

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	:       Optional[int]    	=							logging.getLogger(__name__)
torch.set_grad_enabled(False)
__UpperCAmelCase	:       Optional[int]    	=							'cuda' if torch.cuda.is_available() else 'cpu'







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=100 , SCREAMING_SNAKE_CASE__=" ")       ->   List[Any]:
  __snake_case:						List[Any]			  =						text.split(UpperCAmelCase_)
  return [character.join(text[i : i + n]).strip() for i in range(0 , len(UpperCAmelCase_) , UpperCAmelCase_)]







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   int:
  __snake_case  ,       __snake_case:						Optional[Any]			  =						[], []
  for title, text in zip(documents["""title"""] , documents["""text"""]):
    if text is not None:
      for passage in split_text(UpperCAmelCase_):
        titles.append(title if title is not None else """""")
        texts.append(UpperCAmelCase_)
  return {"title": titles, "text": texts}







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Union[str, Any]:
  __snake_case:						Any			  =						ctx_tokenizer(
      documents["""title"""] , documents["""text"""] , truncation=UpperCAmelCase_ , padding="""longest""" , return_tensors="""pt""")["""input_ids"""]
  __snake_case:						List[str]			  =						ctx_encoder(input_ids.to(device=UpperCAmelCase_) , return_dict=UpperCAmelCase_).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , )       ->   Optional[Any]:
  ######################################
  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
  __snake_case:						Optional[int]			  =						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
  __snake_case:						str			  =						dataset.map(UpperCAmelCase_ , batched=UpperCAmelCase_ , num_proc=processing_args.num_proc)

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

  # And finally save your dataset
  __snake_case:						Dict			  =						os.path.join(rag_example_args.output_dir , """my_knowledge_dataset""")
  dataset.save_to_disk(UpperCAmelCase_)
  # 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
  __snake_case:						Optional[int]			  =						faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT)
  dataset.add_faiss_index("""embeddings""" , custom_index=UpperCAmelCase_)

  # And save the index
  __snake_case:						Any			  =						os.path.join(rag_example_args.output_dir , """my_knowledge_dataset_hnsw_index.faiss""")
  dataset.get_index("""embeddings""").save(UpperCAmelCase_)
  # dataset.load_faiss_index("embeddings", index_path)  # to reload the index







@dataclass
class    __snake_case	:





       '''simple docstring'''







       lowerCAmelCase__						=					field(
           default=str(Path(_lowerCamelCase      ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""      )       ,      metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}       ,      )
       lowerCAmelCase__						=					field(
           default=_lowerCamelCase       ,      metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}       ,      )
       lowerCAmelCase__						=					field(
           default="""facebook/rag-sequence-nq"""       ,      metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}       ,      )
       lowerCAmelCase__						=					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'"""
               )
           }       ,      )
       lowerCAmelCase__						=					field(
           default=str(Path(_lowerCamelCase      ).parent / """test_run""" / """dummy-kb"""      )       ,      metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}       ,      )







@dataclass
class    __snake_case	:





       '''simple docstring'''







       lowerCAmelCase__						=					field(
           default=_lowerCamelCase       ,      metadata={
               """help""": """The number of processes to use to split the documents into passages. Default is single process."""
           }       ,      )
       lowerCAmelCase__						=					field(
           default=16       ,      metadata={
               """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
           }       ,      )







@dataclass
class    __snake_case	:





       '''simple docstring'''







       lowerCAmelCase__						=					field(
           default=7_68       ,      metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}       ,      )
       lowerCAmelCase__						=					field(
           default=1_28       ,      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	:       Any    	=							HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      __UpperCAmelCase	:       Any    	=							parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            __UpperCAmelCase	:       str    	=							rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)













from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__UpperCAmelCase	:       str    	=							logging.get_logger(__name__)







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def __init__(  self			:   Any							,    A			:   int							,    A			:   int							,    A			:   float							,    **A			:   Optional[int]		):
         __snake_case:						List[str]			  =						feature_size
         __snake_case:						Optional[int]			  =						sampling_rate
         __snake_case:						Any			  =						padding_value

         __snake_case:						Dict			  =						kwargs.pop("""padding_side"""							,    """right"""		)
         __snake_case:						Union[str, Any]			  =						kwargs.pop("""return_attention_mask"""							,    A		)

         super().__init__(**A		)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   Union[
               BatchFeature,
               List[BatchFeature],
               Dict[str, BatchFeature],
               Dict[str, List[BatchFeature]],
               List[Dict[str, BatchFeature]],
           ]							,    A			:   Union[bool, str, PaddingStrategy] = True							,    A			:   Optional[int] = None							,    A			:   bool = False							,    A			:   Optional[int] = None							,    A			:   Optional[bool] = None							,    A			:   Optional[Union[str, TensorType]] = None							,    ):
         # If we have a list of dicts, let's convert it in a dict of lists
         # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
         if isinstance(A							,    (list, tuple)		) and isinstance(processed_features[0]							,    (dict, BatchFeature)		):
           __snake_case:						Optional[int]			  =						{
               key: [example[key] for example in processed_features] for key in processed_features[0].keys()
           }

         # The model's main input name, usually `input_values`, has be passed for padding
         if self.model_input_names[0] not in processed_features:
           raise ValueError(
               """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
               f''' to this method that includes {self.model_input_names[0]}, but you provided'''
               f''' {list(processed_features.keys()		)}'''		)

         __snake_case:						List[str]			  =						processed_features[self.model_input_names[0]]
         __snake_case:						Any			  =						(
             return_attention_mask if return_attention_mask is not None else self.return_attention_mask
         )

         if len(A		) == 0:
           if return_attention_mask:
             __snake_case:						Union[str, Any]			  =						[]
           return processed_features

         # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
         # and rebuild them afterwards if no return_tensors is specified
         # Note that we lose the specific device the tensor may be on for PyTorch

         __snake_case:						int			  =						required_input[0]
         if isinstance(A							,    (list, tuple)		):
           # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
           __snake_case:						Optional[int]			  =						0
           while len(required_input[index]		) == 0:
             index += 1
           if index < len(A		):
             __snake_case:						Optional[int]			  =						required_input[index][0]

         if return_tensors is None:
           if is_tf_tensor(A		):
             __snake_case:						str			  =						"""tf"""
           elif is_torch_tensor(A		):
             __snake_case:						str			  =						"""pt"""
           elif isinstance(A							,    (int, float, list, tuple, np.ndarray)		):
             __snake_case:						List[str]			  =						"""np"""
           else:
             raise ValueError(
                 f'''type of {first_element} unknown: {type(A		)}. '''
                 """Should be one of a python, numpy, pytorch or tensorflow object."""		)

         for key, value in processed_features.items():
           if isinstance(value[0]							,    (int, float)		):
             __snake_case:						List[Any]			  =						to_numpy(A		)
           else:
             __snake_case:						Union[str, Any]			  =						[to_numpy(A		) for v in value]

        # Convert padding_strategy in PaddingStrategy
         __snake_case:						Union[str, Any]			  =						self._get_padding_strategies(padding=A							,    max_length=A		)

         __snake_case:						Any			  =						processed_features[self.model_input_names[0]]

         __snake_case:						int			  =						len(A		)
         if not all(len(A		) == batch_size for v in processed_features.values()		):
           raise ValueError("""Some items in the output dictionary have a different batch size than others."""		)

         __snake_case:						Union[str, Any]			  =						[]
         for i in range(A		):
           __snake_case:						List[Any]			  =						{k: v[i] for k, v in processed_features.items()}
           # truncation
           __snake_case:						Tuple			  =						self._truncate(
               A							,    max_length=A							,    pad_to_multiple_of=A							,    truncation=A							,    )
           truncated_inputs.append(A		)

         if padding_strategy == PaddingStrategy.LONGEST:
           # make sure that `max_length` cannot be longer than the longest truncated length
           __snake_case:						Optional[Any]			  =						max(len(input_slice[self.model_input_names[0]]		) for input_slice in truncated_inputs		)
           __snake_case:						List[str]			  =						PaddingStrategy.MAX_LENGTH

         __snake_case:						List[Any]			  =						{}
         for i in range(A		):
           # padding
           __snake_case:						Any			  =						self._pad(
               truncated_inputs[i]							,    max_length=A							,    padding_strategy=A							,    pad_to_multiple_of=A							,    return_attention_mask=A							,    )

           for key, value in outputs.items():
             if key not in batch_outputs:
               __snake_case:						Optional[Any]			  =						[]
             if value.dtype is np.dtype(np.floataa		):
               __snake_case:						str			  =						value.astype(np.floataa		)
             batch_outputs[key].append(A		)

         return BatchFeature(A							,    tensor_type=A		)




       def 							UpperCAmelCase__			(  self			:   int							,    A			:   Union[Dict[str, np.ndarray], BatchFeature]							,    A			:   Optional[int] = None							,    A			:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,    A			:   Optional[int] = None							,    A			:   Optional[bool] = None							,    ):
         __snake_case:						List[Any]			  =						processed_features[self.model_input_names[0]]

         if padding_strategy == PaddingStrategy.LONGEST:
           __snake_case:						List[str]			  =						len(A		)

         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           __snake_case:						List[Any]			  =						((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         __snake_case:						Dict			  =						padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A		) < max_length

         if return_attention_mask and "attention_mask" not in processed_features:
           __snake_case:						List[str]			  =						np.ones(len(A		)							,    dtype=np.intaa		)

         if needs_to_be_padded:
           __snake_case:						Any			  =						max_length - len(A		)
           if self.padding_side == "right":
             if return_attention_mask:
               __snake_case:						Optional[int]			  =						np.pad(
                   processed_features["""attention_mask"""]							,    (0, difference)		)
             __snake_case:						Any			  =						((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
             __snake_case:						Union[str, Any]			  =						np.pad(
                 A							,    A							,    """constant"""							,    constant_values=self.padding_value		)
           elif self.padding_side == "left":
             if return_attention_mask:
               __snake_case:						Dict			  =						np.pad(
                   processed_features["""attention_mask"""]							,    (difference, 0)		)
             __snake_case:						Union[str, Any]			  =						((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
             __snake_case:						str			  =						np.pad(
                 A							,    A							,    """constant"""							,    constant_values=self.padding_value		)
           else:
             raise ValueError("""Invalid padding strategy:""" + str(self.padding_side		)		)

         return processed_features




       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   Union[Dict[str, np.ndarray], BatchFeature]							,    A			:   Optional[int] = None							,    A			:   Optional[int] = None							,    A			:   Optional[bool] = None							,    ):
         if not truncation:
           return processed_features
         elif truncation and max_length is None:
           raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""		)

         __snake_case:						List[str]			  =						processed_features[self.model_input_names[0]]

         # find `max_length` that fits `pad_to_multiple_of`
         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           __snake_case:						List[Any]			  =						((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         __snake_case:						Tuple			  =						len(A		) > max_length

         if needs_to_be_truncated:
           __snake_case:						List[Any]			  =						processed_features[self.model_input_names[0]][:max_length]
           if "attention_mask" in processed_features:
             __snake_case:						int			  =						processed_features["""attention_mask"""][:max_length]

         return processed_features






       def 							UpperCAmelCase__			(  self			:   int							,    A			:   int=False							,    A			:   int=None		):

         # Get padding strategy
         if padding is not False:
           if padding is True:
             __snake_case:						Optional[int]			  =						PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
           elif not isinstance(A							,    A		):
             __snake_case:						Optional[int]			  =						PaddingStrategy(A		)
           elif isinstance(A							,    A		):
             __snake_case:						Any			  =						padding
         else:
           __snake_case:						Any			  =						PaddingStrategy.DO_NOT_PAD

         # Set max length if needed
         if max_length is None:
           if padding_strategy == PaddingStrategy.MAX_LENGTH:
             raise ValueError(
                 f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''		)

        # Test if we have a padding value
         if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
           raise ValueError(
               """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
               """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""		)

         return padding_strategy






