'''simple docstring'''




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
__SCREAMING_SNAKE_CASE	:					str					=  False
__SCREAMING_SNAKE_CASE	:					str					=  Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE	:					str					=  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_048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)

class      lowerCamelCase_     :
      '''simple docstring'''

      def __init__(	self						:    Optional[int]						, A						:    Tuple=sys.argv[1:]	):
             _UpperCAmelCase					:  List[str]											=   None
             _UpperCAmelCase					:  Union[str, Any]											=   None
             _UpperCAmelCase					:  Optional[Any]											=   None
             _UpperCAmelCase					:  Any											=   1
             _UpperCAmelCase				,  _UpperCAmelCase					:  str											=   getopt.getopt(A						, "i:o:b:s"						, ["inputdir=", "outfile=", "batch_size=", "subset_list="]	)
             for opt, arg in opts:
                    if opt in ("-i", "--inputdir"):
                           _UpperCAmelCase					:  Union[str, Any]											=   arg
                    elif opt in ("-o", "--outfile"):
                           _UpperCAmelCase					:  str											=   arg
                    elif opt in ("-b", "--batch_size"):
                           _UpperCAmelCase					:  Optional[int]											=   int(A	)
                    elif opt in ("-s", "--subset_list"):
                           _UpperCAmelCase					:  List[str]											=   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					:  List[Any]											=   {self._vqa_file_split()[0] for x in tryload(A	)}
             else:
                    _UpperCAmelCase					:  Tuple											=   None

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




      def 					_A     (	self						:    Tuple						, A						:    Optional[int]	):
             _UpperCAmelCase					:  List[str]											=   int(file.split("."	)[0].split("_"	)[-1]	)
             _UpperCAmelCase					:  Dict											=   os.path.join(self.inputdir						, A	)
             return (img_id, filepath)




      @property
      def 					_A     (	self						:    str	):
             _UpperCAmelCase					:  Union[str, Any]											=   []
             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					:  Dict											=   batch
                           _UpperCAmelCase					:  List[Any]											=   []
                           yield list(map(A						, zip(*A	)	)	)

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





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

def      UpperCamelCase_							( _UpperCAmelCase  :				Any   )   ->   int:


       """simple docstring"""







       try:
              _UpperCAmelCase					:  Any											=   json.load(_UpperCAmelCase   )
              try:
                     _UpperCAmelCase					:  int											=   list(data.keys()   )
              except Exception:
                     _UpperCAmelCase					:  Optional[Any]											=   [d["img_id"] for d in data]
       except Exception:
              try:
                     _UpperCAmelCase					:  List[Any]											=   eval(stream.read()   )
              except Exception:
                     _UpperCAmelCase					:  int											=   stream.read().split("\n"   )
       return data


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


'''simple docstring'''




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter

class      lowerCamelCase_     (TensorFormatter[Mapping, np.ndarray, Mapping]   ):
      '''simple docstring'''

      def __init__(	self						:    str						, A						:    List[str]=None						, **A						:    Optional[Any]	):
             super().__init__(features=A	)
             _UpperCAmelCase					:  Tuple											=   np_array_kwargs




      def 					_A     (	self						:    str						, A						:    Tuple	):
             if isinstance(A						, A	):
                    if column and all(
                        isinstance(A						, np.ndarray	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                           return np.stack(A	)
                    else:
                           # don't use np.array(column, dtype=object)
                           # since it fails in certain cases
                           # see https://stackoverflow.com/q/51005699
                           _UpperCAmelCase					:  List[str]											=   np.empty(len(A	)						, dtype=A	)
                           _UpperCAmelCase					:  int											=   column
                           return out
             return column




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             if isinstance(A						, (str, bytes, type(A	))	):
                    return value
             elif isinstance(A						, (np.character, np.ndarray)	) and np.issubdtype(value.dtype						, np.character	):
                    return value
             elif isinstance(A						, np.number	):
                    return value

             _UpperCAmelCase					:  List[str]											=   {}

             if isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.integer	):
                    _UpperCAmelCase					:  str											=   {"dtype": np.intaa}
             elif isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.floating	):
                    _UpperCAmelCase					:  int											=   {"dtype": np.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(A						, PIL.Image.Image	):
                           return np.asarray(A						, **self.np_array_kwargs	)

             return np.asarray(A						, **{**default_dtype, **self.np_array_kwargs}	)




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             # support for torch, tf, jax etc.
             if config.TORCH_AVAILABLE and "torch" in sys.modules:
                    import torch

                    if isinstance(A						, torch.Tensor	):
                           return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
             if hasattr(A						, "__array__"	) and not isinstance(A						, (np.ndarray, np.character, np.number)	):
                    _UpperCAmelCase					:  Dict											=   data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(A						, np.ndarray	):
                    if data_struct.dtype == object:
                           return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             if isinstance(A						, (list, tuple)	):
                    return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             return self._tensorize(A	)




      def 					_A     (	self						:    List[Any]						, A						:    dict	):
             return map_nested(self._recursive_tensorize						, A						, map_list=A	)




      def 					_A     (	self						:    List[str]						, A						:    pa.Table	):
             _UpperCAmelCase					:  str											=   self.numpy_arrow_extractor().extract_row(A	)
             _UpperCAmelCase					:  Dict											=   self.python_features_decoder.decode_row(A	)
             return self.recursive_tensorize(A	)




      def 					_A     (	self						:    str						, A						:    pa.Table	):
             _UpperCAmelCase					:  List[str]											=   self.numpy_arrow_extractor().extract_column(A	)
             _UpperCAmelCase					:  Optional[Any]											=   self.python_features_decoder.decode_column(A						, pa_table.column_names[0]	)
             _UpperCAmelCase					:  Tuple											=   self.recursive_tensorize(A	)
             _UpperCAmelCase					:  Tuple											=   self._consolidate(A	)
             return column





      def 					_A     (	self						:    Union[str, Any]						, A						:    pa.Table	):
             _UpperCAmelCase					:  Any											=   self.numpy_arrow_extractor().extract_batch(A	)
             _UpperCAmelCase					:  Any											=   self.python_features_decoder.decode_batch(A	)
             _UpperCAmelCase					:  List[str]											=   self.recursive_tensorize(A	)
             for column_name in batch:
                    _UpperCAmelCase					:  Optional[Any]											=   self._consolidate(batch[column_name]	)
             return batch

