





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


__lowerCamelCase			:	List[Any]					    =	logging.getLogger(__name__)
torch.set_grad_enabled(False)
__lowerCamelCase			:	Any					    =	"""cuda""" if torch.cuda.is_available() else """cpu"""



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase=100				,  _lowerCAmelCase=" "	)		->					List[str]:
 UpperCamelCase      :   Union[str, Any]			      =  text.split(_lowerCAmelCase	)
 return [character.join(text[i : i + n]	).strip() for i in range(0				,  len(_lowerCAmelCase	)				,  _lowerCAmelCase	)]



def 						A_							(	_lowerCAmelCase	)		->					dict:
 UpperCamelCase      ,       UpperCamelCase      :   Tuple			      =  [], []
 for title, text in zip(documents["title"]				,  documents["text"]	):
  if text is not None:
   for passage in split_text(_lowerCAmelCase	):
    titles.append(title if title is not None else ""	)
    texts.append(_lowerCAmelCase	)
 return {"title": titles, "text": texts}



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)		->					dict:
 UpperCamelCase      :   List[Any]			      =  ctx_tokenizer(
     documents["title"]				,  documents["text"]				,  truncation=_lowerCAmelCase				,  padding="longest"				,  return_tensors="pt"	)["input_ids"]
 UpperCamelCase      :   Any			      =  ctx_encoder(input_ids.to(device=_lowerCAmelCase	)				,  return_dict=_lowerCAmelCase	).pooler_output
 return {"embeddings": embeddings.detach().cpu().numpy()}



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  )		->					Optional[int]:
 ######################################
 logger.info("Step 1 - Create the dataset"	)
 ######################################

 # The dataset needed for RAG must have three columns:
 # - title (string): title of the document
 # - text (string): text of a passage of the document
 # - embeddings (array of dimension d): DPR representation of the passage
 # Let's say you have documents in tab-separated csv files with columns "title" and "text"
 assert os.path.isfile(rag_example_args.csv_path	), "Please provide a valid path to a csv file"

 # You can load a Dataset object this way
 UpperCamelCase      :   Tuple			      =  load_dataset(
     "csv"				,  data_files=[rag_example_args.csv_path]				,  split="train"				,  delimiter="\t"				,  column_names=["title", "text"]	)

 # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

 # Then split the documents into passages of 100 words
 UpperCamelCase      :   Dict			      =  dataset.map(_lowerCAmelCase				,  batched=_lowerCAmelCase				,  num_proc=processing_args.num_proc	)

 # And compute the embeddings
 UpperCamelCase      :   str			      =  DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	).to(device=_lowerCAmelCase	)
 UpperCamelCase      :   Tuple			      =  DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	)
 UpperCamelCase      :   Union[str, Any]			      =  Features(
     {"text": Value("string"	), "title": Value("string"	), "embeddings": Sequence(Value("float32"	)	)}	)  # optional, save as float32 instead of float64 to save space
 UpperCamelCase      :   Optional[Any]			      =  dataset.map(
     partial(_lowerCAmelCase				,  ctx_encoder=_lowerCAmelCase				,  ctx_tokenizer=_lowerCAmelCase	)				,  batched=_lowerCAmelCase				,  batch_size=processing_args.batch_size				,  features=_lowerCAmelCase				,  )

 # And finally save your dataset
 UpperCamelCase      :   List[Any]			      =  os.path.join(rag_example_args.output_dir				,  "my_knowledge_dataset"	)
 dataset.save_to_disk(_lowerCAmelCase	)
 # from datasets import load_from_disk
 # dataset = load_from_disk(passages_path)  # to reload the dataset

 ######################################
 logger.info("Step 2 - Index the dataset"	)
 ######################################

 # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
 UpperCamelCase      :   Union[str, Any]			      =  faiss.IndexHNSWFlat(index_hnsw_args.d				,  index_hnsw_args.m				,  faiss.METRIC_INNER_PRODUCT	)
 dataset.add_faiss_index("embeddings"				,  custom_index=_lowerCAmelCase	)

 # And save the index
 UpperCamelCase      :   List[Any]			      =  os.path.join(rag_example_args.output_dir				,  "my_knowledge_dataset_hnsw_index.faiss"	)
 dataset.get_index("embeddings"	).save(_lowerCAmelCase	)
 # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class    A__  :
      _UpperCAmelCase      :str		       =  field(
          default=str(Path(__snake_case     ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'     )			,  metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}			,  )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default=__snake_case			,  metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}			,  )
      _UpperCAmelCase      :str		       =  field(
          default='facebook/rag-sequence-nq'			,  metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}			,  )
      _UpperCAmelCase      :str		       =  field(
          default='facebook/dpr-ctx_encoder-multiset-base'			,  metadata={
              'help': (
                  'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
                  ' \'facebook/dpr-ctx_encoder-multiset-base\''
              )
          }			,  )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default=str(Path(__snake_case     ).parent / 'test_run' / 'dummy-kb'     )			,  metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}			,  )



@dataclass
class    A__  :
      _UpperCAmelCase      :Optional[int]		       =  field(
          default=__snake_case			,  metadata={
              'help': 'The number of processes to use to split the documents into passages. Default is single process.'
          }			,  )
      _UpperCAmelCase      :int		       =  field(
          default=1_6			,  metadata={
              'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
          }			,  )



@dataclass
class    A__  :
      _UpperCAmelCase      :int		       =  field(
          default=7_6_8			,  metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}			,  )
      _UpperCAmelCase      :int		       =  field(
          default=1_2_8			,  metadata={
              'help': (
                  'The number of bi-directional links created for every new element during the HNSW index construction.'
              )
          }			,  )


if __name__ == "__main__":
    logging.basicConfig(level=logging.WARNING)
    logger.setLevel(logging.INFO)

    __lowerCamelCase			:	Optional[Any]					    =	HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    __lowerCamelCase    ,      __lowerCamelCase    ,      __lowerCamelCase			:	Dict					    =	parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        __lowerCamelCase			:	Tuple					    =	rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)







import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
    from PIL import Image
else:



    class    A__  :





          @staticmethod
          def 			__UpperCamelCase(					*A_			,				**A_  ):
           '''simple docstring'''




           pass



def 						A_							(	_lowerCAmelCase	)		->					str:
 UpperCamelCase      :   Tuple			      =  hashlib.mda(image.tobytes()	)
 return m.hexdigest()[:10]



def 						A_							(	_lowerCAmelCase	)		->					Dict:
 UpperCamelCase      :   Dict			      =  np.array(_lowerCAmelCase	)
 UpperCamelCase      :   Union[str, Any]			      =  npimg.shape
 return {"hash": hashimage(_lowerCAmelCase	), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class    A__  (			unittest.TestCase     ):
      _UpperCAmelCase      :Any		       =  dict(
          (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])     )
      _UpperCAmelCase      :str		       =  dict(
          (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])     )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  MaskGenerationPipeline(model=A_			,				image_processor=A_  )
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]





      def 			__UpperCamelCase(					self			,				A_			,				A_  ):
       '''simple docstring'''




       pass





      @require_tf
      @unittest.skip("Image segmentation not implemented in TF"  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @slow
      @require_torch
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[Any]			      =  pipeline("mask-generation"			,				model="facebook/sam-vit-huge"  )

       UpperCamelCase      :   Tuple			      =  image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"			,				points_per_batch=256  )

       # Shortening by hashing
       UpperCamelCase      :   str			      =  []
       for i, o in enumerate(outputs["masks"]  ):
        new_outupt += [{"mask": mask_to_test_readable(A_  ), "scores": outputs["scores"][i]}]

       # fmt: off
       self.assertEqual(
           nested_simplify(A_			,				decimals=4  )			,				[
               {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
               {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
               {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
               {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
               {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
               {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
               {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
               {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
               {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
               {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
               {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
               {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
               {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
               {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
               {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
               {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
               {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
               {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
               {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
               {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
               {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
               {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
               {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
               {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
               {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
               {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
               {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
               {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
               {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
               {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
           ]			,				)
       # fmt: on





      @require_torch
      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  "facebook/sam-vit-huge"
       UpperCamelCase      :   str			      =  pipeline("mask-generation"			,				model=A_  )

       UpperCamelCase      :   str			      =  image_segmenter(
           "http://images.cocodataset.org/val2017/000000039769.jpg"			,				pred_iou_thresh=1			,				points_per_batch=256  )

       # Shortening by hashing
       UpperCamelCase      :   Tuple			      =  []
       for i, o in enumerate(outputs["masks"]  ):
        new_outupt += [{"mask": mask_to_test_readable(A_  ), "scores": outputs["scores"][i]}]

       self.assertEqual(
           nested_simplify(A_			,				decimals=4  )			,				[
               {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
               {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
               {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
               {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
               {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
           ]			,				)
