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_     (		lowercase:							str  ,					lowercase:							Tuple=100  ,					lowercase:							Optional[Any]=" "		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= text.split(lowercase		)
 return [character.join(text[i : i + n]		).strip() for i in range(0  ,					len(lowercase		)  ,					lowercase		)]




def       lowerCAmelCase_     (		lowercase:							dict		)    ->				dict:







 '''simple docstring'''
 _UpperCamelCase	,							_UpperCamelCase:     str							= [], []
 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       lowerCAmelCase_     (		lowercase:							dict  ,					lowercase:							DPRContextEncoder  ,					lowercase:							DPRContextEncoderTokenizerFast		)    ->				dict:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= ctx_tokenizer(
     documents['''title''']  ,					documents['''text''']  ,					truncation=lowercase  ,					padding='''longest'''  ,					return_tensors='''pt'''		)['''input_ids''']
 _UpperCamelCase:     Optional[Any]							= ctx_encoder(input_ids.to(device=lowercase		)  ,					return_dict=lowercase		).pooler_output
 return {"embeddings": embeddings.detach().cpu().numpy()}




def       lowerCAmelCase_     (		lowercase:							"RagExampleArguments"  ,					lowercase:							"ProcessingArguments"  ,					lowercase:							"IndexHnswArguments"  ,					)    ->				List[str]:







 '''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
 _UpperCamelCase:     Dict							= 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
 _UpperCamelCase:     Optional[int]							= dataset.map(lowercase  ,					batched=lowercase  ,					num_proc=processing_args.num_proc		)

 # And compute the embeddings
 _UpperCamelCase:     Tuple							= DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name		).to(device=lowercase		)
 _UpperCamelCase:     Union[str, Any]							= DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name		)
 _UpperCamelCase:     Union[str, Any]							= Features(
     {'''text''': Value('''string'''		), '''title''': Value('''string'''		), '''embeddings''': Sequence(Value('''float32'''		)		)}		)  # optional, save as float32 instead of float64 to save space
 _UpperCamelCase:     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
 _UpperCamelCase:     Any							= 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
 _UpperCamelCase:     Optional[Any]							= 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
 _UpperCamelCase:     str							= 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  __magic_name__  :





   """simple docstring"""




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




@dataclass
class  __magic_name__  :





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   field(
       default=__a      ,	metadata={
           '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
       }      ,	)
   lowerCAmelCase      :    int  			=   field(
       default=1_6      ,	metadata={
           '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
       }      ,	)




@dataclass
class  __magic_name__  :





   """simple docstring"""




   lowerCAmelCase      :    int  			=   field(
       default=7_6_8      ,	metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}      ,	)
   lowerCAmelCase      :    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)
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= [
     '''encoder.version''',
     '''decoder.version''',
     '''model.encoder.version''',
     '''model.decoder.version''',
     '''decoder.output_projection.weight''',
     '''_float_tensor''',
     '''encoder.embed_positions._float_tensor''',
     '''decoder.embed_positions._float_tensor''',
 ]
 for k in ignore_keys:
  state_dict.pop(lowercase  ,					lowercase		)




def       lowerCAmelCase_     (		lowercase:							int		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase	,							_UpperCamelCase:     Optional[int]							= emb.weight.shape
 _UpperCamelCase:     int							= nn.Linear(lowercase  ,					lowercase  ,					bias=lowercase		)
 _UpperCamelCase:     Any							= emb.weight.data
 return lin_layer




def       lowerCAmelCase_     (		lowercase:							Optional[Any]		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= torch.load(lowercase  ,					map_location='''cpu'''		)
 _UpperCamelCase:     Any							= mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
 _UpperCamelCase:     List[Any]							= mam_aaa['''model''']
 remove_ignore_keys_(lowercase		)
 _UpperCamelCase:     Tuple							= state_dict['''encoder.embed_tokens.weight'''].shape[0]

 _UpperCamelCase:     List[Any]							= MaMaaaConfig(
     vocab_size=lowercase  ,					max_position_embeddings=1_024  ,					encoder_layers=args.encoder_layers  ,					decoder_layers=args.decoder_layers  ,					encoder_attention_heads=args.encoder_attention_heads  ,					decoder_attention_heads=args.decoder_attention_heads  ,					encoder_ffn_dim=args.encoder_ffn_embed_dim  ,					decoder_ffn_dim=args.decoder_ffn_embed_dim  ,					d_model=args.encoder_embed_dim  ,					encoder_layerdrop=args.encoder_layerdrop  ,					decoder_layerdrop=args.decoder_layerdrop  ,					dropout=args.dropout  ,					attention_dropout=args.attention_dropout  ,					activation_dropout=args.activation_dropout  ,					activation_function='''relu'''  ,					)

 _UpperCamelCase:     List[Any]							= state_dict['''decoder.embed_tokens.weight''']
 _UpperCamelCase:     Optional[int]							= MaMaaaForConditionalGeneration(lowercase		)
 model.model.load_state_dict(lowercase  ,					strict=lowercase		)
 _UpperCamelCase:     Optional[Any]							= make_linear_from_emb(model.model.shared		)

 return model


if __name__ == "__main__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
  parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  UpperCAmelCase_			    =							parser.parse_args()
  UpperCAmelCase_			    =							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
  model.save_pretrained(args.pytorch_dump_folder_path)