



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  UpperCAmelCase__    (    lowercase__						,       lowercase__=100						,       lowercase__=" "   ) ->      List[str]:
						__lowercase					=				text.split(lowercase__   )
						return [character.join(text[i : i + n]   ).strip() for i in range(0						,       len(lowercase__   )						,       lowercase__   )]






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






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       ) ->      List[str]:
						######################################
						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
						__lowercase					=				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
						__lowercase					=				dataset.map(lowercase__						,       batched=lowercase__						,       num_proc=processing_args.num_proc   )

						# And compute the embeddings
						__lowercase					=				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=lowercase__   )
						__lowercase					=				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
						__lowercase					=				Features(
						    {"""text""": Value("""string"""   ), """title""": Value("""string"""   ), """embeddings""": Sequence(Value("""float32"""   )   )}   )  # optional, save as float32 instead of float64 to save space
						__lowercase					=				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
						__lowercase					=				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
						__lowercase					=				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
						__lowercase					=				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 			_lowerCAmelCase   :






			"""simple docstring"""





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





@dataclass
class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  Optional[int]      =			field(
			    default=_UpperCAmelCase    ,					metadata={
			        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
			    }    ,					)
			lowercase__      :  int      =			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"""





			lowercase__      :  int      =			field(
			    default=768    ,					metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}    ,					)
			lowercase__      :  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)










import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					_UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  int      =			IFImgaImgSuperResolutionPipeline
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}			)
			lowercase__      :  Tuple      =			PipelineTesterMixin.required_optional_params - {"""latents"""}





			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									return self._get_superresolution_dummy_components()





			def 		snake_case__			(				self	:       List[str]			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[Any]=0   )						->					Union[str, Any]:
									"""simple docstring"""







									if str(lowercase   ).startswith("""mps"""   ):
															__lowercase					=				torch.manual_seed(lowercase   )
									else:
															__lowercase					=				torch.Generator(device=lowercase   ).manual_seed(lowercase   )

									__lowercase					=				floats_tensor((1, 3, 32, 32)			,     rng=random.Random(lowercase   )   ).to(lowercase   )
									__lowercase					=				floats_tensor((1, 3, 16, 16)			,     rng=random.Random(lowercase   )   ).to(lowercase   )

									__lowercase					=				{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """original_image""": original_image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}

									return inputs





			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()			,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""			,     )
			def 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )





			def 		snake_case__			(				self	:       List[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







									self._test_save_load_optional_components()





			@unittest.skipIf(torch_device != """cuda"""			,     reason="""float16 requires CUDA"""   )
			def 		snake_case__			(				self	:       List[Any]   )						->					Dict:
									"""simple docstring"""







									super().test_save_load_floataa(expected_max_diff=1E-1   )





			def 		snake_case__			(				self	:       Dict   )						->					int:
									"""simple docstring"""







									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					int:
									"""simple docstring"""







									self._test_save_load_local()







			def 		snake_case__			(				self	:       Optional[int]   )						->					Optional[int]:
									"""simple docstring"""







									self._test_inference_batch_single_identical(
									    expected_max_diff=1E-2			,     )





