
"""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,
)


lowerCAmelCase			=		logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCAmelCase			=		"cuda" if torch.cuda.is_available() else "cpu"






def      lowerCAmelCase_     (	snake_case_	:    List[Any]							,					snake_case_	:    Any=1_0_0							,					snake_case_	:    Any=" "    )     ->List[str]:
			lowerCamelCase__			:  str  =text.split(snake_case_    )
			return [character.join(text[i : i + n]    ).strip() for i in range(0							,					len(snake_case_    )							,					snake_case_    )]






def      lowerCAmelCase_     (	snake_case_	:    Dict    )     ->dict:
			lowerCamelCase__			:  str  =[], []
			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_     (	snake_case_	:    List[Any]							,					snake_case_	:    int							,					snake_case_	:    Tuple    )     ->dict:
			lowerCamelCase__			:  Dict  =ctx_tokenizer(
			    documents['title']							,					documents['text']							,					truncation=snake_case_							,					padding='longest'							,					return_tensors='pt'    )['''input_ids''']
			lowerCamelCase__			:  str  =ctx_encoder(input_ids.to(device=snake_case_    )							,					return_dict=snake_case_    ).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}






def      lowerCAmelCase_     (	snake_case_	:    Union[str, Any]							,					snake_case_	:    str							,					snake_case_	:    Optional[Any]							,					)     ->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
			lowerCamelCase__			:  Optional[Any]  =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__			:  int  =dataset.map(snake_case_							,					batched=snake_case_							,					num_proc=processing_args.num_proc    )

			# And compute the embeddings
			lowerCamelCase__			:  List[Any]  =DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    ).to(device=snake_case_    )
			lowerCamelCase__			:  str  =DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    )
			lowerCamelCase__			:  Optional[Any]  =Features(
			    {'text': Value('string'    ), 'title': Value('string'    ), 'embeddings': Sequence(Value('float32'    )    )}    )  # optional, save as float32 instead of float64 to save space
			lowerCamelCase__			:  Any  =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__			:  List[str]  =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__			:  Union[str, Any]  =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__			:  List[str]  =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__			:  Union[str, Any]  =RagRetriever.from_pretrained(
			    rag_example_args.rag_model_name							,					index_name='custom'							,					indexed_dataset=snake_case_    )
			lowerCamelCase__			:  Union[str, Any]  =RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name							,					retriever=snake_case_    )
			lowerCamelCase__			:  Any  =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__			:  Union[str, Any]  =rag_example_args.question or '''What does Moses\' rod turn into ?'''
			lowerCamelCase__			:  Optional[int]  =tokenizer.question_encoder(snake_case_							,					return_tensors='pt'    )['''input_ids''']
			lowerCamelCase__			:  Optional[Any]  =model.generate(snake_case_    )
			lowerCamelCase__			:  Tuple  =tokenizer.batch_decode(snake_case_							,					skip_special_tokens=snake_case_    )[0]
			logger.info('Q: ' + question    )
			logger.info('A: ' + generated_string    )

@dataclass
class 					A_			:



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     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_			   =     field(
							    default=UpperCamelCase_   ,      metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}   ,      )
							SCREAMING_SNAKE_CASE_			   =     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_			   =     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_			   =     field(
							    default=UpperCamelCase_   ,      metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}   ,      )






@dataclass
class 					A_			:



							"""simple docstring"""






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






@dataclass
class 					A_			:



							"""simple docstring"""






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

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

"""simple docstring"""


from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowerCAmelCase			=		logging.get_logger(__name__)  # pylint: disable=invalid-name

class 					A_			(  A__     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     ["""safety_checker""", """feature_extractor"""]





							def __init__(						self						:Optional[Any]  ,							lowerCamelCase_						:AutoencoderKL  ,							lowerCamelCase_						:CLIPTextModel  ,							lowerCamelCase_						:CLIPTokenizer  ,							lowerCamelCase_						:UNetaDConditionModel  ,							lowerCamelCase_						:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,							lowerCamelCase_						:StableDiffusionSafetyChecker  ,							lowerCamelCase_						:CLIPImageProcessor  ,							lowerCamelCase_						:bool = True  ,							):





										"""simple docstring"""

										super().__init__()
										if hasattr(scheduler.config  ,							'steps_offset' ) and scheduler.config.steps_offset != 1:
													lowerCamelCase__			:  Dict  =(
													    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
													    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
													    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
													    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
													    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
													    ' file'
													)
													deprecate('steps_offset!=1'  ,							'1.0.0'  ,							lowerCamelCase_  ,							standard_warn=lowerCamelCase_ )
													lowerCamelCase__			:  Tuple  =dict(scheduler.config )
													lowerCamelCase__			:  str  =1
													lowerCamelCase__			:  List[Any]  =FrozenDict(lowerCamelCase_ )

										self.register_modules(
										    vae=lowerCamelCase_  ,							text_encoder=lowerCamelCase_  ,							tokenizer=lowerCamelCase_  ,							unet=lowerCamelCase_  ,							scheduler=lowerCamelCase_  ,							safety_checker=lowerCamelCase_  ,							feature_extractor=lowerCamelCase_  ,							)
										self.register_to_config(requires_safety_checker=lowerCamelCase_ )





							@property
							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										return {k: getattr(self  ,							lowerCamelCase_ ) for k in self.config.keys() if not k.startswith('_' )}





							def    UpperCAmelCase__							(						self						:Optional[Any]  ,							lowerCamelCase_						:Optional[Union[str, int]] = "auto" ):





										"""simple docstring"""

										if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													lowerCamelCase__			:  List[Any]  =self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										self.enable_attention_slicing(lowerCamelCase_ )





							@torch.no_grad()
							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:Union[str, List[str]]  ,							lowerCamelCase_						:Union[torch.FloatTensor, PIL.Image.Image]  ,							lowerCamelCase_						:Union[torch.FloatTensor, PIL.Image.Image]  ,							lowerCamelCase_						:float = 0.8  ,							lowerCamelCase_						:Optional[int] = 50  ,							lowerCamelCase_						:Optional[float] = 7.5  ,							lowerCamelCase_						:Optional[Union[str, List[str]]] = None  ,							lowerCamelCase_						:Optional[int] = 1  ,							lowerCamelCase_						:Optional[float] = 0.0  ,							lowerCamelCase_						:Optional[torch.Generator] = None  ,							lowerCamelCase_						:Optional[str] = "pil"  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							lowerCamelCase_						:int = 1  ,							):





										"""simple docstring"""

										return StableDiffusionInpaintPipelineLegacy(**self.components )(
										    prompt=lowerCamelCase_  ,							image=lowerCamelCase_  ,							mask_image=lowerCamelCase_  ,							strength=lowerCamelCase_  ,							num_inference_steps=lowerCamelCase_  ,							guidance_scale=lowerCamelCase_  ,							negative_prompt=lowerCamelCase_  ,							num_images_per_prompt=lowerCamelCase_  ,							eta=lowerCamelCase_  ,							generator=lowerCamelCase_  ,							output_type=lowerCamelCase_  ,							return_dict=lowerCamelCase_  ,							callback=lowerCamelCase_  ,							)





							@torch.no_grad()
							def    UpperCAmelCase__							(						self						:Any  ,							lowerCamelCase_						:Union[str, List[str]]  ,							lowerCamelCase_						:Union[torch.FloatTensor, PIL.Image.Image]  ,							lowerCamelCase_						:float = 0.8  ,							lowerCamelCase_						:Optional[int] = 50  ,							lowerCamelCase_						:Optional[float] = 7.5  ,							lowerCamelCase_						:Optional[Union[str, List[str]]] = None  ,							lowerCamelCase_						:Optional[int] = 1  ,							lowerCamelCase_						:Optional[float] = 0.0  ,							lowerCamelCase_						:Optional[torch.Generator] = None  ,							lowerCamelCase_						:Optional[str] = "pil"  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							lowerCamelCase_						:int = 1  ,							**lowerCamelCase_						:str  ,							):





										"""simple docstring"""

										return StableDiffusionImgaImgPipeline(**self.components )(
										    prompt=lowerCamelCase_  ,							image=lowerCamelCase_  ,							strength=lowerCamelCase_  ,							num_inference_steps=lowerCamelCase_  ,							guidance_scale=lowerCamelCase_  ,							negative_prompt=lowerCamelCase_  ,							num_images_per_prompt=lowerCamelCase_  ,							eta=lowerCamelCase_  ,							generator=lowerCamelCase_  ,							output_type=lowerCamelCase_  ,							return_dict=lowerCamelCase_  ,							callback=lowerCamelCase_  ,							callback_steps=lowerCamelCase_  ,							)





							@torch.no_grad()
							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:Union[str, List[str]]  ,							lowerCamelCase_						:int = 512  ,							lowerCamelCase_						:int = 512  ,							lowerCamelCase_						:int = 50  ,							lowerCamelCase_						:float = 7.5  ,							lowerCamelCase_						:Optional[Union[str, List[str]]] = None  ,							lowerCamelCase_						:Optional[int] = 1  ,							lowerCamelCase_						:float = 0.0  ,							lowerCamelCase_						:Optional[torch.Generator] = None  ,							lowerCamelCase_						:Optional[torch.FloatTensor] = None  ,							lowerCamelCase_						:Optional[str] = "pil"  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							lowerCamelCase_						:int = 1  ,							):





										"""simple docstring"""

										return StableDiffusionPipeline(**self.components )(
										    prompt=lowerCamelCase_  ,							height=lowerCamelCase_  ,							width=lowerCamelCase_  ,							num_inference_steps=lowerCamelCase_  ,							guidance_scale=lowerCamelCase_  ,							negative_prompt=lowerCamelCase_  ,							num_images_per_prompt=lowerCamelCase_  ,							eta=lowerCamelCase_  ,							generator=lowerCamelCase_  ,							latents=lowerCamelCase_  ,							output_type=lowerCamelCase_  ,							return_dict=lowerCamelCase_  ,							callback=lowerCamelCase_  ,							callback_steps=lowerCamelCase_  ,							)