


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase      						=    logging.get_logger(__name__)

__UpperCamelCase      						=    {
    'facebook/opt-125m': 'https://huggingface.co/facebook/opt-125m/blob/main/config.json',
    'facebook/opt-350m': 'https://huggingface.co/facebook/opt-350m/blob/main/config.json',
    'facebook/opt-1.3b': 'https://huggingface.co/facebook/opt-1.3b/blob/main/config.json',
    'facebook/opt-2.7b': 'https://huggingface.co/facebook/opt-2.7b/blob/main/config.json',
    'facebook/opt-6.7b': 'https://huggingface.co/facebook/opt-6.7b/blob/main/config.json',
    'facebook/opt-13b': 'https://huggingface.co/facebook/opt-13b/blob/main/config.json',
}







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			_UpperCamelCase						:   int					=						'opt'
			_UpperCamelCase						:   str					=						['past_key_values']




			def __init__(							self ,  snake_case=50272 ,  snake_case=768 ,  snake_case=12 ,  snake_case=3072 ,  snake_case=2048 ,  snake_case=True ,  snake_case=False ,  snake_case=None ,  snake_case=0.1 ,  snake_case=0.0 ,  snake_case=12 ,  snake_case="relu" ,  snake_case=0.0 ,  snake_case=0.02 ,  snake_case=True ,  snake_case=1 ,  snake_case=2 ,  snake_case=2 ,  snake_case=True ,  snake_case=True ,  **snake_case ,  ):

						'''simple docstring'''


						super().__init__(
						    pad_token_id=snake_case ,  bos_token_id=snake_case ,  eos_token_id=snake_case ,  **snake_case ,  )
						UpperCamelCase__   =    vocab_size
						UpperCamelCase__   =    max_position_embeddings
						UpperCamelCase__   =    num_attention_heads
						UpperCamelCase__   =    word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
						UpperCamelCase__   =    ffn_dim
						UpperCamelCase__   =    hidden_size
						UpperCamelCase__   =    num_hidden_layers
						UpperCamelCase__   =    dropout
						UpperCamelCase__   =    attention_dropout
						UpperCamelCase__   =    activation_function
						UpperCamelCase__   =    init_std
						UpperCamelCase__   =    layerdrop
						UpperCamelCase__   =    use_cache
						UpperCamelCase__   =    do_layer_norm_before
						# We keep these variables at `True` for backward compatibility.
						UpperCamelCase__   =    enable_bias
						UpperCamelCase__   =    layer_norm_elementwise_affine

						# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
						# with checkpoints that have been fine-tuned before transformers v4.20.1
						# see https://github.com/facebookresearch/metaseq/pull/164
						UpperCamelCase__   =    _remove_final_layer_norm






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,
)


__UpperCamelCase      						=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
__UpperCamelCase      						=    'cuda' if torch.cuda.is_available() else 'cpu'
def     UpperCamelCase_(			_A				:str			,       _A				:Tuple=1_00			,       _A				:str=" "					)->						List[str]:
			UpperCamelCase__   =    text.split(_A					)
			return [character.join(text[i : i + n]					).strip() for i in range(0			,       len(_A					)			,       _A					)]
def     UpperCamelCase_(			_A				:dict					)->						dict:
			UpperCamelCase__, UpperCamelCase__   =    [], []
			for title, text in zip(documents["title"]			,       documents["text"]					):
						if text is not None:
									for passage in split_text(_A					):
												titles.append(title if title is not None else ""					)
												texts.append(_A					)
			return {"title": titles, "text": texts}
def     UpperCamelCase_(			_A				:dict			,       _A				:DPRContextEncoder			,       _A				:DPRContextEncoderTokenizerFast					)->						dict:
			UpperCamelCase__   =    ctx_tokenizer(
			    documents["title"]			,       documents["text"]			,       truncation=_A			,       padding="longest"			,       return_tensors="pt"					)["input_ids"]
			UpperCamelCase__   =    ctx_encoder(input_ids.to(device=_A					)			,       return_dict=_A					).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}
def     UpperCamelCase_(			_A				:"RagExampleArguments"			,       _A				:"ProcessingArguments"			,       _A				:"IndexHnswArguments"			,       )->						List[str]:
			######################################
			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__   =    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__   =    dataset.map(_A			,       batched=_A			,       num_proc=processing_args.num_proc					)

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

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

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

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

			# Easy way to load the model
			UpperCamelCase__   =    RagRetriever.from_pretrained(
			    rag_example_args.rag_model_name			,       index_name="custom"			,       indexed_dataset=_A					)
			UpperCamelCase__   =    RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,       retriever=_A					)
			UpperCamelCase__   =    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"					)
			######################################

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







@dataclass
class     lowerCamelCase__		:






			"""simple docstring"""






			_UpperCamelCase						:   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\''}   ,		)
			_UpperCamelCase						:   Optional[str]					=						field(
			    default=UpperCAmelCase   ,		metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}   ,		)
			_UpperCamelCase						:   str					=						field(
			    default='facebook/rag-sequence-nq'   ,		metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}   ,		)
			_UpperCamelCase						:   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\''
			        )
			    }   ,		)
			_UpperCamelCase						:   Optional[str]					=						field(
			    default=UpperCAmelCase   ,		metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}   ,		)







@dataclass
class     lowerCamelCase__		:






			"""simple docstring"""






			_UpperCamelCase						:   Optional[int]					=						field(
			    default=UpperCAmelCase   ,		metadata={
			        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
			    }   ,		)
			_UpperCamelCase						:   int					=						field(
			    default=16   ,		metadata={
			        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
			    }   ,		)







@dataclass
class     lowerCamelCase__		:






			"""simple docstring"""






			_UpperCamelCase						:   int					=						field(
			    default=768   ,		metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}   ,		)
			_UpperCamelCase						:   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)

						__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)


