

"""simple docstring"""





from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


_a				   =							{
    'id': '5733be284776f41900661182',
    'title': 'University_of_Notre_Dame',
    'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
    'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
    'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [5_15]},
}




@pytest.mark.parametrize(
    "task_cls",    [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ],    )
def 						_A						(						UpperCamelCase_  : Tuple)			->		Tuple:
   '''simple docstring'''


   __lowercase			=		task_cls()
   __lowercase			=		asdict(UpperCamelCase_)
   __lowercase			=		task_template_from_dict(UpperCamelCase_)
   assert task == reloaded




class       _lowerCAmelCase						:







 """simple docstring"""







 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		LanguageModeling(text_column="input_text"			)
    assert {"input_text": "text"} == task.column_mapping



 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		Features({"text": Value("string"			)}			)
    __lowercase			=		{"text_column": "input_text"}
    __lowercase			=		LanguageModeling.from_dict(UpperCAmelCase__			)
    assert "language-modeling" == task.task
    assert input_schema == task.input_schema







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		sorted(["pos", "neg"]			)


 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		TextClassification(text_column="input_text",      label_column="input_label"			)
    self.assertDictEqual({"input_text": "text", "input_label": "labels"},      task.column_mapping			)


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase			=		Features({"text": Value("string"			)}			)
    # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
    __lowercase			=		Features({"labels": ClassLabel}			)
    __lowercase			=		{"text_column": "input_text", "label_column": "input_labels"}
    __lowercase			=		TextClassification.from_dict(UpperCAmelCase__			)
    self.assertEqual("text-classification",      task.task			)
    self.assertEqual(UpperCAmelCase__,      task.input_schema			)
    self.assertEqual(UpperCAmelCase__,      task.label_schema			)



 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		TextClassification(text_column="input_text",      label_column="input_label"			)
    self.assertEqual(task.label_schema["labels"],      UpperCAmelCase__			)
    __lowercase			=		task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
    self.assertEqual(task.label_schema["labels"],      ClassLabel(names=self.labels			)			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		QuestionAnsweringExtractive(
        context_column="input_context",      question_column="input_question",      answers_column="input_answers"			)
    self.assertDictEqual(
        {"input_context": "context", "input_question": "question", "input_answers": "answers"},      task.column_mapping			)



 def       _lowercase				(    self							:    Any			):
    __lowercase			=		Features({"question": Value("string"			), "context": Value("string"			)}			)
    __lowercase			=		Features(
        {
            "answers": Sequence(
                {
                    "text": Value("string"			),
                    "answer_start": Value("int32"			),
                }			)
        }			)
    __lowercase			=		{
        "context_column": "input_input_context",
        "question_column": "input_question",
        "answers_column": "input_answers",
    }
    __lowercase			=		QuestionAnsweringExtractive.from_dict(UpperCAmelCase__			)
    self.assertEqual("question-answering-extractive",      task.task			)
    self.assertEqual(UpperCAmelCase__,      task.input_schema			)
    self.assertEqual(UpperCAmelCase__,      task.label_schema			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Optional[Any]			):
    __lowercase			=		Summarization(text_column="input_text",      summary_column="input_summary"			)
    self.assertDictEqual({"input_text": "text", "input_summary": "summary"},      task.column_mapping			)



 def       _lowercase				(    self							:    int			):
    __lowercase			=		Features({"text": Value("string"			)}			)
    __lowercase			=		Features({"summary": Value("string"			)}			)
    __lowercase			=		{"text_column": "input_text", "summary_column": "input_summary"}
    __lowercase			=		Summarization.from_dict(UpperCAmelCase__			)
    self.assertEqual("summarization",      task.task			)
    self.assertEqual(UpperCAmelCase__,      task.input_schema			)
    self.assertEqual(UpperCAmelCase__,      task.label_schema			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		AutomaticSpeechRecognition(audio_column="input_audio",      transcription_column="input_transcription"			)
    self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"},      task.column_mapping			)



 def       _lowercase				(    self							:    int			):
    __lowercase			=		Features({"audio": Audio()}			)
    __lowercase			=		Features({"transcription": Value("string"			)}			)
    __lowercase			=		{
        "audio_column": "input_audio",
        "transcription_column": "input_transcription",
    }
    __lowercase			=		AutomaticSpeechRecognition.from_dict(UpperCAmelCase__			)
    self.assertEqual("automatic-speech-recognition",      task.task			)
    self.assertEqual(UpperCAmelCase__,      task.input_schema			)
    self.assertEqual(UpperCAmelCase__,      task.label_schema			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    str			):
    __lowercase			=		sorted(["pos", "neg"]			)


 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		AudioClassification(audio_column="input_audio",      label_column="input_label"			)
    self.assertDictEqual({"input_audio": "audio", "input_label": "labels"},      task.column_mapping			)


 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		Features({"audio": Audio()}			)
    __lowercase			=		Features({"labels": ClassLabel}			)
    __lowercase			=		{
        "audio_column": "input_image",
        "label_column": "input_label",
    }
    __lowercase			=		AudioClassification.from_dict(UpperCAmelCase__			)
    self.assertEqual("audio-classification",      task.task			)
    self.assertEqual(UpperCAmelCase__,      task.input_schema			)
    self.assertEqual(UpperCAmelCase__,      task.label_schema			)



 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		AudioClassification(audio_column="input_audio",      label_column="input_label"			)
    self.assertEqual(task.label_schema["labels"],      UpperCAmelCase__			)
    __lowercase			=		task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
    self.assertEqual(task.label_schema["labels"],      ClassLabel(names=self.labels			)			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		sorted(["pos", "neg"]			)


 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		ImageClassification(image_column="input_image",      label_column="input_label"			)
    self.assertDictEqual({"input_image": "image", "input_label": "labels"},      task.column_mapping			)


 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		Features({"image": Image()}			)
    __lowercase			=		Features({"labels": ClassLabel}			)
    __lowercase			=		{
        "image_column": "input_image",
        "label_column": "input_label",
    }
    __lowercase			=		ImageClassification.from_dict(UpperCAmelCase__			)
    self.assertEqual("image-classification",      task.task			)
    self.assertEqual(UpperCAmelCase__,      task.input_schema			)
    self.assertEqual(UpperCAmelCase__,      task.label_schema			)



 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		ImageClassification(image_column="input_image",      label_column="input_label"			)
    self.assertEqual(task.label_schema["labels"],      UpperCAmelCase__			)
    __lowercase			=		task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
    self.assertEqual(task.label_schema["labels"],      ClassLabel(names=self.labels			)			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    int			):
    __lowercase			=		DatasetInfo(task_templates=QuestionAnsweringExtractive()			)
    __lowercase			=		Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()},      info=UpperCAmelCase__			)
    assert isinstance(dataset.info.task_templates,      UpperCAmelCase__			)
    assert len(dataset.info.task_templates			) == 1

    def keep_task(UpperCAmelCase__							:    Tuple			):
       return x

    def dont_keep_task(UpperCAmelCase__							:    Tuple			):
       __lowercase			=		deepcopy(UpperCAmelCase__			)
       __lowercase			=		0
       return out

    __lowercase			=		dataset.map(UpperCAmelCase__			)
    assert mapped_dataset.info.task_templates == dataset.info.task_templates
    # reload from cache
    __lowercase			=		dataset.map(UpperCAmelCase__			)
    assert mapped_dataset.info.task_templates == dataset.info.task_templates

    __lowercase			=		dataset.map(UpperCAmelCase__			)
    assert mapped_dataset.info.task_templates == []
    # reload from cache
    __lowercase			=		dataset.map(UpperCAmelCase__			)
    assert mapped_dataset.info.task_templates == []



 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		Features({"text": Value("string"			), "label": ClassLabel(names=("pos", "neg")			)}			)
    __lowercase			=		TextClassification(text_column="text",      label_column="label"			)
    __lowercase			=		DatasetInfo(features=UpperCAmelCase__,      task_templates=UpperCAmelCase__			)
    __lowercase			=		Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]},      info=UpperCAmelCase__			)

    def process(UpperCAmelCase__							:    List[str]			):
       return example

    __lowercase			=		dataset.remove_columns("label"			)
    __lowercase			=		modified_dataset.map(UpperCAmelCase__			)
    assert mapped_dataset.info.task_templates == []







"""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
_a				   =							False
_a				   =							Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_a				   =							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       _lowerCAmelCase						:







 """simple docstring"""







 def __init__(    self							:    Optional[int],      UpperCAmelCase__							:    Optional[Any]=sys.argv[1:]			):
    __lowercase			=		None
    __lowercase			=		None
    __lowercase			=		None
    __lowercase			=		1
    __lowercase	,__lowercase			=		getopt.getopt(UpperCAmelCase__,      "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(UpperCAmelCase__			)
       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(UpperCAmelCase__			), F"""{outputfile}"""
    if subset_list is not None:
       with open(os.path.realpath(UpperCAmelCase__			)			) as f:
          __lowercase			=		{self._vqa_file_split()[0] for x in tryload(UpperCAmelCase__			)}
    else:
       __lowercase			=		None

    __lowercase			=		CONFIG
    if torch.cuda.is_available():
       __lowercase			=		"cuda"
    __lowercase			=		os.path.realpath(UpperCAmelCase__			)
    __lowercase			=		os.path.realpath(UpperCAmelCase__			)
    __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       _lowercase				(    self							:    List[str],      UpperCAmelCase__							:    List[Any]			):
    __lowercase			=		int(file.split("."			)[0].split("_"			)[-1]			)
    __lowercase			=		os.path.join(self.inputdir,      UpperCAmelCase__			)
    return (img_id, filepath)


 @property
 def       _lowercase				(    self							:    int			):
    __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(UpperCAmelCase__			)			)
       if len(UpperCAmelCase__			) == self.batch:
          __lowercase			=		batch
          __lowercase			=		[]
          yield list(map(UpperCAmelCase__,      zip(*UpperCAmelCase__			)			)			)

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



 def __call__(    self							:    Optional[Any]			):
    # make writer
    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(UpperCAmelCase__			)
       __lowercase			=		self.model(
           UpperCAmelCase__,      UpperCAmelCase__,      scales_yx=UpperCAmelCase__,      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(UpperCAmelCase__			)
          __lowercase			=		self.schema.encode_batch(UpperCAmelCase__			)
          writer.write_batch(UpperCAmelCase__			)
       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 >> 2_0} mb"""			)







def 						_A						(						UpperCamelCase_  : int)			->		Optional[Any]:
   '''simple docstring'''


   try:
      __lowercase			=		json.load(UpperCamelCase_)
      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__":
   _a				   =							Extract(sys.argv[1:])
   extract()
   if not TEST:
      _a				   =							datasets.Dataset.from_file(extract.outputfile)
      # wala!
      # print(np.array(dataset[0:2]["roi_features"]).shape)




