




"""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


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







def   _snake_case    (       lowercase__							,					lowercase__=100							,					lowercase__=" "	):
				_lowerCamelCase  :    int					=					text.split(lowercase__	)
				return [character.join(text[i : i + n]	).strip() for i in range(0							,					len(lowercase__	)							,					lowercase__	)]







def   _snake_case    (       lowercase__	):
				_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__							,					lowercase__							,					lowercase__	):
				_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__							,					lowercase__							,					lowercase__							,					):
				######################################
				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  :    int					=					dataset.map(lowercase__							,					batched=lowercase__							,					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=lowercase__	)
				_lowerCamelCase  :    Tuple					=					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  :    Optional[int]					=					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  :    str					=					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  :    Dict					=					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  :    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 lowerCAmelCase__			:






			'''simple docstring'''



			lowerCamelCase__   =    field(
			    default=str(Path(lowercase		).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""		),			metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""},			)
			lowerCamelCase__   =    field(
			    default=lowercase,			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=str(Path(lowercase		).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'''



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

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





"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available






@dataclass
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    42


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
			from .pipeline_text_to_video_synth import TextToVideoSDPipeline
			from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
			from .pipeline_text_to_video_zero import TextToVideoZeroPipeline