'''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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


_A      :				Tuple 				=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
_A      :				Tuple 				=    '''cuda''' if torch.cuda.is_available() else '''cpu'''







def 					UpperCamelCase_						( snake_case_	:	str						,		snake_case_	:	int=1_00						,		snake_case_	:	int=" "				) ->					List[str]:






 '''simple docstring'''

 __lowerCAmelCase    							=						text.split(snake_case_				)
 return [character.join(text[i : i + n]				).strip() for i in range(0						,		len(snake_case_				)						,		snake_case_				)]







def 					UpperCamelCase_						( snake_case_	:	dict				) ->					dict:






 '''simple docstring'''

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







def 					UpperCamelCase_						( snake_case_	:	dict						,		snake_case_	:	DPRContextEncoder						,		snake_case_	:	DPRContextEncoderTokenizerFast				) ->					dict:






 '''simple docstring'''

 __lowerCAmelCase    							=						ctx_tokenizer(
     documents["""title"""]						,		documents["""text"""]						,		truncation=snake_case_						,		padding="""longest"""						,		return_tensors="""pt"""				)["""input_ids"""]
 __lowerCAmelCase    							=						ctx_encoder(input_ids.to(device=snake_case_				)						,		return_dict=snake_case_				).pooler_output
 return {"embeddings": embeddings.detach().cpu().numpy()}







def 					UpperCamelCase_						( snake_case_	:	"RagExampleArguments"						,		snake_case_	:	"ProcessingArguments"						,		snake_case_	:	"IndexHnswArguments"						,		) ->					Optional[Any]:






 '''simple docstring'''

 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
 __lowerCAmelCase    							=						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
 __lowerCAmelCase    							=						dataset.map(snake_case_						,		batched=snake_case_						,		num_proc=processing_args.num_proc				)

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

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

 # And save the index
 __lowerCAmelCase    							=						os.path.join(rag_example_args.output_dir						,		"""my_knowledge_dataset_hnsw_index.faiss"""				)
 dataset.get_index("""embeddings"""				).save(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
 __lowerCAmelCase    							=						RagRetriever.from_pretrained(
     rag_example_args.rag_model_name						,		index_name="""custom"""						,		indexed_dataset=snake_case_				)
 __lowerCAmelCase    							=						RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name						,		retriever=snake_case_				)
 __lowerCAmelCase    							=						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"""				)
 ######################################

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







@dataclass
class     _lowercase					:


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	str				     = field(
     default=str(Path(UpperCAmelCase__      ).parent / """test_data""" / """my_knowledge_dataset.csv"""      )  ,							metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}  ,							)
 _SCREAMING_SNAKE_CASE					:	Optional[str]				     = field(
     default=UpperCAmelCase__  ,							metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}  ,							)
 _SCREAMING_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'"""}  ,							)
 _SCREAMING_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'"""
         )
     }  ,							)
 _SCREAMING_SNAKE_CASE					:	Optional[str]				     = field(
     default=UpperCAmelCase__  ,							metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}  ,							)







@dataclass
class     _lowercase					:


 '''simple docstring'''






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


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	int				     = field(
     default=768  ,							metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}  ,							)
 _SCREAMING_SNAKE_CASE					:	int				     = field(
     default=128  ,							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      :				Tuple 				=    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 _A				,					_A				,					_A      :				Union[str, Any] 				=    parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  _A      :				Dict 				=    rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)







'''simple docstring'''

import functools
from math import sqrt
from time import time







def 					UpperCamelCase_						( snake_case_	:	Dict						,		*snake_case_	:	Union[str, Any]						,		**snake_case_	:	List[str]				) ->					List[str]:






 '''simple docstring'''

 __lowerCAmelCase    							=						time()
 __lowerCAmelCase    							=						func(*snake_case_						,		**snake_case_				)
 __lowerCAmelCase    							=						time()
 if int(end - start				) > 0:
  print(f"""{func.__name__} runtime: {(end - start):0.4f} s"""				)
 else:
  print(f"""{func.__name__} runtime: {(end - start) * 10_00:0.4f} ms"""				)
 return output







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 if n < 0:
  raise Exception("""n is negative"""				)
 if n == 0:
  return [0]
 __lowerCAmelCase    							=						[0, 1]
 for _ in range(n - 1				):
  fib.append(fib[-1] + fib[-2]				)
 return fib







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 def fib_recursive_term(snake_case_	:	int				) -> int:
  if i < 0:
   raise Exception("""n is negative"""				)
  if i < 2:
   return i
  return fib_recursive_term(i - 1				) + fib_recursive_term(i - 2				)

 if n < 0:
  raise Exception("""n is negative"""				)
 return [fib_recursive_term(snake_case_				) for i in range(n + 1				)]







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 @functools.cache
 def fib_recursive_term(snake_case_	:	int				) -> int:
  if i < 0:
   raise Exception("""n is negative"""				)
  if i < 2:
   return i
  return fib_recursive_term(i - 1				) + fib_recursive_term(i - 2				)

 if n < 0:
  raise Exception("""n is negative"""				)
 return [fib_recursive_term(snake_case_				) for i in range(n + 1				)]







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 if n < 0:
  raise Exception("""n is negative"""				)
 # Cache must be outside recursuive function
 # other it will reset every time it calls itself.
 __lowerCAmelCase    							=						{0: 0, 1: 1, 2: 1}  # Prefilled cache

 def rec_fn_memoized(snake_case_	:	int				) -> int:
  if num in cache:
   return cache[num]

  __lowerCAmelCase    							=						rec_fn_memoized(num - 1				) + rec_fn_memoized(num - 2				)
  __lowerCAmelCase    							=						value
  return value

 return [rec_fn_memoized(snake_case_				) for i in range(n + 1				)]







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 if n < 0:
  raise Exception("""n is negative"""				)
 if n >= 14_75:
  raise Exception("""n is too large"""				)
 __lowerCAmelCase    							=						sqrt(5				)
 __lowerCAmelCase    							=						(1 + sqrt_a) / 2
 return [round(phi**i / sqrt_a				) for i in range(n + 1				)]


if __name__ == "__main__":
 _A      :				Optional[Any] 				=    30
 time_func(fib_iterative, num)
 time_func(fib_recursive, num)  # Around 3s runtime
 time_func(fib_recursive_cached, num)  # Around 0ms runtime
 time_func(fib_memoization, num)
 time_func(fib_binet, num)






