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







		"""simple docstring"""

		def __init__(	self							:       List[str]   ,     UpperCamelCase__							:       List[str]=sys.argv[1:]	)		->       str:






				"""simple docstring"""







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

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



		def 	lowerCAmelCase							(	self							:       Optional[int]   ,     UpperCamelCase__							:       List[str]	)		->       List[Any]:






				"""simple docstring"""







				snake_case   :    str			       =		int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
				snake_case   :    Optional[Any]			       =		os.path.join(self.inputdir   ,     UpperCamelCase__	)
				return (img_id, filepath)



		@property
		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    Union[str, 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(UpperCamelCase__	)	)
						if len(UpperCamelCase__	) == self.batch:
								snake_case   :    Optional[int]			       =		batch
								snake_case   :    int			       =		[]
								yield list(map(UpperCamelCase__   ,     zip(*UpperCamelCase__	)	)	)

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



		def __call__(	self							:       Dict	)		->       str:






				"""simple docstring"""







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







def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Dict:


		'''simple docstring'''


		try:
				snake_case   :    Optional[int]			       =		json.load(SCREAMING_SNAKE_CASE__       )
				try:
						snake_case   :    Union[str, Any]			       =		list(data.keys()       )
				except Exception:
						snake_case   :    Any			       =		[d['''img_id'''] for d in data]
		except Exception:
				try:
						snake_case   :    Tuple			       =		eval(stream.read()       )
				except Exception:
						snake_case   :    List[Any]			       =		stream.read().split('''\n'''       )
		return data


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



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


lowerCAmelCase__							= logging.get_logger(__name__)

lowerCAmelCase__							= {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__							= {
    'vocab_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__							= {
    'microsoft/mpnet-base': 512,
}

lowerCAmelCase__							= {
    'microsoft/mpnet-base': {'do_lower_case': True},
}
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					VOCAB_FILES_NAMES
 UpperCAmelCase_	 =					PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_	 =					PRETRAINED_INIT_CONFIGURATION
 UpperCAmelCase_	 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_	 =					MPNetTokenizer
 UpperCAmelCase_	 =					['input_ids', 'attention_mask']




 def __init__(			self					:  Dict			,    lowercase__					:  Optional[int]=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  str=True			,    lowercase__					:  int="<s>"			,    lowercase__					:  List[str]="</s>"			,    lowercase__					:  Tuple="</s>"			,    lowercase__					:  Tuple="<s>"			,    lowercase__					:  Dict="[UNK]"			,    lowercase__					:  int="<pad>"			,    lowercase__					:  Union[str, Any]="<mask>"			,    lowercase__					:  Tuple=True			,    lowercase__					:  int=None			,    **lowercase__					:  Union[str, Any]			,    ):







        '''simple docstring'''



        super().__init__(
            lowercase__			,    tokenizer_file=lowercase__			,    do_lower_case=lowercase__			,    bos_token=lowercase__			,    eos_token=lowercase__			,    sep_token=lowercase__			,    cls_token=lowercase__			,    unk_token=lowercase__			,    pad_token=lowercase__			,    mask_token=lowercase__			,    tokenize_chinese_chars=lowercase__			,    strip_accents=lowercase__			,    **lowercase__			,    )

        lowerCAmelCase__     	=					json.loads(self.backend_tokenizer.normalizer.__getstate__())
        if (
            pre_tok_state.get('lowercase'			,    lowercase__) != do_lower_case
            or pre_tok_state.get('strip_accents'			,    lowercase__) != strip_accents
        ):
               lowerCAmelCase__     	=					getattr(lowercase__			,    pre_tok_state.pop('type'))
               lowerCAmelCase__     	=					do_lower_case
               lowerCAmelCase__     	=					strip_accents
               lowerCAmelCase__     	=					pre_tok_class(**lowercase__)

        lowerCAmelCase__     	=					do_lower_case




 @property
 def 		__snake_case						(			self					:  Tuple):







        '''simple docstring'''



        if self._mask_token is None:
               if self.verbose:
                      logger.error('Using mask_token, but it is not set yet.')
               return None
        return str(self._mask_token)




 @mask_token.setter
 def 		__snake_case						(			self					:  Optional[int]			,    lowercase__					:  str):







        '''simple docstring'''



        lowerCAmelCase__     	=					AddedToken(lowercase__			,    lstrip=lowercase__			,    rstrip=lowercase__) if isinstance(lowercase__			,    lowercase__) else value
        lowerCAmelCase__     	=					value




 def 		__snake_case						(			self					:  Tuple			,    lowercase__					:  Union[str, Any]			,    lowercase__					:  Any=None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
               return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




 def 		__snake_case						(			self					:  Optional[int]			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.sep_token_id]
        lowerCAmelCase__     	=					[self.cls_token_id]

        if token_ids_a is None:
               return len(cls + token_ids_a + sep) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]




 def 		__snake_case						(			self					:  Tuple			,    lowercase__					:  str			,    lowercase__					:  Optional[str] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					self._tokenizer.model.save(lowercase__			,    name=lowercase__)
        return tuple(lowercase__)
