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        =       False
_lowerCAmelCase        =       Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_lowerCAmelCase        =       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 ,       __magic_name__=sys.argv[1:]      ):

        """simple docstring"""

        A_							:       Tuple	      =				None
        A_							:       Optional[Any]	      =				None
        A_							:       str	      =				None
        A_							:       str	      =				1
        A_      ,							A_							:       Dict	      =				getopt.getopt(__magic_name__ ,       '''i:o:b:s''' ,       ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']      )
        for opt, arg in opts:
               if opt in ("-i", "--inputdir"):
                      A_							:       Dict	      =				arg
               elif opt in ("-o", "--outfile"):
                      A_							:       Dict	      =				arg
               elif opt in ("-b", "--batch_size"):
                      A_							:       int	      =				int(__magic_name__      )
               elif opt in ("-s", "--subset_list"):
                      A_							:       Dict	      =				arg

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

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

        """simple docstring"""

        A_							:       int	      =				int(file.split('''.'''      )[0].split('''_'''      )[-1]      )
        A_							:       List[str]	      =				os.path.join(self.inputdir ,       __magic_name__      )
        return (img_id, filepath)
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       str	      =				[]
        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(__magic_name__      )      )
               if len(__magic_name__      ) == self.batch:
                      A_							:       List[Any]	      =				batch
                      A_							:       Dict	      =				[]
                      yield list(map(__magic_name__ ,       zip(*__magic_name__      )      )      )

        for i in range(1      ):
               yield list(map(__magic_name__ ,       zip(*__magic_name__      )      )      )
 def __call__(    self      ):

        """simple docstring"""

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


def       a__      (					a							)		->	Any:
       try:
              A_							:       Dict	      =				json.load(a							)
              try:
                     A_							:       Optional[Any]	      =				list(data.keys()							)
              except Exception:
                     A_							:       str	      =				[d['''img_id'''] for d in data]
       except Exception:
              try:
                     A_							:       str	      =				eval(stream.read()							)
              except Exception:
                     A_							:       Any	      =				stream.read().split('''\n'''							)
       return data


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



from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    from ..models.auto.modeling_auto import (
        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
        MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
        MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
    )


_lowerCAmelCase        =       logging.get_logger(__name__)


_lowerCAmelCase        =       Dict[str, Any]
_lowerCAmelCase        =       List[Prediction]






@add_end_docstrings(A__				)
class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 def __init__(    self ,       *__magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        super().__init__(*__magic_name__ ,       **__magic_name__      )

        if self.framework == "tf":
               raise ValueError(f"""The {self.__class__} is only available in PyTorch."""      )

        requires_backends(self ,       '''vision'''      )
        self.check_model_type(
            dict(
                MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()      )      )
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        A_							:       str	      =				{}
        A_							:       List[Any]	      =				{}
        if "subtask" in kwargs:
               A_							:       Optional[int]	      =				kwargs['''subtask''']
               A_							:       Dict	      =				kwargs['''subtask''']
        if "threshold" in kwargs:
               A_							:       Tuple	      =				kwargs['''threshold''']
        if "mask_threshold" in kwargs:
               A_							:       int	      =				kwargs['''mask_threshold''']
        if "overlap_mask_area_threshold" in kwargs:
               A_							:       Optional[Any]	      =				kwargs['''overlap_mask_area_threshold''']

        return preprocess_kwargs, {}, postprocess_kwargs
 def __call__(    self ,       __magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        return super().__call__(__magic_name__ ,       **__magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				load_image(__magic_name__      )
        A_							:       int	      =				[(image.height, image.width)]
        if self.model.config.__class__.__name__ == "OneFormerConfig":
               if subtask is None:
                      A_							:       int	      =				{}
               else:
                      A_							:       str	      =				{'''task_inputs''': [subtask]}
               A_							:       List[Any]	      =				self.image_processor(images=[image] ,       return_tensors='''pt''' ,       **__magic_name__      )
               A_							:       int	      =				self.tokenizer(
                   inputs['''task_inputs'''] ,       padding='''max_length''' ,       max_length=self.model.config.task_seq_len ,       return_tensors=self.framework ,       )['''input_ids''']
        else:
               A_							:       Any	      =				self.image_processor(images=[image] ,       return_tensors='''pt'''      )
        A_							:       Optional[int]	      =				target_size
        return inputs
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        A_							:       Dict	      =				model_inputs.pop('''target_size'''      )
        A_							:       Union[str, Any]	      =				self.model(**__magic_name__      )
        A_							:       Any	      =				target_size
        return model_outputs
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None ,       __magic_name__=0.9 ,       __magic_name__=0.5 ,       __magic_name__=0.5      ):

        """simple docstring"""

        A_							:       Tuple	      =				None
        if subtask in {"panoptic", None} and hasattr(self.image_processor ,       '''post_process_panoptic_segmentation'''      ):
               A_							:       Dict	      =				self.image_processor.post_process_panoptic_segmentation
        elif subtask in {"instance", None} and hasattr(self.image_processor ,       '''post_process_instance_segmentation'''      ):
               A_							:       Tuple	      =				self.image_processor.post_process_instance_segmentation

        if fn is not None:
               A_							:       Dict	      =				fn(
                   __magic_name__ ,       threshold=__magic_name__ ,       mask_threshold=__magic_name__ ,       overlap_mask_area_threshold=__magic_name__ ,       target_sizes=model_outputs['''target_size'''] ,       )[0]

               A_							:       Optional[Any]	      =				[]
               A_							:       Optional[int]	      =				outputs['''segmentation''']

               for segment in outputs["segments_info"]:
                      A_							:       Optional[int]	      =				(segmentation == segment['''id''']) * 255
                      A_							:       int	      =				Image.fromarray(mask.numpy().astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       Union[str, Any]	      =				self.model.config.idalabel[segment['''label_id''']]
                      A_							:       Optional[int]	      =				segment['''score''']
                      annotation.append({'''score''': score, '''label''': label, '''mask''': mask}      )

        elif subtask in {"semantic", None} and hasattr(self.image_processor ,       '''post_process_semantic_segmentation'''      ):
               A_							:       int	      =				self.image_processor.post_process_semantic_segmentation(
                   __magic_name__ ,       target_sizes=model_outputs['''target_size''']      )[0]

               A_							:       Tuple	      =				[]
               A_							:       Any	      =				outputs.numpy()
               A_							:       Optional[Any]	      =				np.unique(__magic_name__      )

               for label in labels:
                      A_							:       Any	      =				(segmentation == label) * 255
                      A_							:       str	      =				Image.fromarray(mask.astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       List[str]	      =				self.model.config.idalabel[label]
                      annotation.append({'''score''': None, '''label''': label, '''mask''': mask}      )
        else:
               raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model      )}"""      )
        return annotation


