
"""simple docstring"""


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



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase=100					,				__UpperCamelCase=" " )       ->				List[str]:
	_lowerCAmelCase			=text.split(snake_case__ )
	return [character.join(text[i : i + n] ).strip() for i in range(0					,				len(snake_case__ )					,				snake_case__ )]



def 					_lowerCamelCase(__UpperCamelCase )       ->				dict:
	_lowerCAmelCase			=[], []
	for title, text in zip(documents["""title"""]					,				documents["""text"""] ):
		if text is not None:
			for passage in split_text(snake_case__ ):
				titles.append(title if title is not None else """""" )
				texts.append(snake_case__ )
	return {"title": titles, "text": texts}



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				dict:
	_lowerCAmelCase			=ctx_tokenizer(
	    documents["""title"""]					,				documents["""text"""]					,				truncation=snake_case__					,				padding="""longest"""					,				return_tensors="""pt""" )['input_ids']
	_lowerCAmelCase			=ctx_encoder(input_ids.to(device=snake_case__ )					,				return_dict=snake_case__ ).pooler_output
	return {"embeddings": embeddings.detach().cpu().numpy()}



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				)       ->				List[Any]:
	######################################
	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(snake_case__					,				batched=snake_case__					,				num_proc=processing_args.num_proc )

	# And compute the embeddings
	_lowerCAmelCase			=DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=snake_case__ )
	_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(snake_case__					,				ctx_encoder=snake_case__					,				ctx_tokenizer=snake_case__ )					,				batched=snake_case__					,				batch_size=processing_args.batch_size					,				features=snake_case__					,				)

	# And finally save your dataset
	_lowerCAmelCase			=os.path.join(rag_example_args.output_dir					,				"""my_knowledge_dataset""" )
	dataset.save_to_disk(snake_case__ )
	# 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=snake_case__ )

	# And save the index
	_lowerCAmelCase			=os.path.join(rag_example_args.output_dir					,				"""my_knowledge_dataset_hnsw_index.faiss""" )
	dataset.get_index("""embeddings""" ).save(snake_case__ )
	# 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=snake_case__ )
	_lowerCAmelCase			=RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name					,				retriever=snake_case__ )
	_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(snake_case__					,				return_tensors="""pt""" )['input_ids']
	_lowerCAmelCase			=model.generate(snake_case__ )
	_lowerCAmelCase			=tokenizer.batch_decode(snake_case__					,				skip_special_tokens=snake_case__ )[0]
	logger.info("""Q: """ + question )
	logger.info("""A: """ + generated_string )







@dataclass
class 	lowerCamelCase__      :





			'''simple docstring'''
			lowerCamelCase						      =	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						      =	field(
			    default=a__				,			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=a__				,			metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}				,			)







@dataclass
class 	lowerCamelCase__      :





			'''simple docstring'''
			lowerCamelCase						      =	field(
			    default=a__				,			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 	lowerCamelCase__      :





			'''simple docstring'''
			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)

			__A 		= HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
			__A		,      __A		,      __A 		= parser.parse_args_into_dataclasses()
			with TemporaryDirectory() as tmp_dir:
						__A 		= rag_example_args.output_dir or tmp_dir
						main(rag_example_args, processing_args, index_hnsw_args)

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


__A 		= logging.get_logger(__name__)

__A 		= {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''ernie'''



			def __init__(							self  ,  __UpperCAmelCase=3_05_22  ,  __UpperCAmelCase=7_68  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=30_72  ,  __UpperCAmelCase="gelu"  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=5_12  ,  __UpperCAmelCase=2  ,  __UpperCAmelCase=3  ,  __UpperCAmelCase=False  ,  __UpperCAmelCase=0.0_2  ,  __UpperCAmelCase=1e-12  ,  __UpperCAmelCase=0  ,  __UpperCAmelCase="absolute"  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=None  ,  **__UpperCAmelCase  ,  )				->  str:
				super().__init__(pad_token_id=__UpperCAmelCase  ,  **__UpperCAmelCase						)

				_lowerCAmelCase			=vocab_size
				_lowerCAmelCase			=hidden_size
				_lowerCAmelCase			=num_hidden_layers
				_lowerCAmelCase			=num_attention_heads
				_lowerCAmelCase			=hidden_act
				_lowerCAmelCase			=intermediate_size
				_lowerCAmelCase			=hidden_dropout_prob
				_lowerCAmelCase			=attention_probs_dropout_prob
				_lowerCAmelCase			=max_position_embeddings
				_lowerCAmelCase			=type_vocab_size
				_lowerCAmelCase			=task_type_vocab_size
				_lowerCAmelCase			=use_task_id
				_lowerCAmelCase			=initializer_range
				_lowerCAmelCase			=layer_norm_eps
				_lowerCAmelCase			=position_embedding_type
				_lowerCAmelCase			=use_cache
				_lowerCAmelCase			=classifier_dropout







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@property
			def       _lowerCAmelCase	(							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),
				        ("""task_type_ids""", dynamic_axis),
				    ]						)



