



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


__UpperCAmelCase    	=  {
    '''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     __lowerCamelCase							(   __magic_name__						:	Optional[Any]    ):
							a__:							Tuple									=task_cls()
							a__:							Union[str, Any]									=asdict(__magic_name__    )
							a__:							int									=task_template_from_dict(__magic_name__    )
							assert task == reloaded


class 						lowerCamelCase__			:


						def 	_lowerCamelCase      (			self    :	str      ):
													a__:							List[Any]									=LanguageModeling(text_column="input_text"      )
													assert {"input_text": "text"} == task.column_mapping







						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__:							Optional[int]									=Features({"text": Value("string"      )}      )
													a__:							Dict									={"text_column": "input_text"}
													a__:							List[str]									=LanguageModeling.from_dict(_a      )
													assert "language-modeling" == task.task
													assert input_schema == task.input_schema


class 						lowerCamelCase__			(		_a ):


						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__:							Optional[Any]									=sorted(["pos", "neg"]      )


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


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







						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							Dict									=TextClassification(text_column="input_text"   ,							label_column="input_label"      )
													self.assertEqual(task.label_schema["labels"]   ,							_a      )
													a__:							List[Any]									=task.align_with_features(Features({"input_label": ClassLabel(names=self.labels      )}      )      )
													self.assertEqual(task.label_schema["labels"]   ,							ClassLabel(names=self.labels      )      )


class 						lowerCamelCase__			(		_a ):


						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							Any									=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 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							List[Any]									=Features({"question": Value("string"      ), "context": Value("string"      )}      )
													a__:							Optional[int]									=Features(
													    {
													        "answers": Sequence(
													            {
													                "text": Value("string"      ),
													                "answer_start": Value("int32"      ),
													            }      )
													    }      )
													a__:							List[Any]									={
													    "context_column": "input_input_context",
													    "question_column": "input_question",
													    "answers_column": "input_answers",
													}
													a__:							Optional[int]									=QuestionAnsweringExtractive.from_dict(_a      )
													self.assertEqual("question-answering-extractive"   ,							task.task      )
													self.assertEqual(_a   ,							task.input_schema      )
													self.assertEqual(_a   ,							task.label_schema      )


class 						lowerCamelCase__			(		_a ):


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							List[str]									=Summarization(text_column="input_text"   ,							summary_column="input_summary"      )
													self.assertDictEqual({"input_text": "text", "input_summary": "summary"}   ,							task.column_mapping      )







						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Dict									=Features({"text": Value("string"      )}      )
													a__:							Optional[Any]									=Features({"summary": Value("string"      )}      )
													a__:							List[Any]									={"text_column": "input_text", "summary_column": "input_summary"}
													a__:							Any									=Summarization.from_dict(_a      )
													self.assertEqual("summarization"   ,							task.task      )
													self.assertEqual(_a   ,							task.input_schema      )
													self.assertEqual(_a   ,							task.label_schema      )


class 						lowerCamelCase__			(		_a ):


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







						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							Tuple									=Features({"audio": Audio()}      )
													a__:							str									=Features({"transcription": Value("string"      )}      )
													a__:							Any									={
													    "audio_column": "input_audio",
													    "transcription_column": "input_transcription",
													}
													a__:							Tuple									=AutomaticSpeechRecognition.from_dict(_a      )
													self.assertEqual("automatic-speech-recognition"   ,							task.task      )
													self.assertEqual(_a   ,							task.input_schema      )
													self.assertEqual(_a   ,							task.label_schema      )


class 						lowerCamelCase__			(		_a ):


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Optional[int]									=sorted(["pos", "neg"]      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Union[str, Any]									=AudioClassification(audio_column="input_audio"   ,							label_column="input_label"      )
													self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}   ,							task.column_mapping      )


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Union[str, Any]									=Features({"audio": Audio()}      )
													a__:							List[str]									=Features({"labels": ClassLabel}      )
													a__:							Optional[int]									={
													    "audio_column": "input_image",
													    "label_column": "input_label",
													}
													a__:							List[Any]									=AudioClassification.from_dict(_a      )
													self.assertEqual("audio-classification"   ,							task.task      )
													self.assertEqual(_a   ,							task.input_schema      )
													self.assertEqual(_a   ,							task.label_schema      )







						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							str									=AudioClassification(audio_column="input_audio"   ,							label_column="input_label"      )
													self.assertEqual(task.label_schema["labels"]   ,							_a      )
													a__:							List[Any]									=task.align_with_features(Features({"input_label": ClassLabel(names=self.labels      )}      )      )
													self.assertEqual(task.label_schema["labels"]   ,							ClassLabel(names=self.labels      )      )


class 						lowerCamelCase__			(		_a ):


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							List[Any]									=sorted(["pos", "neg"]      )


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


						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							Tuple									=Features({"image": Image()}      )
													a__:							Optional[int]									=Features({"labels": ClassLabel}      )
													a__:							Tuple									={
													    "image_column": "input_image",
													    "label_column": "input_label",
													}
													a__:							List[str]									=ImageClassification.from_dict(_a      )
													self.assertEqual("image-classification"   ,							task.task      )
													self.assertEqual(_a   ,							task.input_schema      )
													self.assertEqual(_a   ,							task.label_schema      )







						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Optional[int]									=ImageClassification(image_column="input_image"   ,							label_column="input_label"      )
													self.assertEqual(task.label_schema["labels"]   ,							_a      )
													a__:							Optional[int]									=task.align_with_features(Features({"input_label": ClassLabel(names=self.labels      )}      )      )
													self.assertEqual(task.label_schema["labels"]   ,							ClassLabel(names=self.labels      )      )


class 						lowerCamelCase__			(		_a ):


						def 	_lowerCamelCase      (			self    :	str      ):
													a__:							Optional[Any]									=DatasetInfo(task_templates=QuestionAnsweringExtractive()      )
													a__:							List[Any]									=Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}   ,							info=_a      )
													assert isinstance(dataset.info.task_templates   ,							_a      )
													assert len(dataset.info.task_templates      ) == 1

													def keep_task(_a    :	Any      ):
																				return x

													def dont_keep_task(_a    :	int      ):
																				a__:							List[Any]									=deepcopy(_a      )
																				a__:							Optional[Any]									=0
																				return out

													a__:							Optional[Any]									=dataset.map(_a      )
													assert mapped_dataset.info.task_templates == dataset.info.task_templates
													# reload from cache
													a__:							Any									=dataset.map(_a      )
													assert mapped_dataset.info.task_templates == dataset.info.task_templates

													a__:							Union[str, Any]									=dataset.map(_a      )
													assert mapped_dataset.info.task_templates == []
													# reload from cache
													a__:							int									=dataset.map(_a      )
													assert mapped_dataset.info.task_templates == []







						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__:							Any									=Features({"text": Value("string"      ), "label": ClassLabel(names=("pos", "neg")      )}      )
													a__:							Tuple									=TextClassification(text_column="text"   ,							label_column="label"      )
													a__:							Optional[int]									=DatasetInfo(features=_a   ,							task_templates=_a      )
													a__:							Union[str, Any]									=Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]}   ,							info=_a      )

													def process(_a    :	Union[str, Any]      ):
																				return example

													a__:							Optional[Any]									=dataset.remove_columns("label"      )
													a__:							List[str]									=modified_dataset.map(_a      )
													assert mapped_dataset.info.task_templates == []










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, 20_48), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)


class 						lowerCamelCase__			:


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

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


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Dict      ):
													a__:							Tuple									=int(file.split("."      )[0].split("_"      )[-1]      )
													a__:							int									=os.path.join(self.inputdir   ,							_a      )
													return (img_id, filepath)


						@property
						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							List[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:
																											a__:							List[Any]									=batch
																											a__:							Union[str, Any]									=[]
																											yield list(map(_a   ,							zip(*_a      )      )      )

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







						def __call__(			self    :	List[str]      ):
													# make writer
													if not TEST:
																				a__:							int									=datasets.ArrowWriter(features=self.schema   ,							path=self.outputfile      )
													# do file generator
													for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
																				a__				,      a__				,      a__:							Dict									=self.preprocess(_a      )
																				a__:							Optional[Any]									=self.model(
																				    _a   ,							_a   ,							scales_yx=_a   ,							padding="max_detections"   ,							max_detections=self.config.MAX_DETECTIONS   ,							pad_value=0   ,							return_tensors="np"   ,							location="cpu"   ,							)
																				a__:							Dict									=output_dict.pop("normalized_boxes"      )
																				if not TEST:
																											a__:							Tuple									=np.array(_a      )
																											a__:							Union[str, Any]									=self.schema.encode_batch(_a      )
																											writer.write_batch(_a      )
																				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 >> 2_0} mb"      )





def     __lowerCamelCase							(   __magic_name__						:	Union[str, Any]    ):
							try:
														a__:							Any									=json.load(__magic_name__    )
														try:
																					a__:							List[str]									=list(data.keys()    )
														except Exception:
																					a__:							Union[str, Any]									=[d["img_id"] for d in data]
							except Exception:
														try:
																					a__:							Dict									=eval(stream.read()    )
														except Exception:
																					a__:							int									=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)





