



import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__UpperCAmelCase    	=  logging.getLogger(__name__)


class 						lowerCamelCase__			:


						def __init__(			self    :	Optional[Any]      ):
													a__:							Tuple									=False


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Any   ,							_a    :	Union[str, Any]   ,							_a    :	List[str]   ,							_a    :	Optional[int]      ):
													if not self.initialized:
																				a__:							Any									=RagRetriever(
																				    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							index=_a   ,							init_retrieval=_a   ,							)
																				a__:							str									=True


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													self.retriever.index.init_index()


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													# delete the old self.retriever object before assigning the new index
													del self.retriever
													a__:							Dict									=False







						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	List[Any]   ,							_a    :	Any      ):
													a__				,      a__:							List[str]									=self.retriever._main_retrieve(_a   ,							_a      )
													a__:							List[str]									=self.retriever.index.get_doc_dicts(_a      )
													return doc_ids, retrieved_doc_embeds, doc_dicts


class 						lowerCamelCase__			(		_a ):


						def __init__(			self    :	Optional[int]   ,							_a    :	List[Any]   ,							_a    :	Any   ,							_a    :	Optional[Any]   ,							_a    :	Union[str, Any]   ,							_a    :	str=None      ):
													if index is not None and index.is_initialized() and len(_a      ) > 0:
																				raise ValueError(
																				    "When using Ray for distributed fine-tuning, "
																				    "you'll need to provide the paths instead, "
																				    "as the dataset and the index are loaded "
																				    "separately. More info in examples/rag/use_own_knowledge_dataset.py "      )

													super().__init__(
													    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							index=_a   ,							init_retrieval=_a   ,							)

													a__:							Optional[Any]									=retrieval_workers
													a__:							str									=question_encoder_tokenizer
													a__:							Optional[int]									=generator_tokenizer
													if len(self.retrieval_workers      ) > 0:
																				ray.get(
																				    [
																				        worker.create_rag_retriever.remote(_a   ,							_a   ,							_a   ,							_a      )
																				        for worker in self.retrieval_workers
																				    ]      )


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													logger.info("initializing retrieval"      )

													if len(self.retrieval_workers      ) > 0:
																				ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
													else:
																				# Non-distributed training. Load index into this same process.
																				self.index.init_index()


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	Union[str, Any]   ,							_a    :	Union[str, Any]      ):
													if len(self.retrieval_workers      ) > 0:
																				# Select a random retrieval actor.
																				a__:							Tuple									=self.retrieval_workers[random.randint(0   ,							len(self.retrieval_workers      ) - 1      )]
																				a__				,      a__				,      a__:							Union[str, Any]									=ray.get(
																				    random_worker.retrieve.remote(_a   ,							_a      )      )
													else:
																				a__				,      a__:							List[Any]									=self._main_retrieve(_a   ,							_a      )
																				a__:							List[str]									=self.index.get_doc_dicts(_a      )
													return retrieved_doc_embeds, doc_ids, doc_dicts


						@classmethod
						def 	_lowerCamelCase      (			cls    :	str   ,							_a    :	Union[str, Any]   ,							_a    :	Any=None   ,							**_a    :	Dict      ):
													return super(_a   ,							cls      ).get_tokenizers(_a   ,							_a   ,							**_a      )


						@classmethod
						def 	_lowerCamelCase      (			cls    :	Dict   ,							_a    :	Optional[int]   ,							_a    :	Tuple   ,							_a    :	int=None   ,							**_a    :	List[str]      ):
													a__:							Union[str, Any]									=kwargs.pop("config"   ,							_a      ) or RagConfig.from_pretrained(_a   ,							**_a      )
													a__:							List[Any]									=RagTokenizer.from_pretrained(_a   ,							config=_a      )
													a__:							str									=rag_tokenizer.question_encoder
													a__:							Any									=rag_tokenizer.generator

													if indexed_dataset is not None:
																				a__:							Dict									="custom"
																				a__:							Dict									=CustomHFIndex(config.retrieval_vector_size   ,							_a      )
													else:
																				a__:							List[str]									=cls._build_index(_a      )

													return cls(
													    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							retrieval_workers=_a   ,							index=_a   ,							)







						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													logger.info("re-loading the new dataset with embeddings"      )
													# access from the training loop

													ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]      )

													# build the index object again
													a__:							str									=self._build_index(self.config      )

													ray.get(
													    [
													        worker.create_rag_retriever.remote(
													            self.config   ,							self.question_encoder_tokenizer   ,							self.generator_tokenizer   ,							_a      )
													        for worker in self.retrieval_workers
													    ]      )










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)





