





import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
     from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
     _UpperCAmelCase										=					None

if is_ray_available():
     import ray  # noqa: E402 # isort:skip
     from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					None




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Optional[Any]:
       if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
              UpperCamelCase_ 							=	unittest.skip("test requires Datasets, Faiss, psutil"	)(UpperCamelCase_	)
       return test_case







@require_distributed_retrieval
class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def lowercase			(		self:			Tuple		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	tempfile.mkdtemp()
              UpperCamelCase_ 							=	8

              # DPR tok
              UpperCamelCase_ 							=	[
                  "[UNK]",
                  "[CLS]",
                  "[SEP]",
                  "[PAD]",
                  "[MASK]",
                  "want",
                  "##want",
                  "##ed",
                  "wa",
                  "un",
                  "runn",
                  "##ing",
                  ",",
                  "low",
                  "lowest",
              ]
              UpperCamelCase_ 							=	os.path.join(self.tmpdirname       , "dpr_tokenizer"		)
              os.makedirs(_SCREAMING_SNAKE_CASE       , exist_ok=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	os.path.join(_SCREAMING_SNAKE_CASE       , DPR_VOCAB_FILES_NAMES["vocab_file"]		)
              with open(self.vocab_file       , "w"       , encoding="utf-8"		) as vocab_writer:
                     vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)

              # BART tok
              UpperCamelCase_ 							=	[
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "\u0120",
                  "\u0120l",
                  "\u0120n",
                  "\u0120lo",
                  "\u0120low",
                  "er",
                  "\u0120lowest",
                  "\u0120newer",
                  "\u0120wider",
                  "<unk>",
              ]
              UpperCamelCase_ 							=	dict(zip(_SCREAMING_SNAKE_CASE       , range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
              UpperCamelCase_ 							=	["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
              UpperCamelCase_ 							=	{"unk_token": "<unk>"}

              UpperCamelCase_ 							=	os.path.join(self.tmpdirname       , "bart_tokenizer"		)
              os.makedirs(_SCREAMING_SNAKE_CASE       , exist_ok=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	os.path.join(_SCREAMING_SNAKE_CASE       , BART_VOCAB_FILES_NAMES["vocab_file"]		)
              UpperCamelCase_ 							=	os.path.join(_SCREAMING_SNAKE_CASE       , BART_VOCAB_FILES_NAMES["merges_file"]		)
              with open(self.vocab_file       , "w"       , encoding="utf-8"		) as fp:
                     fp.write(json.dumps(_SCREAMING_SNAKE_CASE		) + "\n"		)
              with open(self.merges_file       , "w"       , encoding="utf-8"		) as fp:
                     fp.write("\n".join(_SCREAMING_SNAKE_CASE		)		)

       def lowercase			(		self:			List[str]		)							->			DPRQuestionEncoderTokenizer:




              """simple docstring"""


              return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       , "dpr_tokenizer"		)		)

       def lowercase			(		self:			Optional[Any]		)							->			BartTokenizer:




              """simple docstring"""


              return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       , "bart_tokenizer"		)		)

       def lowercase			(		self:			List[str]		)							->			Optional[Any]:




              """simple docstring"""


              shutil.rmtree(self.tmpdirname		)

       def lowercase			(		self:			int		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	Dataset.from_dict(
                  {
                      "id": ["0", "1"],
                      "text": ["foo", "bar"],
                      "title": ["Foo", "Bar"],
                      "embeddings": [np.ones(self.retrieval_vector_size		), 2 * np.ones(self.retrieval_vector_size		)],
                  }		)
              dataset.add_faiss_index("embeddings"       , string_factory="Flat"       , metric_type=faiss.METRIC_INNER_PRODUCT		)
              return dataset

       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			str=12345		)							->			RagPyTorchDistributedRetriever:




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_dummy_dataset()
              UpperCamelCase_ 							=	RagConfig(
                  retrieval_vector_size=self.retrieval_vector_size       , question_encoder=DPRConfig().to_dict()       , generator=BartConfig().to_dict()       , )
              with patch("transformers.models.rag.retrieval_rag.load_dataset"		) as mock_load_dataset:
                     UpperCamelCase_ 							=	dataset
                     UpperCamelCase_ 							=	RagPyTorchDistributedRetriever(
                         _SCREAMING_SNAKE_CASE       , question_encoder_tokenizer=self.get_dpr_tokenizer()       , generator_tokenizer=self.get_bart_tokenizer()       , )
                     if init_retrieval:
                            retriever.init_retrieval(_SCREAMING_SNAKE_CASE		)
              return retriever

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			bool		)							->			RagRayDistributedRetriever:




              """simple docstring"""


              ray.init(local_mode=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	RagConfig(
                  retrieval_vector_size=self.retrieval_vector_size       , question_encoder=DPRConfig().to_dict()       , generator=BartConfig().to_dict()       , )
              UpperCamelCase_ 							=	ray.remote(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	[remote_cls.remote() for _ in range(1		)]
              with patch("transformers.models.rag.retrieval_rag.load_dataset"		) as mock_load_dataset:
                     UpperCamelCase_ 							=	self.get_dummy_dataset()
                     UpperCamelCase_ 							=	RagRayDistributedRetriever(
                         _SCREAMING_SNAKE_CASE       , question_encoder_tokenizer=self.get_dpr_tokenizer()       , generator_tokenizer=self.get_bart_tokenizer()       , retrieval_workers=_SCREAMING_SNAKE_CASE       , )
                     if init_retrieval:
                            retriever.init_retrieval()
              return retriever

       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			int=12345		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_dummy_dataset()
              UpperCamelCase_ 							=	RagConfig(
                  retrieval_vector_size=self.retrieval_vector_size       , question_encoder=DPRConfig().to_dict()       , generator=BartConfig().to_dict()       , index_name="custom"       , )
              if from_disk:
                     UpperCamelCase_ 							=	os.path.join(self.tmpdirname       , "dataset"		)
                     UpperCamelCase_ 							=	os.path.join(self.tmpdirname       , "index.faiss"		)
                     dataset.get_index("embeddings"		).save(os.path.join(self.tmpdirname       , "index.faiss"		)		)
                     dataset.drop_index("embeddings"		)
                     dataset.save_to_disk(os.path.join(self.tmpdirname       , "dataset"		)		)
                     del dataset
                     UpperCamelCase_ 							=	RagPyTorchDistributedRetriever(
                         _SCREAMING_SNAKE_CASE       , question_encoder_tokenizer=self.get_dpr_tokenizer()       , generator_tokenizer=self.get_bart_tokenizer()       , )
              else:
                     UpperCamelCase_ 							=	RagPyTorchDistributedRetriever(
                         _SCREAMING_SNAKE_CASE       , question_encoder_tokenizer=self.get_dpr_tokenizer()       , generator_tokenizer=self.get_bart_tokenizer()       , index=CustomHFIndex(config.retrieval_vector_size       , _SCREAMING_SNAKE_CASE		)       , )
              if init_retrieval:
                     retriever.init_retrieval(_SCREAMING_SNAKE_CASE		)
              return retriever

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			bool		)							->			List[str]:




              """simple docstring"""


              ray.init(local_mode=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	self.get_dummy_dataset()
              UpperCamelCase_ 							=	RagConfig(
                  retrieval_vector_size=self.retrieval_vector_size       , question_encoder=DPRConfig().to_dict()       , generator=BartConfig().to_dict()       , index_name="custom"       , )
              UpperCamelCase_ 							=	ray.remote(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	[remote_cls.remote() for _ in range(1		)]
              if from_disk:
                     UpperCamelCase_ 							=	os.path.join(self.tmpdirname       , "dataset"		)
                     UpperCamelCase_ 							=	os.path.join(self.tmpdirname       , "index.faiss"		)
                     dataset.get_index("embeddings"		).save(os.path.join(self.tmpdirname       , "index.faiss"		)		)
                     dataset.drop_index("embeddings"		)
                     dataset.save_to_disk(os.path.join(self.tmpdirname       , "dataset"		)		)
                     del dataset
                     UpperCamelCase_ 							=	RagRayDistributedRetriever(
                         _SCREAMING_SNAKE_CASE       , question_encoder_tokenizer=self.get_dpr_tokenizer()       , generator_tokenizer=self.get_bart_tokenizer()       , retrieval_workers=_SCREAMING_SNAKE_CASE       , index=CustomHFIndex.load_from_disk(
                             vector_size=config.retrieval_vector_size       , dataset_path=config.passages_path       , index_path=config.index_path       , )       , )
              else:
                     UpperCamelCase_ 							=	RagRayDistributedRetriever(
                         _SCREAMING_SNAKE_CASE       , question_encoder_tokenizer=self.get_dpr_tokenizer()       , generator_tokenizer=self.get_bart_tokenizer()       , retrieval_workers=_SCREAMING_SNAKE_CASE       , index=CustomHFIndex(config.retrieval_vector_size       , _SCREAMING_SNAKE_CASE		)       , )
              if init_retrieval:
                     retriever.init_retrieval()
              return retriever

       def lowercase			(		self:			str       , _SCREAMING_SNAKE_CASE:			RagRetriever       , _SCREAMING_SNAKE_CASE:			np.array       , _SCREAMING_SNAKE_CASE:			int		)							->			None:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	retriever.retrieve(_SCREAMING_SNAKE_CASE       , n_docs=_SCREAMING_SNAKE_CASE		)
              self.assertEqual(retrieved_doc_embeds.shape       , (2, n_docs, self.retrieval_vector_size)		)
              self.assertEqual(len(_SCREAMING_SNAKE_CASE		)       , 2		)
              self.assertEqual(sorted(doc_dicts[0]		)       , ["embeddings", "id", "text", "title"]		)
              self.assertEqual(len(doc_dicts[0]["id"]		)       , _SCREAMING_SNAKE_CASE		)
              self.assertEqual(doc_dicts[0]["id"][0]       , "1"		)  # max inner product is reached with second doc
              self.assertEqual(doc_dicts[1]["id"][0]       , "0"		)  # max inner product is reached with first doc
              self.assertListEqual(doc_ids.tolist()       , [[1], [0]]		)

       def lowercase			(		self:			Optional[Any]		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	np.array(
                  [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]       , dtype=np.floataa		)

              self.distributed_retriever_check(
                  self.get_dummy_pytorch_distributed_retriever(init_retrieval=_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			List[Any]		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	np.array(
                  [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]       , dtype=np.floataa		)

              self.distributed_retriever_check(
                  self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_SCREAMING_SNAKE_CASE       , from_disk=_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , )

       def lowercase			(		self:			List[str]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	np.array(
                  [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]       , dtype=np.floataa		)

              self.distributed_retriever_check(
                  self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_SCREAMING_SNAKE_CASE       , from_disk=_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , )

       @require_ray
       def lowercase			(		self:			List[str]		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	np.array(
                  [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]       , dtype=np.floataa		)

              self.distributed_retriever_check(
                  self.get_dummy_ray_distributed_retriever(init_retrieval=_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              ray.shutdown()

       @require_ray
       def lowercase			(		self:			Optional[Any]		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	np.array(
                  [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]       , dtype=np.floataa		)
              with self.assertRaises(_SCREAMING_SNAKE_CASE		):
                     self.distributed_retriever_check(
                         self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_SCREAMING_SNAKE_CASE       , from_disk=_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , )
              ray.shutdown()



       @require_ray
       def lowercase			(		self:			Optional[Any]		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	np.array(
                  [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]       , dtype=np.floataa		)

              self.distributed_retriever_check(
                  self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_SCREAMING_SNAKE_CASE       , from_disk=_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              ray.shutdown()






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, 2_048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)





class 		__SCREAMING_SNAKE_CASE      :






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__=sys.argv[1:]      ):






					"""simple docstring"""

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

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

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   int(file.split("""."""      )[0].split("""_"""      )[-1]      )
					_snake_case					:							Optional[int]					     =   os.path.join(self.inputdir						,	SCREAMING_SNAKE_CASE__      )
					return (img_id, filepath)







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

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







				def __call__(				self      ):






					"""simple docstring"""

					if not TEST:
						_snake_case					:							Optional[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					:							List[Any]					     =   self.preprocess(SCREAMING_SNAKE_CASE__      )
						_snake_case					:							Tuple					     =   self.model(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	scales_yx=SCREAMING_SNAKE_CASE__						,	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					:							List[str]					     =   np.array(SCREAMING_SNAKE_CASE__      )
							_snake_case					:							int					     =   self.schema.encode_batch(SCREAMING_SNAKE_CASE__      )
							writer.write_batch(SCREAMING_SNAKE_CASE__      )
						if TEST:
							break
						# finalizer the writer
					if not TEST:
						_snake_case     , _snake_case					:							Optional[int]					     =   writer.finalize()
						print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''      )
def   UpperCAmelCase							(		A__     )							->					List[str]:
	try:
		_snake_case					:							Optional[int]					     =   json.load(A__     )
		try:
			_snake_case					:							Union[str, Any]					     =   list(data.keys()     )
		except Exception:
			_snake_case					:							Optional[int]					     =   [d["""img_id"""] for d in data]
	except Exception:
		try:
			_snake_case					:							List[str]					     =   eval(stream.read()     )
		except Exception:
			_snake_case					:							Any					     =   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)





