



"""simple docstring"""
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


_UpperCAmelCase     =			logging.getLogger(__name__)
torch.set_grad_enabled(False)
_UpperCAmelCase     =			"""cuda""" if torch.cuda.is_available() else """cpu"""





def      __magic_name__				(							lowercase							,			lowercase=100							,			lowercase=" " ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=text.split(lowercase )
   return [character.join(text[i : i + n] ).strip() for i in range(0							,			len(lowercase )							,			lowercase )]





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: int     			=[], []
   for title, text in zip(documents["""title"""]							,			documents["""text"""] ):
      if text is not None:
         for passage in split_text(lowercase ):
            titles.append(title if title is not None else """""" )
            texts.append(lowercase )
   return {"title": titles, "text": texts}





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=ctx_tokenizer(
       documents["""title"""]							,			documents["""text"""]							,			truncation=lowercase							,			padding="""longest"""							,			return_tensors="""pt""" )["""input_ids"""]
   SCREAMING_SNAKE_CASE_: List[Any]     			=ctx_encoder(input_ids.to(device=lowercase )							,			return_dict=lowercase ).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			):
   ######################################
   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
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=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
   SCREAMING_SNAKE_CASE_: str     			=dataset.map(lowercase							,			batched=lowercase							,			num_proc=processing_args.num_proc )

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

   # And finally save your dataset
   SCREAMING_SNAKE_CASE_: str     			=os.path.join(rag_example_args.output_dir							,			"""my_knowledge_dataset""" )
   dataset.save_to_disk(lowercase )
   # 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
   SCREAMING_SNAKE_CASE_: int     			=faiss.IndexHNSWFlat(index_hnsw_args.d							,			index_hnsw_args.m							,			faiss.METRIC_INNER_PRODUCT )
   dataset.add_faiss_index("""embeddings"""							,			custom_index=lowercase )

   # And save the index
   SCREAMING_SNAKE_CASE_: List[Any]     			=os.path.join(rag_example_args.output_dir							,			"""my_knowledge_dataset_hnsw_index.faiss""" )
   dataset.get_index("""embeddings""" ).save(lowercase )
   # dataset.load_faiss_index("embeddings", index_path)  # to reload the index
@dataclass
class 					a	:
  UpperCamelCase	:				str     						=					field(
      default=str(Path(UpperCAmelCase__       ).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=UpperCAmelCase__	,      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(UpperCAmelCase__       ).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=UpperCAmelCase__	,      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)

   _UpperCAmelCase     =			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
   _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase     =			parser.parse_args_into_dataclasses()
   with TemporaryDirectory() as tmp_dir:
      _UpperCAmelCase     =			rag_example_args.output_dir or tmp_dir
      main(rag_example_args, processing_args, index_hnsw_args)








"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 					a	(  yaml.SafeLoader       ):



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					List[str]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=[self.constructed_objects[key_node] for key_node, _ in node.value]
     SCREAMING_SNAKE_CASE_: Any     			=[tuple(lowerCAmelCase		) if isinstance(lowerCAmelCase       ,		lowerCAmelCase		) else key for key in keys]
     SCREAMING_SNAKE_CASE_: Dict     			=Counter(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=[key for key in counter if counter[key] > 1]
     if duplicate_keys:
        raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''		)





  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Optional[int]=False		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=super().construct_mapping(lowerCAmelCase       ,		deep=lowerCAmelCase		)
     self._check_no_duplicates_on_constructed_node(lowerCAmelCase		)
     return mapping







def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=list(readme_content.splitlines() )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=full_content[1:].index("""---""" ) + 1
      SCREAMING_SNAKE_CASE_: List[str]     			="""\n""".join(full_content[1:sep_idx] )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

   return None, "\n".join(lowercase )
class 					a	(  UpperCAmelCase__       ):
  # class attributes
  UpperCamelCase	:				Tuple     						=					{'train_eval_index'}  # train-eval-index in the YAML metadata



  @classmethod
  def 	lowerCamelCase__       (						cls :					List[Any]       ,		lowerCAmelCase :					Path		)     ->					"DatasetMetadata":


     '''simple docstring'''




     with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=_split_yaml_from_readme(readme_file.read()		)
     if yaml_string is not None:
        return cls.from_yaml_string(lowerCAmelCase		)
     else:
        return cls()



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Path		)     ->					List[str]:


     '''simple docstring'''




     if path.exists():
        with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
           SCREAMING_SNAKE_CASE_: str     			=readme_file.read()
     else:
        SCREAMING_SNAKE_CASE_: str     			=None
     SCREAMING_SNAKE_CASE_: Tuple     			=self._to_readme(lowerCAmelCase		)
     with open(lowerCAmelCase       ,		"""w"""       ,		encoding="""utf-8"""		) as readme_file:
        readme_file.write(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Optional[str] = None		)     ->					str:


     '''simple docstring'''




     if readme_content is not None:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=_split_yaml_from_readme(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: List[str]     			="""---\n""" + self.to_yaml_string() + """---\n""" + content
     else:
        SCREAMING_SNAKE_CASE_: List[Any]     			="""---\n""" + self.to_yaml_string() + """---\n"""
     return full_content



  @classmethod
  def 	lowerCamelCase__       (						cls :					Optional[int]       ,		lowerCAmelCase :					str		)     ->					"DatasetMetadata":


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=yaml.load(lowerCAmelCase       ,		Loader=_NoDuplicateSafeLoader		) or {}

     # Convert the YAML keys to DatasetMetadata fields
     SCREAMING_SNAKE_CASE_: List[Any]     			={
         (key.replace("""-"""       ,		"""_"""		) if key.replace("""-"""       ,		"""_"""		) in cls._FIELDS_WITH_DASHES else key): value
         for key, value in metadata_dict.items()
     }
     return cls(**lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     return yaml.safe_dump(
         {
             (key.replace("""_"""       ,		"""-"""		) if key in self._FIELDS_WITH_DASHES else key): value
             for key, value in self.items()
         }       ,		sort_keys=lowerCAmelCase       ,		allow_unicode=lowerCAmelCase       ,		encoding="""utf-8"""       ,		).decode("""utf-8"""		)







_UpperCAmelCase     =			{
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
   from argparse import ArgumentParser

   _UpperCAmelCase     =			ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
   ap.add_argument("""readme_filepath""")
   _UpperCAmelCase     =			ap.parse_args()

   _UpperCAmelCase     =			Path(args.readme_filepath)
   _UpperCAmelCase     =			DatasetMetadata.from_readme(readme_filepath)
   print(dataset_metadata)
   dataset_metadata.to_readme(readme_filepath)



