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
lowerCAmelCase__	:  Optional[Any]						=  False
lowerCAmelCase__	:  List[Any]						=  Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
lowerCAmelCase__	:  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, 20_48), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)





class 					__snake_case    :




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



									'''simple docstring'''







									snake_case__   :      List[Any]   				= None
									snake_case__   :      int   				= None
									snake_case__   :      Optional[Any]   				= None
									snake_case__   :      Any   				= 1
									snake_case__				,    snake_case__   :      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__   :      Union[str, Any]   				= arg
													elif opt in ("-o", "--outfile"):
																	snake_case__   :      Optional[int]   				= arg
													elif opt in ("-b", "--batch_size"):
																	snake_case__   :      Optional[Any]   				= int(__UpperCamelCase						)
													elif opt in ("-s", "--subset_list"):
																	snake_case__   :      Dict   				= 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__   :      Any   				= {self._vqa_file_split()[0] for x in tryload(__UpperCamelCase						)}
									else:
													snake_case__   :      str   				= None

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




					def      __a     (  self       ,			__UpperCamelCase						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= int(file.split('.'						)[0].split('_'						)[-1]						)
									snake_case__   :      Tuple   				= os.path.join(self.inputdir       ,			__UpperCamelCase						)
									return (img_id, filepath)




					@property
					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      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__   :      Any   				= batch
																	snake_case__   :      Tuple   				= []
																	yield list(map(__UpperCamelCase       ,			zip(*__UpperCamelCase						)						)						)

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




					def __call__(  self						)	->    str:



									'''simple docstring'''







									if not TEST:
													snake_case__   :      int   				= 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__   :      Optional[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__   :      List[Any]   				= output_dict.pop('normalized_boxes'						)
													if not TEST:
																	snake_case__   :      int   				= np.array(__UpperCamelCase						)
																	snake_case__   :      List[str]   				= self.schema.encode_batch(__UpperCamelCase						)
																	writer.write_batch(__UpperCamelCase						)
													if TEST:
																	break
													# finalizer the writer
									if not TEST:
													snake_case__				,    snake_case__   :      str   				= writer.finalize()
													print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""						)




def 					UpperCamelCase__		(							A__       )  ->   Optional[Any]:
				try:
								snake_case__   :      Optional[int]   				= json.load(A__       )
								try:
												snake_case__   :      Tuple   				= list(data.keys()       )
								except Exception:
												snake_case__   :      int   				= [d['img_id'] for d in data]
				except Exception:
								try:
												snake_case__   :      Optional[int]   				= eval(stream.read()       )
								except Exception:
												snake_case__   :      int   				= stream.read().split('\n'       )
				return data


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



from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
				from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline


