





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,
)


__lowerCAmelCase			:Any	 =     logging.getLogger(__name__)
torch.set_grad_enabled(False)
__lowerCAmelCase			:Optional[Any]	 =     'cuda' if torch.cuda.is_available() else 'cpu'







def  A					(				UpperCAmelCase	,	UpperCAmelCase=100	,	UpperCAmelCase=" "					):
					_snake_case							:       Optional[Any]												= text.split(UpperCAmelCase					)
					return [character.join(text[i : i + n]					).strip() for i in range(0	,	len(UpperCAmelCase					)	,	UpperCAmelCase					)]







def  A					(				UpperCAmelCase					):
					_snake_case							,			_snake_case							:       str												= [], []
					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  A					(				UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase					):
					_snake_case							:       int												= ctx_tokenizer(
					    documents["title"]	,	documents["text"]	,	truncation=UpperCAmelCase	,	padding="longest"	,	return_tensors="pt"					)["input_ids"]
					_snake_case							:       Optional[Any]												= ctx_encoder(input_ids.to(device=UpperCAmelCase					)	,	return_dict=UpperCAmelCase					).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}







def  A					(				UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	,	):
					######################################
					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							:       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							:       Optional[Any]												= dataset.map(UpperCAmelCase	,	batched=UpperCAmelCase	,	num_proc=processing_args.num_proc					)

					# And compute the embeddings
					_snake_case							:       List[str]												= DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase					)
					_snake_case							:       str												= DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
					_snake_case							:       List[Any]												= Features(
					    {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
					_snake_case							:       Union[str, Any]												= 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							:       str												= 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							:       Any												= 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							:       List[Any]												= 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

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

					# Easy way to load the model
					_snake_case							:       Optional[Any]												= RagRetriever.from_pretrained(
					    rag_example_args.rag_model_name	,	index_name="custom"	,	indexed_dataset=UpperCAmelCase					)
					_snake_case							:       str												= RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name	,	retriever=UpperCAmelCase					)
					_snake_case							:       Tuple												= 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							:       List[Any]												= rag_example_args.question or "What does Moses' rod turn into ?"
					_snake_case							:       Optional[Any]												= tokenizer.question_encoder(UpperCAmelCase	,	return_tensors="pt"					)["input_ids"]
					_snake_case							:       str												= model.generate(UpperCAmelCase					)
					_snake_case							:       List[str]												= tokenizer.batch_decode(UpperCAmelCase	,	skip_special_tokens=UpperCAmelCase					)[0]
					logger.info("Q: " + question					)
					logger.info("A: " + generated_string					)






@dataclass
class 							_a:
		lowerCamelCase__  :str 					=		field(
		    default=str(Path(__A     ).parent / 'test_data' / '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=__A						, metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}						, )






@dataclass
class 							_a:
		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=16						, metadata={
		        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
		    }						, )






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

							__lowerCAmelCase			:Optional[Any]	 =     HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase			:int	 =     parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														__lowerCAmelCase			:str	 =     rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)






import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
class 							_a(     __A     ):
		lowerCamelCase__  :torch.FloatTensor
		lowerCamelCase__  :torch.FloatTensor







def  A					(				UpperCAmelCase	,	UpperCAmelCase=0.9_9_9	,	UpperCAmelCase="cosine"	,	):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(UpperCAmelCase					):
															return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(UpperCAmelCase					):
															return math.exp(t * -1_2.0					)

					else:
										raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

					_snake_case							:       Optional[int]												= []
					for i in range(UpperCAmelCase					):
										_snake_case							:       Optional[int]												= i / num_diffusion_timesteps
										_snake_case							:       int												= (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(UpperCAmelCase					) / alpha_bar_fn(UpperCAmelCase					)	,	UpperCAmelCase					)					)
					return torch.tensor(UpperCAmelCase	,	dtype=torch.floataa					)






class 							_a(     __A						, __A     ):
		lowerCamelCase__  :Any 					=		1







		@register_to_config
		def __init__(     self			,    __snake_case = 1_0_0_0			,    __snake_case = 0.00_01			,    __snake_case = 0.02			,    __snake_case = "linear"			,    __snake_case = 0.0			,    __snake_case = None			,    __snake_case = True			,    )   ->   Tuple:


							'''simple docstring'''

							if trained_betas is not None:
												_snake_case							:       Any												= torch.from_numpy(__snake_case					)
							elif beta_schedule == "linear":
												_snake_case							:       List[Any]												= torch.linspace(__snake_case			,    __snake_case			,    __snake_case			,    dtype=torch.floataa					)
							elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												_snake_case							:       Tuple												= (
												    torch.linspace(beta_start**0.5			,    beta_end**0.5			,    __snake_case			,    dtype=torch.floataa					) ** 2
												)
							elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												_snake_case							:       Optional[int]												= betas_for_alpha_bar(__snake_case					)
							elif beta_schedule == "sigmoid":
												# GeoDiff sigmoid schedule
												_snake_case							:       Union[str, Any]												= torch.linspace(-6			,    6			,    __snake_case					)
												_snake_case							:       Optional[Any]												= torch.sigmoid(__snake_case					) * (beta_end - beta_start) + beta_start
							else:
												raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""					)

							_snake_case							:       Optional[int]												= 1.0 - self.betas
							_snake_case							:       Tuple												= torch.cumprod(self.alphas			,    dim=0					)
							_snake_case							:       List[str]												= torch.tensor(1.0					)

							_snake_case							:       Any												= torch.tensor(1.0					)

							# standard deviation of the initial noise distribution
							_snake_case							:       int												= 1.0

							# setable values
							_snake_case							:       Optional[Any]												= None
							_snake_case							:       str												= torch.from_numpy(np.arange(0			,    __snake_case					)[::-1].copy()					)

							_snake_case							:       Any												= eta







		def 							lowercase							(     self			,    __snake_case			,    __snake_case = None					)   ->   torch.FloatTensor:


							'''simple docstring'''

							return sample







		def 							lowercase							(     self			,    __snake_case			,    __snake_case = 1_0			,    __snake_case = 1_0			,    __snake_case = None			,    )   ->   List[str]:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= min(self.config.num_train_timesteps			,    __snake_case					)
							_snake_case							:       Tuple												= num_inference_steps

							_snake_case							:       List[str]												= []

							_snake_case							:       str												= {}
							for j in range(0			,    num_inference_steps - jump_length			,    __snake_case					):
												_snake_case							:       Tuple												= jump_n_sample - 1

							_snake_case							:       int												= num_inference_steps
							while t >= 1:
												_snake_case							:       Union[str, Any]												= t - 1
												timesteps.append(__snake_case					)

												if jumps.get(__snake_case			,    0					) > 0:
																	_snake_case							:       Any												= jumps[t] - 1
																	for _ in range(__snake_case					):
																						_snake_case							:       List[str]												= t + 1
																						timesteps.append(__snake_case					)

							_snake_case							:       Optional[Any]												= np.array(__snake_case					) * (self.config.num_train_timesteps // self.num_inference_steps)
							_snake_case							:       int												= torch.from_numpy(__snake_case					).to(__snake_case					)







		def 							lowercase							(     self			,    __snake_case					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= t - self.config.num_train_timesteps // self.num_inference_steps

							_snake_case							:       Optional[int]												= self.alphas_cumprod[t]
							_snake_case							:       Union[str, Any]												= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case							:       Optional[Any]												= 1 - alpha_prod_t
							_snake_case							:       str												= 1 - alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from
							# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
							# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
							# variance to pred_sample
							# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
							# without eta.
							# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
							_snake_case							:       Dict												= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case = None			,    __snake_case = True			,    )   ->   Union[RePaintSchedulerOutput, Tuple]:


							'''simple docstring'''

							_snake_case							:       List[Any]												= timestep
							_snake_case							:       Optional[int]												= timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							_snake_case							:       Optional[Any]												= self.alphas_cumprod[t]
							_snake_case							:       Any												= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case							:       Dict												= 1 - alpha_prod_t

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							_snake_case							:       List[str]												= (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
												_snake_case							:       str												= torch.clamp(__snake_case			,    -1			,    1					)

							# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
							# substitute formula (7) in the algorithm coming from DDPM paper
							# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
							# DDIM schedule gives the same results as DDPM with eta = 1.0
							# Noise is being reused in 7. and 8., but no impact on quality has
							# been observed.

							# 5. Add noise
							_snake_case							:       Tuple												= model_output.device
							_snake_case							:       Any												= randn_tensor(model_output.shape			,    generator=__snake_case			,    device=__snake_case			,    dtype=model_output.dtype					)
							_snake_case							:       Tuple												= self.eta * self._get_variance(__snake_case					) ** 0.5

							_snake_case							:       str												= 0
							if t > 0 and self.eta > 0:
												_snake_case							:       List[str]												= std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case							:       List[str]												= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case							:       int												= alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

							# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case							:       Tuple												= (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

							# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case							:       int												= mask * prev_known_part + (1.0 - mask) * prev_unknown_part

							if not return_dict:
												return (
												    pred_prev_sample,
												    pred_original_sample,
												)

							return RePaintSchedulerOutput(prev_sample=__snake_case			,    pred_original_sample=__snake_case					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case=None					)   ->   Optional[Any]:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= self.config.num_train_timesteps // self.num_inference_steps

							for i in range(__snake_case					):
												_snake_case							:       List[Any]												= self.betas[timestep + i]
												if sample.device.type == "mps":
																	# randn does not work reproducibly on mps
																	_snake_case							:       List[Any]												= randn_tensor(sample.shape			,    dtype=sample.dtype			,    generator=__snake_case					)
																	_snake_case							:       int												= noise.to(sample.device					)
												else:
																	_snake_case							:       Any												= randn_tensor(sample.shape			,    generator=__snake_case			,    device=sample.device			,    dtype=sample.dtype					)

												# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
												_snake_case							:       List[Any]												= (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case			,    )   ->   torch.FloatTensor:


							'''simple docstring'''

							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."					)







		def __len__(     self					)   ->   Optional[Any]:


							'''simple docstring'''

							return self.config.num_train_timesteps