



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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


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



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		str			,    SCREAMING_SNAKE_CASE__    :		Optional[Any]=100			,    SCREAMING_SNAKE_CASE__    :		List[str]=" "			)					->       List[str]:
 _snake_case      :					Tuple					=   text.split(SCREAMING_SNAKE_CASE__			)
 return [character.join(text[i : i + n]			).strip() for i in range(0			,    len(SCREAMING_SNAKE_CASE__			)			,    SCREAMING_SNAKE_CASE__			)]



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		dict			)					->       dict:
 _snake_case		,      _snake_case      :					List[Any]					=   [], []
 for title, text in zip(documents["""title"""]			,    documents["""text"""]			):
  if text is not None:
   for passage in split_text(SCREAMING_SNAKE_CASE__			):
    titles.append(title if title is not None else """"""			)
    texts.append(SCREAMING_SNAKE_CASE__			)
 return {"title": titles, "text": texts}



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		dict			,    SCREAMING_SNAKE_CASE__    :		DPRContextEncoder			,    SCREAMING_SNAKE_CASE__    :		DPRContextEncoderTokenizerFast			)					->       dict:
 _snake_case      :					Optional[Any]					=   ctx_tokenizer(
     documents["""title"""]			,    documents["""text"""]			,    truncation=SCREAMING_SNAKE_CASE__			,    padding="""longest"""			,    return_tensors="""pt"""			)["""input_ids"""]
 _snake_case      :					Union[str, Any]					=   ctx_encoder(input_ids.to(device=SCREAMING_SNAKE_CASE__			)			,    return_dict=SCREAMING_SNAKE_CASE__			).pooler_output
 return {"embeddings": embeddings.detach().cpu().numpy()}



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		"RagExampleArguments"			,    SCREAMING_SNAKE_CASE__    :		"ProcessingArguments"			,    SCREAMING_SNAKE_CASE__    :		"IndexHnswArguments"			,    )					->       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
 _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      :					List[Any]					=   dataset.map(SCREAMING_SNAKE_CASE__			,    batched=SCREAMING_SNAKE_CASE__			,    num_proc=processing_args.num_proc			)

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

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

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

 ######################################
 logger.info("""Step 3 - Load RAG"""			)
 ######################################

 # Easy way to load the model
 _snake_case      :					int					=   RagRetriever.from_pretrained(
     rag_example_args.rag_model_name			,    index_name="""custom"""			,    indexed_dataset=SCREAMING_SNAKE_CASE__			)
 _snake_case      :					List[str]					=   RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,    retriever=SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Optional[int]					=   RagTokenizer.from_pretrained(rag_example_args.rag_model_name			)

 # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
 # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

 ######################################
 logger.info("""Step 4 - Have fun"""			)
 ######################################

 _snake_case      :					Union[str, Any]					=   rag_example_args.question or """What does Moses' rod turn into ?"""
 _snake_case      :					Tuple					=   tokenizer.question_encoder(SCREAMING_SNAKE_CASE__			,    return_tensors="""pt"""			)["""input_ids"""]
 _snake_case      :					Union[str, Any]					=   model.generate(SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Dict					=   tokenizer.batch_decode(SCREAMING_SNAKE_CASE__			,    skip_special_tokens=SCREAMING_SNAKE_CASE__			)[0]
 logger.info("""Q: """ + question			)
 logger.info("""A: """ + generated_string			)







@dataclass
class 						snake_case  :



   '''simple docstring'''






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







@dataclass
class 						snake_case  :



   '''simple docstring'''






   snake_case_   :	Optional[int]       =						field(
       default=SCREAMING_SNAKE_CASE_   ,metadata={
           """help""": """The number of processes to use to split the documents into passages. Default is single process."""
       }   ,)
   snake_case_   :	int       =						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'''






   snake_case_   :	int       =						field(
       default=7_68   ,metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}   ,)
   snake_case_   :	int       =						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)

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





import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


a__      =		logging.get_logger(__name__)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     Tuple)			->			Optional[int]:

    """simple docstring"""







    super().__init__()
    if version.parse(torch.__version__) < version.parse("""1.12.0"""):
     raise ImportError(
         F'''You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '''
         """PytorchGELUTanh. Please upgrade torch.""")


   def        UpperCamelCase_					(   self							:     Tuple			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return nn.functional.gelu(lowerCAmelCase			,						approximate="""tanh""")







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Optional[int]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044_715 * torch.pow(lowerCAmelCase			,						3.0))))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     str			,						lowerCAmelCase							:     bool = False)			->			Optional[Any]:

    """simple docstring"""







    super().__init__()
    if use_gelu_python:
     _snake_case      :					Optional[int]					=   self._gelu_python
    else:
     _snake_case      :					Any					=   nn.functional.gelu

   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0)))


   def        UpperCamelCase_					(   self							:     str			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return self.act(lowerCAmelCase)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     int			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return 0.5 * input * (1.0 + torch.tanh(input * 0.7_978_845_608 * (1.0 + 0.044_715 * input * input)))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     int			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input * torch.sigmoid(1.702 * input)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     int			,						lowerCAmelCase							:     float			,						lowerCAmelCase							:     float)			->			Optional[int]:

    """simple docstring"""







    if min > max:
     raise ValueError(F'''min should be < max (got min: {min}, max: {max})''')

    super().__init__()
    _snake_case      :					Dict					=   min
    _snake_case      :					Optional[int]					=   max


   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return torch.clip(gelu(lowerCAmelCase)			,						self.min			,						self.max)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     Tuple)			->			List[str]:

    """simple docstring"""







    super().__init__()
    _snake_case      :					Union[str, Any]					=   math.sqrt(2 / math.pi)


   def        UpperCamelCase_					(   self							:     Dict			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044_715 * torch.pow(lowerCAmelCase			,						3))))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Dict			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return nn.functional.silu(lowerCAmelCase)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






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

    """simple docstring"""







    super().__init__()
    if version.parse(torch.__version__) < version.parse("""1.9.0"""):
     _snake_case      :					Optional[int]					=   self._mish_python
    else:
     _snake_case      :					List[str]					=   nn.functional.mish

   def        UpperCamelCase_					(   self							:     str			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input * torch.tanh(nn.functional.softplus(lowerCAmelCase))


   def        UpperCamelCase_					(   self							:     str			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return self.act(lowerCAmelCase)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Optional[Any]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     List[Any]			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     List[Any]=0.707_107			,						lowerCAmelCase							:     Optional[Any]=0.282_095)			->			List[Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   (input - mu).div(sigma * math.sqrt(2.0))
    return 0.5 * (1.0 + torch.erf(lowerCAmelCase))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Any			,						lowerCAmelCase							:     Dict)			->			Union[str, Any]:

    """simple docstring"""







    _snake_case      :					Dict					=   nn.functional.relu(lowerCAmelCase)
    _snake_case      :					Tuple					=   torch.square(lowerCAmelCase)
    return squared







class 						snake_case  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   def __getitem__(   self							:     Dict			,						lowerCAmelCase							:     Tuple)			->			str:

    """simple docstring"""







    _snake_case      :					Optional[Any]					=   super().__getitem__(lowerCAmelCase)
    _snake_case		,      _snake_case      :					List[str]					=   content if isinstance(lowerCAmelCase			,						lowerCAmelCase) else (content, {})
    return cls(**lowerCAmelCase)
a__      =		{
    """gelu""": GELUActivation,
    """gelu_10""": (ClippedGELUActivation, {"""min""": -10, """max""": 10}),
    """gelu_fast""": FastGELUActivation,
    """gelu_new""": NewGELUActivation,
    """gelu_python""": (GELUActivation, {"""use_gelu_python""": True}),
    """gelu_pytorch_tanh""": PytorchGELUTanh,
    """gelu_accurate""": AccurateGELUActivation,
    """laplace""": LaplaceActivation,
    """linear""": LinearActivation,
    """mish""": MishActivation,
    """quick_gelu""": QuickGELUActivation,
    """relu""": nn.ReLU,
    """relu2""": ReLUSquaredActivation,
    """relu6""": nn.ReLUa,
    """sigmoid""": nn.Sigmoid,
    """silu""": SiLUActivation,
    """swish""": SiLUActivation,
    """tanh""": nn.Tanh,
}
a__      =		ClassInstantier(ACTaCLS)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Any			)					->       int:
 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()			)}'''			)


# For backwards compatibility with: from activations import gelu_python
a__      =		get_activation("""gelu_python""")
a__      =		get_activation("""gelu_new""")
a__      =		get_activation("""gelu""")
a__      =		get_activation("""gelu_fast""")
a__      =		get_activation("""quick_gelu""")
a__      =		get_activation("""silu""")
a__      =		get_activation("""mish""")

a__      =		get_activation("""linear""")
