



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, 2_048), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)







class 				a_    :

						def __init__(       self      :Dict  ,		_lowercase      :Dict=sys.argv[1:])						->   Tuple:
												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	1
												UpperCAmelCase_				, UpperCAmelCase_					=	getopt.getopt(_lowercase  ,		'''i:o:b:s'''  ,		['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list='''])
												for opt, arg in opts:
																		if opt in ("-i", "--inputdir"):
																								UpperCAmelCase_					=	arg
																		elif opt in ("-o", "--outfile"):
																								UpperCAmelCase_					=	arg
																		elif opt in ("-b", "--batch_size"):
																								UpperCAmelCase_					=	int(_lowercase)
																		elif opt in ("-s", "--subset_list"):
																								UpperCAmelCase_					=	arg

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

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

						def       __a			(       self      :int  ,		_lowercase      :str)						->   Optional[Any]:
												UpperCAmelCase_					=	int(file.split('''.''')[0].split('''_''')[-1])
												UpperCAmelCase_					=	os.path.join(self.inputdir  ,		_lowercase)
												return (img_id, filepath)

						@property
						def       __a			(       self      :Dict)						->   int:
												UpperCAmelCase_					=	[]
												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(_lowercase))
																		if len(_lowercase) == self.batch:
																								UpperCAmelCase_					=	batch
																								UpperCAmelCase_					=	[]
																								yield list(map(_lowercase  ,		zip(*_lowercase)))

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




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







def 							A     (						__UpperCAmelCase      )			->			Optional[int]:



						'''simple docstring'''






						try:
												UpperCAmelCase_					=	json.load(__UpperCAmelCase      )
												try:
																		UpperCAmelCase_					=	list(data.keys()      )
												except Exception:
																		UpperCAmelCase_					=	[d['''img_id'''] for d in data]
						except Exception:
												try:
																		UpperCAmelCase_					=	eval(stream.read()      )
												except Exception:
																		UpperCAmelCase_					=	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)










import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







@require_tokenizers
class 				a_    (						unittest.TestCase							):
						UpperCamelCase__ :		Any										=EsmTokenizer

						def       __a			(       self      :Optional[int])						->   Optional[int]:
												super().setUp()
												UpperCAmelCase_					=	tempfile.mkdtemp()
												# fmt: off
												UpperCAmelCase_					=	["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
												# fmt: on
												UpperCAmelCase_					=	os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES['''vocab_file'''])
												with open(self.vocab_file  ,		'''w'''  ,		encoding='''utf-8''') as vocab_writer:
																		vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))

						def       __a			(       self      :List[str]  ,		**_lowercase      :Union[str, Any])						->   List[PreTrainedTokenizerBase]:
												return [self.get_tokenizer(**_lowercase)]

						def       __a			(       self      :Optional[int]  ,		**_lowercase      :List[str])						->   PreTrainedTokenizer:
												return self.tokenizer_class.from_pretrained(self.tmpdirname  ,		**_lowercase)

						def       __a			(       self      :int)						->   Any:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	tokenizer.tokenize('''LAGVS''')
												self.assertListEqual(_lowercase  ,		['''L''', '''A''', '''G''', '''V''', '''S'''])
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase)  ,		[4, 5, 6, 7, 8])

						def       __a			(       self      :List[Any])						->   Optional[int]:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	'''LAGVS'''
												self.assertListEqual(tokenizer.encode(_lowercase)  ,		[0, 4, 5, 6, 7, 8, 2])

						def       __a			(       self      :Tuple)						->   Union[str, Any]:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	['''LAGVS''', '''WCB''']
												UpperCAmelCase_					=	tokenizer(_lowercase  ,		padding=_lowercase)['''input_ids''']

												self.assertListEqual(_lowercase  ,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]])

						def       __a			(       self      :str)						->   Any:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	['''LAGVS''', '''WCB''']
												UpperCAmelCase_					=	tokenizer(_lowercase  ,		padding=_lowercase)['''input_ids''']

												self.assertListEqual(_lowercase  ,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]])




						def       __a			(       self      :Dict)						->   Optional[int]:
												UpperCAmelCase_					=	self.get_tokenizers(fast=_lowercase)
												for tokenizer in tokenizers:
																		with self.subTest(f"{tokenizer.__class__.__name__}"):
																								UpperCAmelCase_					=	'''<unk>'''
																								UpperCAmelCase_					=	'''<mask>'''

																								UpperCAmelCase_					=	tokenizer.tokenize(_lowercase)
																								UpperCAmelCase_					=	tokenizer.tokenize(_lowercase)

																								self.assertEqual(len(_lowercase)  ,		1)
																								self.assertEqual(len(_lowercase)  ,		1)
																								self.assertEqual(token_a[0]  ,		_lowercase)
																								self.assertEqual(token_a[0]  ,		_lowercase)





