





'''simple docstring'''



import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


_SCREAMING_SNAKE_CASE    =			get_logger(__name__)


if TYPE_CHECKING:
	from .builder import DatasetBuilder




def       _lowerCAmelCase  ( lowerCamelCase_	:     Union[str, Any]						,				lowerCamelCase_	:     Dict = None ):
			__lowercase   =							importlib.import_module(lowerCamelCase_ )

			# TODO(QL): always update the module to add subsequent new authentication
			if hasattr(lowerCamelCase_						,				'''_patched_for_streaming''' ) and module._patched_for_streaming:
						return

			def wrap_auth(lowerCamelCase_	:     Dict ):
						@wraps(lowerCamelCase_ )
						def wrapper(*lowerCamelCase_	:     Tuple						,				**lowerCamelCase_	:     Optional[Any] ):
									return function(*lowerCamelCase_						,				use_auth_token=lowerCamelCase_						,				**lowerCamelCase_ )

						__lowercase   =							"""wrap_auth"""
						return wrapper

			# open files in a streaming fashion
			patch_submodule(lowerCamelCase_						,				'''open'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			patch_submodule(lowerCamelCase_						,				'''os.listdir'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			patch_submodule(lowerCamelCase_						,				'''os.walk'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			patch_submodule(lowerCamelCase_						,				'''glob.glob'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			# allow to navigate in remote zip files
			patch_submodule(lowerCamelCase_						,				'''os.path.join'''						,				lowerCamelCase_ ).start()
			patch_submodule(lowerCamelCase_						,				'''os.path.dirname'''						,				lowerCamelCase_ ).start()
			patch_submodule(lowerCamelCase_						,				'''os.path.basename'''						,				lowerCamelCase_ ).start()
			patch_submodule(lowerCamelCase_						,				'''os.path.relpath'''						,				lowerCamelCase_ ).start()
			patch_submodule(lowerCamelCase_						,				'''os.path.split'''						,				lowerCamelCase_ ).start()
			patch_submodule(lowerCamelCase_						,				'''os.path.splitext'''						,				lowerCamelCase_ ).start()
			# allow checks on paths
			patch_submodule(lowerCamelCase_						,				'''os.path.exists'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			patch_submodule(lowerCamelCase_						,				'''os.path.isdir'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			patch_submodule(lowerCamelCase_						,				'''os.path.isfile'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			patch_submodule(lowerCamelCase_						,				'''os.path.getsize'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			patch_submodule(lowerCamelCase_						,				'''pathlib.Path'''						,				lowerCamelCase_ ).start()
			# file readers
			patch_submodule(lowerCamelCase_						,				'''gzip.open'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			patch_submodule(lowerCamelCase_						,				'''numpy.load'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			patch_submodule(lowerCamelCase_						,				'''pandas.read_csv'''						,				wrap_auth(lowerCamelCase_ )						,				attrs=['''__version__'''] ).start()
			patch_submodule(lowerCamelCase_						,				'''pandas.read_excel'''						,				wrap_auth(lowerCamelCase_ )						,				attrs=['''__version__'''] ).start()
			patch_submodule(lowerCamelCase_						,				'''scipy.io.loadmat'''						,				wrap_auth(lowerCamelCase_ )						,				attrs=['''__version__'''] ).start()
			patch_submodule(lowerCamelCase_						,				'''xml.etree.ElementTree.parse'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			patch_submodule(lowerCamelCase_						,				'''xml.dom.minidom.parse'''						,				wrap_auth(lowerCamelCase_ ) ).start()
			__lowercase   =							True




def       _lowerCAmelCase  ( lowerCamelCase_	:     int ):
			extend_module_for_streaming(builder.__module__						,				use_auth_token=builder.use_auth_token )
			# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
			if not builder.__module__.startswith('''datasets.''' ):  # check that it's not a packaged builder like csv
						for imports in get_imports(inspect.getfile(builder.__class__ ) ):
									if imports[0] == "internal":
												__lowercase   =							imports[1]
												__lowercase   =							""".""".join(builder.__module__.split('''.''' )[:-1] + [internal_import_name] )
												extend_module_for_streaming(lowerCamelCase_						,				use_auth_token=builder.use_auth_token )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
			from .builder import DatasetBuilder

			__lowercase   =							[
			    cls.__module__
			    for cls in type(lowerCamelCase_ ).__mro__[1:]  # make sure it's not the same module we've already patched
			    if issubclass(cls						,				lowerCamelCase_ ) and cls.__module__ != DatasetBuilder.__module__
			]  # check it's not a standard builder from datasets.builder
			for module in parent_builder_modules:
						extend_module_for_streaming(lowerCamelCase_						,				use_auth_token=builder.use_auth_token )







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_  :       Tuple					      =     False
lowerCamelCase_  :       int					      =     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowerCamelCase_  :       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, 20_48), 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       :				Optional[Any]  ,		snake_case_       :				Optional[Any]=sys.argv[1:]					):
							UpperCamelCase_:							str		=  None
							UpperCamelCase_:							List[str]		=  None
							UpperCamelCase_:							Tuple		=  None
							UpperCamelCase_:							Tuple		=  1
							UpperCamelCase_,      UpperCamelCase_:							Optional[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"):
															UpperCamelCase_:							Dict		=  arg
											elif opt in ("-o", "--outfile"):
															UpperCamelCase_:							Optional[Any]		=  arg
											elif opt in ("-b", "--batch_size"):
															UpperCamelCase_:							List[str]		=  int(snake_case_					)
											elif opt in ("-s", "--subset_list"):
															UpperCamelCase_:							Dict		=  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:
															UpperCamelCase_:							List[Any]		=  {self._vqa_file_split()[0] for x in tryload(snake_case_					)}
							else:
											UpperCamelCase_:							List[str]		=  None

							UpperCamelCase_:							Tuple		=  CONFIG
							if torch.cuda.is_available():
											UpperCamelCase_:							Dict		=  """cuda"""
							UpperCamelCase_:							int		=  os.path.realpath(snake_case_					)
							UpperCamelCase_:							List[str]		=  os.path.realpath(snake_case_					)
							UpperCamelCase_:							str		=  Preprocess(self.config					)
							UpperCamelCase_:							int		=  GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""  ,		config=self.config					)
							UpperCamelCase_:							Union[str, Any]		=  batch_size if batch_size != 0 else 1
							UpperCamelCase_:							Dict		=  DEFAULT_SCHEMA

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				int					):
							UpperCamelCase_:							int		=  int(file.split("""."""					)[0].split("""_"""					)[-1]					)
							UpperCamelCase_:							int		=  os.path.join(self.inputdir  ,		snake_case_					)
							return (img_id, filepath)

			@property
			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							Dict		=  []
							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:
															UpperCamelCase_:							Dict		=  batch
															UpperCamelCase_:							List[Any]		=  []
															yield list(map(snake_case_  ,		zip(*snake_case_					)					)					)

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




			def __call__(				self       :				Optional[int]					):
							# make writer
							if not TEST:
											UpperCamelCase_:							Optional[Any]		=  datasets.ArrowWriter(features=self.schema  ,		path=self.outputfile					)
							# do file generator
							for i, (img_ids, filepaths) in enumerate(self.file_generator					):
											UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							Dict		=  self.preprocess(snake_case_					)
											UpperCamelCase_:							Optional[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"""  ,		)
											UpperCamelCase_:							Tuple		=  output_dict.pop("""normalized_boxes"""					)
											if not TEST:
															UpperCamelCase_:							Union[str, Any]		=  np.array(snake_case_					)
															UpperCamelCase_:							int		=  self.schema.encode_batch(snake_case_					)
															writer.write_batch(snake_case_					)
											if TEST:
															break
											# finalizer the writer
							if not TEST:
											UpperCamelCase_,      UpperCamelCase_:							int		=  writer.finalize()
											print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''					)







def 				A__    (  lowerCamelCase  )     ->		Tuple:
				try:
								UpperCamelCase_:							str		=  json.load(lowerCamelCase  )
								try:
												UpperCamelCase_:							List[Any]		=  list(data.keys()  )
								except Exception:
												UpperCamelCase_:							Tuple		=  [d["""img_id"""] for d in data]
				except Exception:
								try:
												UpperCamelCase_:							int		=  eval(stream.read()  )
								except Exception:
												UpperCamelCase_:							Union[str, Any]		=  stream.read().split("""\n"""  )
				return data


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






