
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    Optional[int]												=			"""AutoFeatureExtractor"""
						__a     :    Optional[Any]												=			"""Speech2Text2Tokenizer"""


						def __init__(  self  , A  , A	)  ->List[str]:
									super().__init__(A  , A	)
									UpperCAmelCase__		:int 		=    self.feature_extractor
									UpperCAmelCase__		:Union[str, Any] 		=    False


						def __call__(  self  , *A  , **A	)  ->int:
									# For backward compatibility
									if self._in_target_context_manager:
												return self.current_processor(*A  , **A	)

									if "raw_speech" in kwargs:
												warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'	)
												UpperCAmelCase__		:Union[str, Any] 		=    kwargs.pop('raw_speech'	)
									else:
												UpperCAmelCase__		:str 		=    kwargs.pop('audio'  , A	)
									UpperCAmelCase__		:Optional[Any] 		=    kwargs.pop('sampling_rate'  , A	)
									UpperCAmelCase__		:Any 		=    kwargs.pop('text'  , A	)
									if len(A	) > 0:
												UpperCAmelCase__		:Tuple 		=    args[0]
												UpperCAmelCase__		:int 		=    args[1:]

									if audio is None and text is None:
												raise ValueError('You need to specify either an `audio` or `text` input to process.'	)

									if audio is not None:
												UpperCAmelCase__		:Tuple 		=    self.feature_extractor(A  , *A  , sampling_rate=A  , **A	)
									if text is not None:
												UpperCAmelCase__		:int 		=    self.tokenizer(A  , **A	)

									if text is None:
												return inputs
									elif audio is None:
												return encodings
									else:
												UpperCAmelCase__		:List[str] 		=    encodings['input_ids']
												return inputs


						def    A__       (  self  , *A  , **A	)  ->Optional[int]:
									return self.tokenizer.batch_decode(*A  , **A	)


						def    A__       (  self  , *A  , **A	)  ->Optional[Any]:
									return self.tokenizer.decode(*A  , **A	)


						@contextmanager
						def    A__       (  self	)  ->Optional[Any]:
									warnings.warn(
									    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
									    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
									    'your audio inputs, or in a separate call.'	)
									UpperCAmelCase__		:int 		=    True
									UpperCAmelCase__		:Optional[Any] 		=    self.tokenizer
									yield
									UpperCAmelCase__		:List[str] 		=    self.feature_extractor
									UpperCAmelCase__		:List[str] 		=    False







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
__snake_case					:							Optional[int]           =							False
__snake_case					:							Union[str, Any]           =							Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__snake_case					:							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_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)


class 	UpperCamelCase__       :

						'''simple docstring'''



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

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


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


						@property
						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:str 		=    []
									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:
															UpperCAmelCase__		:Optional[Any] 		=    batch
															UpperCAmelCase__		:Tuple 		=    []
															yield list(map(A  , zip(*A	)	)	)

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


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




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			try:
						UpperCAmelCase__		:List[Any] 		=    json.load(SCREAMING_SNAKE_CASE							)
						try:
									UpperCAmelCase__		:Optional[Any] 		=    list(data.keys()							)
						except Exception:
									UpperCAmelCase__		:List[str] 		=    [d['img_id'] for d in data]
			except Exception:
						try:
									UpperCAmelCase__		:str 		=    eval(stream.read()							)
						except Exception:
									UpperCAmelCase__		:Optional[Any] 		=    stream.read().split('\n'							)
			return data


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





