





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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=1_00							,						SCREAMING_SNAKE_CASE     :						str=" "			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						dict			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						dict							,						SCREAMING_SNAKE_CASE     :						DPRContextEncoder							,						SCREAMING_SNAKE_CASE     :						DPRContextEncoderTokenizerFast			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						"RagExampleArguments"							,						SCREAMING_SNAKE_CASE     :						"ProcessingArguments"							,						SCREAMING_SNAKE_CASE     :						"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
      lowerCAmelCase  						=       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  						=       dataset.map(SCREAMING_SNAKE_CASE							,						batched=SCREAMING_SNAKE_CASE							,						num_proc=processing_args.num_proc			)

      # And compute the embeddings
      lowerCAmelCase  						=       DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name			).to(device=SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name			)
      lowerCAmelCase  						=       Features(
          {"""text""": Value("""string"""			), """title""": Value("""string"""			), """embeddings""": Sequence(Value("""float32"""			)			)}			)  # optional, save as float32 instead of float64 to save space
      lowerCAmelCase  						=       dataset.map(
          partial(SCREAMING_SNAKE_CASE							,						ctx_encoder=SCREAMING_SNAKE_CASE							,						ctx_tokenizer=SCREAMING_SNAKE_CASE			)							,						batched=SCREAMING_SNAKE_CASE							,						batch_size=processing_args.batch_size							,						features=SCREAMING_SNAKE_CASE							,						)

      # And finally save your dataset
      lowerCAmelCase  						=       os.path.join(rag_example_args.output_dir							,						"""my_knowledge_dataset"""			)
      dataset.save_to_disk(SCREAMING_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  						=       faiss.IndexHNSWFlat(index_hnsw_args.d							,						index_hnsw_args.m							,						faiss.METRIC_INNER_PRODUCT			)
      dataset.add_faiss_index("""embeddings"""							,						custom_index=SCREAMING_SNAKE_CASE			)

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

@dataclass
class 		lowercase					:
      _SCREAMING_SNAKE_CASE					=      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\''}     ,  )
      _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=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 		lowercase					:
      _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 		lowercase					:
      _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)

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








import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


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



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]       )

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



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-1



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_dpmpp_2m'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe(
				    [prompt]				,      generator=A_				,      guidance_scale=7.5				,      num_inference_steps=15				,      output_type='np'				,      use_karras_sigmas=A_				,      )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]       )

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