


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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=100 , SCREAMING_SNAKE_CASE=" " )->  Optional[int]:




						"""simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




						"""simple docstring"""

						snake_case_	,   snake_case_	=				[], []
						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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Tuple:




						"""simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , )->  Dict:




						"""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
						snake_case_	=				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_	=				dataset.map(UpperCAmelCase_ , batched=UpperCAmelCase_ , num_proc=processing_args.num_proc )

						# And compute the embeddings
						snake_case_	=				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=UpperCAmelCase_ )
						snake_case_	=				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
						snake_case_	=				Features(
						    {'''text''': Value('''string''' ), '''title''': Value('''string''' ), '''embeddings''': Sequence(Value('''float32''' ) )} )  # optional, save as float32 instead of float64 to save space
						snake_case_	=				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_	=				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_	=				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_	=				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 lowerCAmelCase_						:

	'''simple docstring'''

	__snake_case												=  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'"} ,						)
	__snake_case												=  field(
	    default=_lowerCamelCase ,						metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."} ,						)
	__snake_case												=  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												=  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												=  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 lowerCAmelCase_						:

	'''simple docstring'''

	__snake_case												=  field(
	    default=_lowerCamelCase ,						metadata={
	        "help": "The number of processes to use to split the documents into passages. Default is single process."
	    } ,						)
	__snake_case												=  field(
	    default=1_6 ,						metadata={
	        "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
	    } ,						)







@dataclass
class lowerCAmelCase_						:

	'''simple docstring'''

	__snake_case												=  field(
	    default=7_6_8 ,						metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."} ,						)
	__snake_case												=  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      =   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)



# Function to print upper half of diamond (pyramid)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      for i in range(0 , SCREAMING_SNAKE_CASE ):
            for _ in range(0 , n - i - 1 ):  # printing spaces
                  print(''' ''' , end='''''' )
            for _ in range(0 , i + 1 ):  # printing stars
                  print('''* ''' , end='''''' )
            print()






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      for i in range(SCREAMING_SNAKE_CASE , 0 , -1 ):
            for _ in range(SCREAMING_SNAKE_CASE , 0 , -1 ):  # printing stars
                  print('''* ''' , end='''''' )
            print()
            for _ in range(n - i + 1 , 0 , -1 ):  # printing spaces
                  print(''' ''' , end='''''' )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      if n <= 0:
            print('''       ...       ....        nothing printing :(''' )
            return
      floyd(SCREAMING_SNAKE_CASE )  # upper half
      reverse_floyd(SCREAMING_SNAKE_CASE )  # lower half


if __name__ == "__main__":
   print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
   print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
   UpperCAmelCase      =   1
   while K:
      UpperCAmelCase      =   int(input("""enter the number and , and see the magic : """))
      print()
      pretty_print(user_number)
      UpperCAmelCase      =   int(input("""press 0 to exit... and 1 to continue..."""))

   print("""Good Bye...""")