASE     =       True







							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Optional[int]:
										fl_logger().setLevel(a      )
										if self._name is not None and self._all_configs:
													print("""Both parameters `config` and `all_configs` can't be used at once."""      )
													exit(1      )
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self._dataset, self._name
										SCREAMING_SNAKE_CASE     =       dataset_module_factory(a      )
										SCREAMING_SNAKE_CASE     =       import_main_class(module.module_path      )
										SCREAMING_SNAKE_CASE     =       len(builder_cls.BUILDER_CONFIGS      ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

										def get_builders() -> Generator[DatasetBuilder, None, None]:
													if self._all_configs and builder_cls.BUILDER_CONFIGS:
																for i, config in enumerate(builder_cls.BUILDER_CONFIGS      ):
																			if "config_name" in module.builder_kwargs:
																						yield builder_cls(
																						    cache_dir=self._cache_dir							,  data_dir=self._data_dir							,  **module.builder_kwargs							,  )
																			else:
																						yield builder_cls(
																						    config_name=config.name							,  cache_dir=self._cache_dir							,  data_dir=self._data_dir							,  **module.builder_kwargs							,  )
													else:
																if "config_name" in module.builder_kwargs:
																			yield builder_cls(cache_dir=self._cache_dir							,  data_dir=self._data_dir							,  **module.builder_kwargs      )
																else:
																			yield builder_cls(
																			    config_name=a							,  cache_dir=self._cache_dir							,  data_dir=self._data_dir							,  **module.builder_kwargs							,  )

										for j, builder in enumerate(get_builders()      ):
													print(f"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"""      )
													SCREAMING_SNAKE_CASE     =       os.path.exists(
													    os.path.join(builder.get_imported_module_dir()							,  datasets.config.DATASETDICT_INFOS_FILENAME      )      )  # record checksums only if we need to update a (deprecated) dataset_infos.json
													builder.download_and_prepare(
													    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
													    if not self._force_redownload
													    else DownloadMode.FORCE_REDOWNLOAD							,  verification_mode=VerificationMode.NO_CHECKS
													    if self._ignore_verifications
													    else VerificationMode.ALL_CHECKS							,  try_from_hf_gcs=a							,  )
													builder.as_dataset()
													if self._save_infos:
																builder._save_infos()

													# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
													# The dataset_infos are saved in the YAML part of the README.md

													# Let's move it to the original directory of the dataset script, to allow the user to
													# upload them on S3 at the same time afterwards.
													if self._save_infos:
																SCREAMING_SNAKE_CASE     =       os.path.join(builder_cls.get_imported_module_dir()							,  """README.md"""      )
																SCREAMING_SNAKE_CASE     =       Path(a      ).name + """.py"""
																SCREAMING_SNAKE_CASE     =       os.path.join(a							,  a      )
																if os.path.isfile(a      ):
																			SCREAMING_SNAKE_CASE     =       os.path.dirname(a      )
																elif os.path.isfile(a      ):
																			SCREAMING_SNAKE_CASE     =       path
																elif os.path.isdir(a      ):  # for local directories containing only data files
																			SCREAMING_SNAKE_CASE     =       path
																else:  # in case of a remote dataset
																			SCREAMING_SNAKE_CASE     =       None
																			print(f"""Dataset card saved at {dataset_readme_path}"""      )

																# Move dataset_info back to the user
																if dataset_dir is not None:
																			SCREAMING_SNAKE_CASE     =       os.path.join(a							,  """README.md"""      )
																			copyfile(a							,  a      )
																			print(f"""Dataset card saved at {user_dataset_readme_path}"""      )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
													if self._clear_cache:
																if os.path.isdir(builder._cache_dir      ):
																			logger.warning(f"""Clearing cache at {builder._cache_dir}"""      )
																			rmtree(builder._cache_dir      )
																SCREAMING_SNAKE_CASE     =       os.path.join(self._cache_dir							,  datasets.config.DOWNLOADED_DATASETS_DIR      )
																if os.path.isdir(a      ):
																			logger.warning(f"""Clearing cache at {download_dir}"""      )
																			rmtree(a      )

										print("""Test successful."""      )






