





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, 2_0_4_8), 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]       , _SCREAMING_SNAKE_CASE:			Any=sys.argv[1:]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	1
              UpperCamelCase_							,			UpperCamelCase_ 							=	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"):
                            UpperCamelCase_ 							=	arg
                     elif opt in ("-o", "--outfile"):
                            UpperCamelCase_ 							=	arg
                     elif opt in ("-b", "--batch_size"):
                            UpperCamelCase_ 							=	int(_SCREAMING_SNAKE_CASE		)
                     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(_SCREAMING_SNAKE_CASE		), f'''{outputfile}'''
              if subset_list is not None:
                     with open(os.path.realpath(_SCREAMING_SNAKE_CASE		)		) as f:
                            UpperCamelCase_ 							=	{self._vqa_file_split()[0] for x in tryload(_SCREAMING_SNAKE_CASE		)}
              else:
                     UpperCamelCase_ 							=	None

              UpperCamelCase_ 							=	CONFIG
              if torch.cuda.is_available():
                     UpperCamelCase_ 							=	"cuda"
              UpperCamelCase_ 							=	os.path.realpath(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	os.path.realpath(_SCREAMING_SNAKE_CASE		)
              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 lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			List[Any]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	int(file.split("."		)[0].split("_"		)[-1]		)
              UpperCamelCase_ 							=	os.path.join(self.inputdir       , _SCREAMING_SNAKE_CASE		)
              return (img_id, filepath)

       @property
       def lowercase			(		self:			Union[str, Any]		)							->			Any:




              """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(_SCREAMING_SNAKE_CASE		)		)
                     if len(_SCREAMING_SNAKE_CASE		) == self.batch:
                            UpperCamelCase_ 							=	batch
                            UpperCamelCase_ 							=	[]
                            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:			List[str]		)							->			Tuple:




              """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(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	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"       , )
                     UpperCamelCase_ 							=	output_dict.pop("normalized_boxes"		)
                     if not TEST:
                            UpperCamelCase_ 							=	np.array(_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_ 							=	self.schema.encode_batch(_SCREAMING_SNAKE_CASE		)
                            writer.write_batch(_SCREAMING_SNAKE_CASE		)
                     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 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Tuple:
       try:
              UpperCamelCase_ 							=	json.load(UpperCamelCase_	)
              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)







'''simple docstring'''







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_lowerCamelCase				:		str				      =    'src/transformers'

# Matches is_xxx_available()
_lowerCamelCase				:		str				      =    re.compile(R'is\_([a-z_]*)_available()')
# Matches from xxx import bla
_lowerCamelCase				:		Union[str, Any]				      =    re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
_lowerCamelCase				:		Dict				      =    re.compile(R'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


_lowerCamelCase				:		int				      =    '\n{0} = None\n'

_lowerCamelCase				:		List[Any]				      =    '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


_lowerCamelCase				:		Any				      =    '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		if _re_test_backend.search(lowercase_    ) is None:
				return None
		_snake_case      			=			[b[0] for b in _re_backend.findall(lowercase_    )]
		backends.sort()
		return "_and_".join(lowercase_    )






def 						_lowercase						(      ):



		'''simple docstring'''







		with open(os.path.join(lowercase_		,				"__init__.py"    )		,				"r"		,				encoding="utf-8"		,				newline="\n"    ) as f:
				_snake_case      			=			f.readlines()

		# Get to the point we do the actual imports for type checking
		_snake_case      			=			0
		while not lines[line_index].startswith("if TYPE_CHECKING"    ):
				line_index += 1

		_snake_case      			=			{}
		# Go through the end of the file
		while line_index < len(lowercase_    ):
				# If the line is an if is_backend_available, we grab all objects associated.
				_snake_case      			=			find_backend(lines[line_index]    )
				if backend is not None:
						while not lines[line_index].startswith("    else:"    ):
								line_index += 1
						line_index += 1

						_snake_case      			=			[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(" " * 8    ):
								_snake_case      			=			lines[line_index]
								_snake_case      			=			_re_single_line_import.search(lowercase_    )
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "    )    )
								elif line.startswith(" " * 12    ):
										objects.append(line[12:-2]    )
								line_index += 1

						_snake_case      			=			objects
				else:
						line_index += 1

		return backend_specific_objects






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		if name.isupper():
				return DUMMY_CONSTANT.format(lowercase_    )
		elif name.islower():
				return DUMMY_FUNCTION.format(lowercase_		,				lowercase_    )
		else:
				return DUMMY_CLASS.format(lowercase_		,				lowercase_    )






def 						_lowercase						(      lowercase_=None    ):



		'''simple docstring'''







		if backend_specific_objects is None:
				_snake_case      			=			read_init()
		# For special correspondence backend to module name as used in the function requires_modulename
		_snake_case      			=			{}

		for backend, objects in backend_specific_objects.items():
				_snake_case      			=			"[" + ", ".join(f'''"{b}"''' for b in backend.split("_and_"    )    ) + "]"
				_snake_case      			=			"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
				dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
				dummy_file += "\n".join([create_dummy_object(lowercase_		,				lowercase_    ) for o in objects]    )
				_snake_case      			=			dummy_file

		return dummy_files






def 						_lowercase						(      lowercase_=False    ):



		'''simple docstring'''







		_snake_case      			=			create_dummy_files()
		# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
		_snake_case      			=			{"torch": "pt"}

		# Locate actual dummy modules and read their content.
		_snake_case      			=			os.path.join(lowercase_		,				"utils"    )
		_snake_case      			=			{
		    backend: os.path.join(lowercase_		,				f'''dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py'''    )
		    for backend in dummy_files.keys()
		}

		_snake_case      			=			{}
		for backend, file_path in dummy_file_paths.items():
				if os.path.isfile(lowercase_    ):
						with open(lowercase_		,				"r"		,				encoding="utf-8"		,				newline="\n"    ) as f:
								_snake_case      			=			f.read()
				else:
						_snake_case      			=			""

		for backend in dummy_files.keys():
				if dummy_files[backend] != actual_dummies[backend]:
						if overwrite:
								print(
								    f'''Updating transformers.utils.dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py as the main '''
								    "__init__ has new objects."    )
								with open(dummy_file_paths[backend]		,				"w"		,				encoding="utf-8"		,				newline="\n"    ) as f:
										f.write(dummy_files[backend]    )
						else:
								raise ValueError(
								    "The main __init__ has objects that are not present in "
								    f'''transformers.utils.dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py. Run `make fix-copies` '''
								    "to fix this."    )


if __name__ == "__main__":
						_lowerCamelCase				:		int				      =    argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						_lowerCamelCase				:		List[Any]				      =    parser.parse_args()

						check_dummies(args.fix_and_overwrite)





