

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,
)


__A	:   int	=					logging.getLogger(__name__)
torch.set_grad_enabled(False)
__A	:   Dict	=					'cuda' if torch.cuda.is_available() else 'cpu'




def        __UpperCamelCase     (      _A					:							str      ,  _A					:							str=100      ,  _A					:							Union[str, Any]=" "		)				->List[str]:



							"""simple docstring"""
							lowerCamelCase_								=text.split(_A		)
							return [character.join(text[i : i + n]		).strip() for i in range(0      ,  len(_A		)      ,  _A		)]




def        __UpperCamelCase     (      _A					:							dict		)				->dict:



							"""simple docstring"""
							lowerCamelCase_	,   lowerCamelCase_								=[], []
							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:



							"""simple docstring"""
							lowerCamelCase_								=ctx_tokenizer(
							    documents["""title"""]      ,  documents["""text"""]      ,  truncation=_A      ,  padding="""longest"""      ,  return_tensors="""pt"""		)["""input_ids"""]
							lowerCamelCase_								=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"      ,  )				->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
							lowerCamelCase_								=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
							lowerCamelCase_								=dataset.map(_A      ,  batched=_A      ,  num_proc=processing_args.num_proc		)

							# And compute the embeddings
							lowerCamelCase_								=DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name		).to(device=_A		)
							lowerCamelCase_								=DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name		)
							lowerCamelCase_								=Features(
							    {"""text""": Value("""string"""		), """title""": Value("""string"""		), """embeddings""": Sequence(Value("""float32"""		)		)}		)  # optional, save as float32 instead of float64 to save space
							lowerCamelCase_								=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
							lowerCamelCase_								=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
							lowerCamelCase_								=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
							lowerCamelCase_								=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
							lowerCamelCase_								=RagRetriever.from_pretrained(
							    rag_example_args.rag_model_name      ,  index_name="""custom"""      ,  indexed_dataset=_A		)
							lowerCamelCase_								=RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name      ,  retriever=_A		)
							lowerCamelCase_								=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"""		)
							######################################

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


@dataclass
class 			_SCREAMING_SNAKE_CASE      :
						_UpperCamelCase:str	=		field(
						    default=str(Path(lowerCAmelCase__).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=lowerCAmelCase__ ,    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=lowerCAmelCase__ ,    metadata={"help": "Path to a directory where the dataset passages and the index will be saved"} ,    )


@dataclass
class 			_SCREAMING_SNAKE_CASE      :
						_UpperCamelCase:Optional[int]	=		field(
						    default=lowerCAmelCase__ ,    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 			_SCREAMING_SNAKE_CASE      :
						_UpperCamelCase:int	=		field(
						    default=7_68 ,    metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."} ,    )
						_UpperCamelCase:int	=		field(
						    default=1_28 ,    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)

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



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A	:   List[Any]	=					logging.get_logger(__name__)

__A	:   Tuple	=					{
    'google/electra-small-generator': 'https://huggingface.co/google/electra-small-generator/resolve/main/config.json',
    'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/config.json',
    'google/electra-large-generator': 'https://huggingface.co/google/electra-large-generator/resolve/main/config.json',
    'google/electra-small-discriminator': (
        'https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'
    ),
    'google/electra-base-discriminator': (
        'https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'
    ),
    'google/electra-large-discriminator': (
        'https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'
    ),
}


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Tuple	=		"electra"







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE=3_0522     ,						_SCREAMING_SNAKE_CASE=128     ,						_SCREAMING_SNAKE_CASE=256     ,						_SCREAMING_SNAKE_CASE=12     ,						_SCREAMING_SNAKE_CASE=4     ,						_SCREAMING_SNAKE_CASE=1024     ,						_SCREAMING_SNAKE_CASE="gelu"     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=512     ,						_SCREAMING_SNAKE_CASE=2     ,						_SCREAMING_SNAKE_CASE=0.0_2     ,						_SCREAMING_SNAKE_CASE=1E-12     ,						_SCREAMING_SNAKE_CASE="first"     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE="gelu"     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0     ,						_SCREAMING_SNAKE_CASE="absolute"     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=None     ,						**_SCREAMING_SNAKE_CASE     ,						)->							Optional[int]:
													super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=vocab_size
													lowerCamelCase_								=embedding_size
													lowerCamelCase_								=hidden_size
													lowerCamelCase_								=num_hidden_layers
													lowerCamelCase_								=num_attention_heads
													lowerCamelCase_								=intermediate_size
													lowerCamelCase_								=hidden_act
													lowerCamelCase_								=hidden_dropout_prob
													lowerCamelCase_								=attention_probs_dropout_prob
													lowerCamelCase_								=max_position_embeddings
													lowerCamelCase_								=type_vocab_size
													lowerCamelCase_								=initializer_range
													lowerCamelCase_								=layer_norm_eps

													lowerCamelCase_								=summary_type
													lowerCamelCase_								=summary_use_proj
													lowerCamelCase_								=summary_activation
													lowerCamelCase_								=summary_last_dropout
													lowerCamelCase_								=position_embedding_type
													lowerCamelCase_								=use_cache
													lowerCamelCase_								=classifier_dropout


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):







						@property
						def        _snake_case				(			self							)->							Mapping[str, Mapping[int, str]]:
													if self.task == "multiple-choice":
																				lowerCamelCase_								={0: """batch""", 1: """choice""", 2: """sequence"""}
													else:
																				lowerCamelCase_								={0: """batch""", 1: """sequence"""}
													return OrderedDict(
													    [
													        ("""input_ids""", dynamic_axis),
													        ("""attention_mask""", dynamic_axis),
													        ("""token_type_ids""", dynamic_axis),
													    ]							)
