
'''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
A__ :		Union[str, Any]					=False
A__ :		List[str]					=Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
A__ :		Dict					=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      :     Optional[int]					, lowerCamelCase      :     Any=sys.argv[1:]							):



   """simple docstring"""





   __A		:  Dict       				=		None
   __A		:  List[Any]       				=		None
   __A		:  str       				=		None
   __A		:  List[str]       				=		1
   __A				,       __A		:  Tuple       				=		getopt.getopt(_lowerCAmelCase					, """i:o:b:s"""					, ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]							)
   for opt, arg in opts:
     if opt in ("-i", "--inputdir"):
       __A		:  int       				=		arg
     elif opt in ("-o", "--outfile"):
       __A		:  Dict       				=		arg
     elif opt in ("-b", "--batch_size"):
       __A		:  Optional[int]       				=		int(_lowerCAmelCase							)
     elif opt in ("-s", "--subset_list"):
       __A		:  Union[str, Any]       				=		arg

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

   __A		:  Union[str, Any]       				=		CONFIG
   if torch.cuda.is_available():
     __A		:  int       				=		"""cuda"""
   __A		:  Dict       				=		os.path.realpath(_lowerCAmelCase							)
   __A		:  Optional[int]       				=		os.path.realpath(_lowerCAmelCase							)
   __A		:  Optional[Any]       				=		Preprocess(self.config							)
   __A		:  str       				=		GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""					, config=self.config							)
   __A		:  Any       				=		batch_size if batch_size != 0 else 1
   __A		:  str       				=		DEFAULT_SCHEMA
 def 		lowercase_( self      :     Optional[Any]					, lowerCamelCase      :     int							):



   """simple docstring"""





   __A		:  List[Any]       				=		int(file.split("""."""							)[0].split("""_"""							)[-1]							)
   __A		:  Any       				=		os.path.join(self.inputdir					, _lowerCAmelCase							)
   return (img_id, filepath)
 @property
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  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(_lowerCAmelCase							)							)
     if len(_lowerCAmelCase							) == self.batch:
       __A		:  Any       				=		batch
       __A		:  List[str]       				=		[]
       yield list(map(_lowerCAmelCase					, zip(*_lowerCAmelCase							)							)							)

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






 def __call__( self      :     Any							):



   """simple docstring"""





   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		:  str       				=		self.preprocess(_lowerCAmelCase							)
     __A		:  Any       				=		self.model(
         _lowerCAmelCase					, _lowerCAmelCase					, scales_yx=_lowerCAmelCase					, padding="""max_detections"""					, max_detections=self.config.MAX_DETECTIONS					, pad_value=0					, return_tensors="""np"""					, location="""cpu"""					, )
     __A		:  Tuple       				=		output_dict.pop("""normalized_boxes"""							)
     if not TEST:
       __A		:  List[Any]       				=		np.array(_lowerCAmelCase							)
       __A		:  Any       				=		self.schema.encode_batch(_lowerCAmelCase							)
       writer.write_batch(_lowerCAmelCase							)
     if TEST:
       break
     # finalizer the writer
   if not TEST:
     __A				,       __A		:  Union[str, Any]       				=		writer.finalize()
     print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"							)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Tuple			)					->							str:





  """simple docstring"""

  try:
    __A		:  str       				=		json.load(__SCREAMING_SNAKE_CASE			)
    try:
      __A		:  int       				=		list(data.keys()			)
    except Exception:
      __A		:  Tuple       				=		[d["""img_id"""] for d in data]
  except Exception:
    try:
      __A		:  Optional[Any]       				=		eval(stream.read()			)
    except Exception:
      __A		:  Optional[int]       				=		stream.read().split("""\n"""			)
  return data


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










'''simple docstring'''


import logging
import random

import ray

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


_lowerCamelCase					:						List[str]					     =							logging.getLogger(__name__)



class 				__UpperCAmelCase	:







		'''simple docstring'''







		def __init__(self    :   Any   ):
			A					=   False







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Any   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   Dict   ):
			if not self.initialized:
				A					=   RagRetriever(
				    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   init_retrieval=_lowerCAmelCase   ,   )
				A					=   True







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







		def 							A  (self    :   List[str]   ):
			# delete the old self.retriever object before assigning the new index
			del self.retriever
			A					=   False







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   Optional[int]   ):
			A	,     A					=   self.retriever._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   self.retriever.index.get_doc_dicts(_lowerCAmelCase   )
			return doc_ids, retrieved_doc_embeds, doc_dicts






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def __init__(self    :   Any   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]=None   ):
			if index is not None and index.is_initialized() and len(_lowerCAmelCase   ) > 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__(
			    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   init_retrieval=_lowerCAmelCase   ,   )

			A					=   retrieval_workers
			A					=   question_encoder_tokenizer
			A					=   generator_tokenizer
			if len(self.retrieval_workers   ) > 0:
				ray.get(
				    [
				        worker.create_rag_retriever.remote(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
				        for worker in self.retrieval_workers
				    ]   )







		def 							A  (self    :   Optional[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 							A  (self    :   int   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]   ):
			if len(self.retrieval_workers   ) > 0:
				# Select a random retrieval actor.
				A					=   self.retrieval_workers[random.randint(0   ,   len(self.retrieval_workers   ) - 1   )]
				A	,     A	,     A					=   ray.get(
				    random_worker.retrieve.remote(_lowerCAmelCase   ,   _lowerCAmelCase   )   )
			else:
				A	,     A					=   self._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
				A					=   self.index.get_doc_dicts(_lowerCAmelCase   )
			return retrieved_doc_embeds, doc_ids, doc_dicts







		@classmethod
		def 							A  (cls    :   Dict   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   str=None   ,   **_lowerCAmelCase    :   List[Any]   ):
			return super(_lowerCAmelCase   ,   cls   ).get_tokenizers(_lowerCAmelCase   ,   _lowerCAmelCase   ,   **_lowerCAmelCase   )







		@classmethod
		def 							A  (cls    :   List[str]   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   List[Any]   ,   _lowerCAmelCase    :   Optional[int]=None   ,   **_lowerCAmelCase    :   Optional[int]   ):
			A					=   kwargs.pop("""config"""   ,   _lowerCAmelCase   ) or RagConfig.from_pretrained(_lowerCAmelCase   ,   **_lowerCAmelCase   )
			A					=   RagTokenizer.from_pretrained(_lowerCAmelCase   ,   config=_lowerCAmelCase   )
			A					=   rag_tokenizer.question_encoder
			A					=   rag_tokenizer.generator

			if indexed_dataset is not None:
				A					=   """custom"""
				A					=   CustomHFIndex(config.retrieval_vector_size   ,   _lowerCAmelCase   )
			else:
				A					=   cls._build_index(_lowerCAmelCase   )

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







		def 							A  (self    :   str   ):
			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					=   self._build_index(self.config   )

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

