
"""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


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






def 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  int=1_0_0      ,     lowercase__		:  int=" "		)  ->				List[str]:


			'''simple docstring'''





			lowerCAmelCase_    :Tuple			     =  text.split(lowercase__		)
			return [character.join(text[i : i + n]		).strip() for i in range(0      ,     len(lowercase__		)      ,     lowercase__		)]






def 						_snake_case       (	lowercase__		:  dict		)  ->				dict:


			'''simple docstring'''





			lowerCAmelCase_     ,       lowerCAmelCase_    :List[Any]			     =  [], []
			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__		:  dict      ,     lowercase__		:  DPRContextEncoder      ,     lowercase__		:  DPRContextEncoderTokenizerFast		)  ->				dict:


			'''simple docstring'''





			lowerCAmelCase_    :Tuple			     =  ctx_tokenizer(
			    documents["""title"""]      ,     documents["""text"""]      ,     truncation=lowercase__      ,     padding="""longest"""      ,     return_tensors="""pt"""		)["""input_ids"""]
			lowerCAmelCase_    :int			     =  ctx_encoder(input_ids.to(device=lowercase__		)      ,     return_dict=lowercase__		).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}






def 						_snake_case       (	lowercase__		:  "RagExampleArguments"      ,     lowercase__		:  "ProcessingArguments"      ,     lowercase__		:  "IndexHnswArguments"      ,     )  ->				Optional[Any]:


			'''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_    :Tuple			     =  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_    :str			     =  dataset.map(lowercase__      ,     batched=lowercase__      ,     num_proc=processing_args.num_proc		)

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




@dataclass
class 			_SCREAMING_SNAKE_CASE					:
			UpperCAmelCase_  :str				 =						field(
			    default=str(Path(A__							).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"							)	, metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}	, )
			UpperCAmelCase_  :Optional[str]				 =						field(
			    default=A__	, 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=str(Path(A__							).parent / "test_run" / "dummy-kb"							)	, 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=A__	, 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=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)







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      """facebook/bart-large-mnli"""
    a__	:						int						=      (
        """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
        """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
        """It returns the most likely label in the list of provided `labels` for the input text."""
    )
    a__	:						Optional[Any]						=      """text_classifier"""
    a__	:						Any						=      AutoTokenizer
    a__	:						str						=      AutoModelForSequenceClassification

    a__	:						str						=      ["""text""", ["""text"""]]
    a__	:						Optional[int]						=      ["""text"""]







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        super().setup()
        __UpperCamelCase     :int			  =		self.model.config
        __UpperCamelCase     :Optional[Any]			  =		-1
        for idx, label in config.idalabel.items():
            if label.lower().startswith('''entail'''):
                __UpperCamelCase     :List[Any]			  =		int(__lowercase)
        if self.entailment_id == -1:
            raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''')







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		labels
        return self.pre_processor(
            [text] * len(__lowercase) , [f"""This example is {label}""" for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :List[Any]			  =		outputs.logits
        __UpperCamelCase     :Any			  =		torch.argmax(logits[:, 2]).item()
        return self._labels[label_id]





