"""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
lowerCAmelCase_:       List[str]			     =       False
lowerCAmelCase_:       int			     =       Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
lowerCAmelCase_:       Optional[int]			     =       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  a__     :


   def __init__(  self,					_UpperCAmelCase=sys.argv[1:]	):
        '''simple docstring'''



        lowercase__								=				None
        lowercase__								=				None
        lowercase__								=				None
        lowercase__								=				1
        lowercase__			,    lowercase__								=				getopt.getopt(A_,					"i:o:b:s",					["inputdir=", "outfile=", "batch_size=", "subset_list="]	)
        for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                  lowercase__								=				arg
             elif opt in ("-o", "--outfile"):
                  lowercase__								=				arg
             elif opt in ("-b", "--batch_size"):
                  lowercase__								=				int(A_	)
             elif opt in ("-s", "--subset_list"):
                  lowercase__								=				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:
                  lowercase__								=				{self._vqa_file_split()[0] for x in tryload(A_	)}
        else:
             lowercase__								=				None

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


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



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


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				[]
        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:
                  lowercase__								=				batch
                  lowercase__								=				[]
                  yield list(map(A_,					zip(*A_	)	)	)

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


   def __call__(  self	):
        '''simple docstring'''



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







def        __a (		A   ):






     '''simple docstring'''



     try:
          lowercase__								=				json.load(__A   )
          try:
               lowercase__								=				list(data.keys()   )
          except Exception:
               lowercase__								=				[d["img_id"] for d in data]
     except Exception:
          try:
               lowercase__								=				eval(stream.read()   )
          except Exception:
               lowercase__								=				stream.read().split("\n"   )
     return data


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







from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


A__										=      [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

A__										=      get_logger(__name__)


A__										=      {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    task_template_dict.get("task"						)
    if task_name is None:
        logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(__A						)}"						)
        return None
    SCREAMING_SNAKE_CASE__         =    NAMEaTEMPLATE.get(__A						)
    return template.from_dict(__A						)


