


import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


lowerCAmelCase__       :	Dict     				=logging.get_logger(__name__)







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def __init__(	self    ):





										"""simple docstring"""




										super().__init__()
										if version.parse(torch.__version__    ) < version.parse('1.12.0'    ):
													raise ImportError(
													    F'''You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '''
													    'PytorchGELUTanh. Please upgrade torch.'    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return nn.functional.gelu(lowerCAmelCase__       ,		approximate='tanh'    )







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.044_715 * torch.pow(lowerCAmelCase__       ,		3.0    ))    ))







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__ = False    ):





										"""simple docstring"""




										super().__init__()
										if use_gelu_python:
													SCREAMING_SNAKE_CASE_						:			Dict       						= self._gelu_python
										else:
													SCREAMING_SNAKE_CASE_						:			str       						= nn.functional.gelu






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0    )    ))






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return self.act(lowerCAmelCase__    )







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return 0.5 * input * (1.0 + torch.tanh(input * 0.7_978_845_608 * (1.0 + 0.044_715 * input * input)    ))







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return input * torch.sigmoid(1.702 * input    )







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										if min > max:
													raise ValueError(F'''min should be < max (got min: {min}, max: {max})'''    )

										super().__init__()
										SCREAMING_SNAKE_CASE_						:			str       						= min
										SCREAMING_SNAKE_CASE_						:			int       						= max






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return torch.clip(gelu(lowerCAmelCase__    )       ,		self.min       ,		self.max    )







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def __init__(	self    ):





										"""simple docstring"""




										super().__init__()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= math.sqrt(2 / math.pi    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044_715 * torch.pow(lowerCAmelCase__       ,		3    ))    ))







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return nn.functional.silu(lowerCAmelCase__    )







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def __init__(	self    ):





										"""simple docstring"""




										super().__init__()
										if version.parse(torch.__version__    ) < version.parse('1.9.0'    ):
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= self._mish_python
										else:
													SCREAMING_SNAKE_CASE_						:			str       						= nn.functional.mish






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return input * torch.tanh(nn.functional.softplus(lowerCAmelCase__    )    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return self.act(lowerCAmelCase__    )







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return input







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__=0.707_107       ,		lowerCAmelCase__=0.282_095    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= (input - mu).div(sigma * math.sqrt(2.0    )    )
										return 0.5 * (1.0 + torch.erf(lowerCAmelCase__    ))







class      __lowercase  (nn.Module   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Dict       						= nn.functional.relu(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= torch.square(lowerCAmelCase__    )
										return squared







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def __getitem__(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= super().__getitem__(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Optional[int]       						= content if isinstance(lowerCAmelCase__       ,		lowerCAmelCase__    ) else (content, {})
										return cls(**lowerCAmelCase__    )







lowerCAmelCase__       :	List[str]     				={
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -10, 'max': 10}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
lowerCAmelCase__       :	Any     				=ClassInstantier(ACTaCLS)






def 		a__       (							A__							):
			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()							)}'''							)


# For backwards compatibility with: from activations import gelu_python
lowerCAmelCase__       :	Optional[Any]     				=get_activation('gelu_python')
lowerCAmelCase__       :	List[Any]     				=get_activation('gelu_new')
lowerCAmelCase__       :	str     				=get_activation('gelu')
lowerCAmelCase__       :	Optional[int]     				=get_activation('gelu_fast')
lowerCAmelCase__       :	Tuple     				=get_activation('quick_gelu')
lowerCAmelCase__       :	Optional[int]     				=get_activation('silu')
lowerCAmelCase__       :	int     				=get_activation('mish')



lowerCAmelCase__       :	List[str]     				=get_activation('linear')






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,
)


__SCREAMING_SNAKE_CASE						:    Optional[Any]     = logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE						:    Any     = 'cuda' if torch.cuda.is_available() else 'cpu'
def 					snake_case						(__lowercase					,			__lowercase=100					,			__lowercase=" "						)       ->       List[str]:

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    text.split(__lowercase						)
						return [character.join(text[i : i + n]						).strip() for i in range(0					,			len(__lowercase						)					,			__lowercase						)]
def 					snake_case						(__lowercase						)       ->       dict:

						'''simple docstring'''



						_snake_case				,_snake_case						:     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 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       dict:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    ctx_tokenizer(
						    documents["title"]					,			documents["text"]					,			truncation=__lowercase					,			padding="longest"					,			return_tensors="pt"						)["input_ids"]
						_snake_case						:     Tuple				=    ctx_encoder(input_ids.to(device=__lowercase						)					,			return_dict=__lowercase						).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			)       ->       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
						_snake_case						:     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
						_snake_case						:     Optional[int]				=    dataset.map(__lowercase					,			batched=__lowercase					,			num_proc=processing_args.num_proc						)

						# And compute the embeddings
						_snake_case						:     Optional[int]				=    DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__lowercase						)
						_snake_case						:     int				=    DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
						_snake_case						:     Union[str, 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(__lowercase					,			ctx_encoder=__lowercase					,			ctx_tokenizer=__lowercase						)					,			batched=__lowercase					,			batch_size=processing_args.batch_size					,			features=__lowercase					,			)

						# And finally save your dataset
						_snake_case						:     Dict				=    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
						_snake_case						:     Union[str, 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
						_snake_case						:     Any				=    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

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

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



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




@dataclass
class    lowercase_    :
							_lowerCamelCase						=		field(
							    default=__snake_case						, metadata={
							        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
							    }						, )
							_lowerCamelCase						=		field(
							    default=16						, metadata={
							        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
							    }						, )




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

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