




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




def      __magic_name__							(					A						:   str,    A						:   Union[str, Any]=100,    A						:   List[Any]=" "  ):






			'''simple docstring'''







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




def      __magic_name__							(					A						:   dict  ):






			'''simple docstring'''







			a       ,						a       =     [], []
			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      __magic_name__							(					A						:   dict,    A						:   DPRContextEncoder,    A						:   DPRContextEncoderTokenizerFast  ):






			'''simple docstring'''







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




def      __magic_name__							(					A						:   "RagExampleArguments",    A						:   "ProcessingArguments",    A						:   "IndexHnswArguments",    ):






			'''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
			a       =     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
			a       =     dataset.map(A,    batched=A,    num_proc=processing_args.num_proc  )

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

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




@dataclass
class        snake_case__				:
				"""simple docstring"""



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




@dataclass
class        snake_case__				:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   field(
				    default=_UpperCamelCase       ,			metadata={
				        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
				    }       ,			)
				SCREAMING_SNAKE_CASE_			:				int									=   field(
				    default=16       ,			metadata={
				        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
				    }       ,			)




@dataclass
class        snake_case__				:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				int									=   field(
				    default=7_68       ,			metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}       ,			)
				SCREAMING_SNAKE_CASE_			:				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)

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












import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


__lowerCAmelCase						:							Optional[int]			     =						'\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

__lowerCAmelCase						:							str			     =						'\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

__lowerCAmelCase						:							Optional[Any]			     =						'\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - \'id\': id of the question-answer pair (see above),\n            - \'answers\': a list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,			_KWARGS_DESCRIPTION  )
class        snake_case__				(datasets.Metric  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Union[str, Any]:
							return datasets.MetricInfo(
							    description=_DESCRIPTION   , citation=_CITATION   , inputs_description=_KWARGS_DESCRIPTION   , features=datasets.Features(
							        {
							            "predictions": {
							                "id": datasets.Value("string"				),
							                "prediction_text": datasets.Value("string"				),
							                "no_answer_probability": datasets.Value("float32"				),
							            },
							            "references": {
							                "id": datasets.Value("string"				),
							                "answers": datasets.features.Sequence(
							                    {"text": datasets.Value("string"				), "answer_start": datasets.Value("int32"				)}				),
							            },
							        }				)   , codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]   , reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]   , )





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Dict=1.0				)   ->	List[str]:
							a       =     {p["id"]: p["no_answer_probability"] for p in predictions}
							a       =     [{"paragraphs": [{"qas": references}]}]
							a       =     {p["id"]: p["prediction_text"] for p in predictions}

							a       =     make_qid_to_has_ans(__lowerCamelCase				)  # maps qid to True/False
							a       =     [k for k, v in qid_to_has_ans.items() if v]
							a       =     [k for k, v in qid_to_has_ans.items() if not v]

							a       ,						a       =     get_raw_scores(__lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase				)

							if has_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "HasAns"				)
							if no_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "NoAns"				)
							find_all_best_thresh(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							return dict(__lowerCamelCase				)






