


'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						@property
						def 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		1
													A_			    =		3
													A_			    =		(32, 32)

													A_			    =		floats_tensor((batch_size, num_channels) + sizes ,  rng=random.Random(0	)	).to(lowerCAmelCase	)
													return image






						@property
						def 				_UpperCAmelCase       (  self							:      Dict	):
													torch.manual_seed(0	)
													A_			    =		UNetaDConditionModel(
													    block_out_channels=(32, 64) ,  layers_per_block=2 ,  sample_size=32 ,  in_channels=4 ,  out_channels=4 ,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,  cross_attention_dim=32 ,  )
													return model






						@property
						def 				_UpperCAmelCase       (  self							:      Tuple	):
													torch.manual_seed(0	)
													A_			    =		AutoencoderKL(
													    block_out_channels=[32, 64] ,  in_channels=3 ,  out_channels=3 ,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,  latent_channels=4 ,  )
													return model






						@property
						def 				_UpperCAmelCase       (  self							:      Any	):
													torch.manual_seed(0	)
													A_			    =		RobertaSeriesConfig(
													    hidden_size=32 ,  project_dim=32 ,  intermediate_size=37 ,  layer_norm_eps=1e-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=50_06 ,  )
													return RobertaSeriesModelWithTransformation(lowerCAmelCase	)






						@property
						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													def extract(*lowerCAmelCase							:      Tuple ,  **lowerCAmelCase							:      Optional[Any]	):






																				class 					__lowerCAmelCase   :

																										"""simple docstring"""






																										def __init__(  self							:      Dict	):
																																	A_			    =		torch.ones([0]	)






																										def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Optional[int]	):
																																	self.pixel_values.to(lowerCAmelCase	)
																																	return self



																				return Out()

													return extract






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													A_			    =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													A_			    =		self.dummy_cond_unet
													A_			    =		PNDMScheduler(skip_prk_steps=lowerCAmelCase	)
													A_			    =		self.dummy_vae
													A_			    =		self.dummy_text_encoder
													A_			    =		XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"	)
													A_			    =		77

													A_			    =		self.dummy_image.to(lowerCAmelCase	)
													A_			    =		init_image / 2 + 0.5

													# make sure here that pndm scheduler skips prk
													A_			    =		AltDiffusionImgaImgPipeline(
													    unet=lowerCAmelCase ,  scheduler=lowerCAmelCase ,  vae=lowerCAmelCase ,  text_encoder=lowerCAmelCase ,  tokenizer=lowerCAmelCase ,  safety_checker=lowerCAmelCase ,  feature_extractor=self.dummy_extractor ,  )
													A_			    =		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,  do_normalize=lowerCAmelCase	)
													A_			    =		alt_pipe.to(lowerCAmelCase	)
													alt_pipe.set_progress_bar_config(disable=lowerCAmelCase	)

													A_			    =		"A painting of a squirrel eating a burger"
													A_			    =		torch.Generator(device=lowerCAmelCase	).manual_seed(0	)
													A_			    =		alt_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=6.0 ,  num_inference_steps=2 ,  output_type="np" ,  image=lowerCAmelCase ,  )

													A_			    =		output.images

													A_			    =		torch.Generator(device=lowerCAmelCase	).manual_seed(0	)
													A_			    =		alt_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=6.0 ,  num_inference_steps=2 ,  output_type="np" ,  image=lowerCAmelCase ,  return_dict=lowerCAmelCase ,  )[0]

													A_			    =		image[0, -3:, -3:, -1]
													A_			    =		image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													A_			    =		np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]	)

													assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-3
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 5e-3






						@unittest.skipIf(torch_device != "cuda" ,  "This test requires a GPU"	)
						def 				_UpperCAmelCase       (  self							:      List[str]	):
													A_			    =		self.dummy_cond_unet
													A_			    =		PNDMScheduler(skip_prk_steps=lowerCAmelCase	)
													A_			    =		self.dummy_vae
													A_			    =		self.dummy_text_encoder
													A_			    =		XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"	)
													A_			    =		77

													A_			    =		self.dummy_image.to(lowerCAmelCase	)

													# put models in fp16
													A_			    =		unet.half()
													A_			    =		vae.half()
													A_			    =		bert.half()

													# make sure here that pndm scheduler skips prk
													A_			    =		AltDiffusionImgaImgPipeline(
													    unet=lowerCAmelCase ,  scheduler=lowerCAmelCase ,  vae=lowerCAmelCase ,  text_encoder=lowerCAmelCase ,  tokenizer=lowerCAmelCase ,  safety_checker=lowerCAmelCase ,  feature_extractor=self.dummy_extractor ,  )
													A_			    =		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,  do_normalize=lowerCAmelCase	)
													A_			    =		alt_pipe.to(lowerCAmelCase	)
													alt_pipe.set_progress_bar_config(disable=lowerCAmelCase	)

													A_			    =		"A painting of a squirrel eating a burger"
													A_			    =		torch.manual_seed(0	)
													A_			    =		alt_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  num_inference_steps=2 ,  output_type="np" ,  image=lowerCAmelCase ,  ).images

													assert image.shape == (1, 32, 32, 3)






						@unittest.skipIf(torch_device != "cuda" ,  "This test requires a GPU"	)
						def 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/img2img/sketch-mountains-input.jpg"	)
													# resize to resolution that is divisible by 8 but not 16 or 32
													A_			    =		init_image.resize((7_60, 5_04)	)

													A_			    =		"BAAI/AltDiffusion"
													A_			    =		AltDiffusionImgaImgPipeline.from_pretrained(
													    lowerCAmelCase ,  safety_checker=lowerCAmelCase ,  )
													pipe.to(lowerCAmelCase	)
													pipe.set_progress_bar_config(disable=lowerCAmelCase	)
													pipe.enable_attention_slicing()

													A_			    =		"A fantasy landscape, trending on artstation"

													A_			    =		torch.manual_seed(0	)
													A_			    =		pipe(
													    prompt=lowerCAmelCase ,  image=lowerCAmelCase ,  strength=0.7_5 ,  guidance_scale=7.5 ,  generator=lowerCAmelCase ,  output_type="np" ,  )
													A_			    =		output.images[0]

													A_			    =		image[2_55:2_58, 3_83:3_86, -1]

													assert image.shape == (5_04, 7_60, 3)
													A_			    =		np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]	)

													assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2






@slow
@require_torch_gpu
class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/img2img/sketch-mountains-input.jpg"	)
													A_			    =		init_image.resize((7_68, 5_12)	)
													A_			    =		load_numpy(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"	)

													A_			    =		"BAAI/AltDiffusion"
													A_			    =		AltDiffusionImgaImgPipeline.from_pretrained(
													    lowerCAmelCase ,  safety_checker=lowerCAmelCase ,  )
													pipe.to(lowerCAmelCase	)
													pipe.set_progress_bar_config(disable=lowerCAmelCase	)
													pipe.enable_attention_slicing()

													A_			    =		"A fantasy landscape, trending on artstation"

													A_			    =		torch.manual_seed(0	)
													A_			    =		pipe(
													    prompt=lowerCAmelCase ,  image=lowerCAmelCase ,  strength=0.7_5 ,  guidance_scale=7.5 ,  generator=lowerCAmelCase ,  output_type="np" ,  )
													A_			    =		output.images[0]

													assert image.shape == (5_12, 7_68, 3)
													# img2img is flaky across GPUs even in fp32, so using MAE here
													assert np.abs(expected_image - image	).max() < 1e-2









'''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


__SCREAMING_SNAKE_CASE					:				Optional[int]									=		logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE					:				Dict									=		'''cuda''' if torch.cuda.is_available() else '''cpu'''







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=1_0_0						,  UpperCamelCase_=" " ):
							A_			    =		text.split(UpperCamelCase_ )
							return [character.join(text[i : i + n] ).strip() for i in range(0						,  len(UpperCamelCase_ )						,  UpperCamelCase_ )]







def       a_				(	UpperCamelCase_ ):
							A_    ,					A_			    =		[], []
							for title, text in zip(documents["title"]						,  documents["text"] ):
														if text is not None:
																					for passage in split_text(UpperCamelCase_ ):
																												titles.append(title if title is not None else "" )
																												texts.append(UpperCamelCase_ )
							return {"title": titles, "text": texts}







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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  ):
							######################################
							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(UpperCamelCase_						,  batched=UpperCamelCase_						,  num_proc=processing_args.num_proc )

							# And compute the embeddings
							A_			    =		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=UpperCamelCase_ )
							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(UpperCamelCase_						,  ctx_encoder=UpperCamelCase_						,  ctx_tokenizer=UpperCamelCase_ )						,  batched=UpperCamelCase_						,  batch_size=processing_args.batch_size						,  features=UpperCamelCase_						,  )

							# And finally save your dataset
							A_			    =		os.path.join(rag_example_args.output_dir						,  "my_knowledge_dataset" )
							dataset.save_to_disk(UpperCamelCase_ )
							# 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=UpperCamelCase_ )

							# And save the index
							A_			    =		os.path.join(rag_example_args.output_dir						,  "my_knowledge_dataset_hnsw_index.faiss" )
							dataset.get_index("embeddings" ).save(UpperCamelCase_ )
							# dataset.load_faiss_index("embeddings", index_path)  # to reload the index






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       str   						=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'"}				,			)
						_UpperCAmelCase		:       Optional[str]   						=field(
						    default=lowercase				,			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(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"""






						_UpperCAmelCase		:       Optional[int]   						=field(
						    default=lowercase				,			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 					__lowerCAmelCase   :

						"""simple docstring"""






						_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)

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





