






"""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


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






def        lowerCAmelCase (      UpperCamelCase_:							Tuple     , UpperCamelCase_:							Optional[int]=100     , UpperCamelCase_:							Union[str, Any]=" "					)       ->							str:







  '''simple docstring'''







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






def        lowerCAmelCase (      UpperCamelCase_:							str					)       ->							Optional[Any]:







  '''simple docstring'''







  _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        lowerCAmelCase (      UpperCamelCase_:							str     , UpperCamelCase_:							int     , UpperCamelCase_:							int					)       ->							List[str]:







  '''simple docstring'''







  _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        lowerCAmelCase (      UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							Any     , UpperCamelCase_:							Dict     , )       ->							int:







  '''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
  _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    lowercase_    :
      A__	:		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\''''},    )
      A__	:		Optional[str]       			=				field(
          default=UpperCAmelCase__,    metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''},    )
      A__	:		str       			=				field(
          default='''facebook/rag-sequence-nq''',    metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''},    )
      A__	:		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\''''
              )
          },    )
      A__	:		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    lowercase_    :
      A__	:		Optional[int]       			=				field(
          default=UpperCAmelCase__,    metadata={
              '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
          },    )
      A__	:		int       			=				field(
          default=16,    metadata={
              '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
          },    )







@dataclass
class    lowercase_    :
      A__	:		int       			=				field(
          default=768,    metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''},    )
      A__	:		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)










"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Union[str, Any]       			=				(PNDMScheduler,)
      A__	:		Optional[int]       			=				(('''num_inference_steps''', 50),)






      def 			lowerCamelCase__    (    self     ,     **a_	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	{
            "num_train_timesteps": 1_0_0_0,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**a_	)
        return config






      def 			lowerCamelCase__    (    self     ,     a_=0     ,     **a_	)	->Tuple:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)
        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)
        _a							=	self.dummy_sample
        _a							=	0.1 * sample
        _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config(**a_	)
          _a							=	scheduler_class(**a_	)
          scheduler.set_timesteps(a_	)
          # copy over dummy past residuals
          _a							=	dummy_past_residuals[:]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(a_	)
            _a							=	scheduler_class.from_pretrained(a_	)
            new_scheduler.set_timesteps(a_	)
            # copy over dummy past residuals
            _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        pass






      def 			lowerCamelCase__    (    self     ,     a_=0     ,     **a_	)	->Tuple:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)
        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)
        _a							=	self.dummy_sample
        _a							=	0.1 * sample
        _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)
          scheduler.set_timesteps(a_	)

          # copy over dummy past residuals (must be after setting timesteps)
          _a							=	dummy_past_residuals[:]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(a_	)
            _a							=	scheduler_class.from_pretrained(a_	)
            # copy over dummy past residuals
            new_scheduler.set_timesteps(a_	)

            # copy over dummy past residual (must be after setting timesteps)
            _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"






      def 			lowerCamelCase__    (    self     ,     **a_	)	->Optional[int]:







        '''simple docstring'''


        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(**a_	)
        _a							=	scheduler_class(**a_	)

        _a							=	1_0
        _a							=	self.dummy_model()
        _a							=	self.dummy_sample_deter
        scheduler.set_timesteps(a_	)

        for i, t in enumerate(scheduler.prk_timesteps	):
          _a							=	model(a_     ,     a_	)
          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_	).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps	):
          _a							=	model(a_     ,     a_	)
          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_	).prev_sample

        return sample






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)

        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          _a							=	self.dummy_sample
          _a							=	0.1 * sample

          if num_inference_steps is not None and hasattr(a_     ,     "set_timesteps"	):
            scheduler.set_timesteps(a_	)
          elif num_inference_steps is not None and not hasattr(a_     ,     "set_timesteps"	):
            _a							=	num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
          _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     0     ,     a_     ,     **a_	).prev_sample
          _a							=	scheduler.step_prk(a_     ,     1     ,     a_     ,     **a_	).prev_sample

          self.assertEqual(output_a.shape     ,     sample.shape	)
          self.assertEqual(output_a.shape     ,     output_a.shape	)

          _a							=	scheduler.step_plms(a_     ,     0     ,     a_     ,     **a_	).prev_sample
          _a							=	scheduler.step_plms(a_     ,     1     ,     a_     ,     **a_	).prev_sample

          self.assertEqual(output_a.shape     ,     sample.shape	)
          self.assertEqual(output_a.shape     ,     output_a.shape	)






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        for timesteps in [1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=a_	)






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=a_	)

        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(steps_offset=1	)
        _a							=	scheduler_class(**a_	)
        scheduler.set_timesteps(1_0	)
        assert torch.equal(
            scheduler.timesteps     ,     torch.LongTensor(
                [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]	)     ,     )






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        for beta_start, beta_end in zip([0.0_001, 0.001]     ,     [0.002, 0.02]	):
          self.check_over_configs(beta_start=a_     ,     beta_end=a_	)






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=a_	)






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=a_	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        for t in [1, 5, 1_0]:
          self.check_over_forward(time_step=a_	)






      def 			lowerCamelCase__    (    self	)	->List[Any]:







        '''simple docstring'''


        for t, num_inference_steps in zip([1, 5, 1_0]     ,     [1_0, 5_0, 1_0_0]	):
          self.check_over_forward(num_inference_steps=a_	)






      def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''simple docstring'''


        _a							=	2_7

        for scheduler_class in self.scheduler_classes:
          _a							=	self.dummy_sample
          _a							=	0.1 * sample

          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          scheduler.set_timesteps(a_	)

          # before power of 3 fix, would error on first step, so we only need to do two
          for i, t in enumerate(scheduler.prk_timesteps[:2]	):
            _a							=	scheduler.step_prk(a_     ,     a_     ,     a_	).prev_sample






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        with self.assertRaises(a_	):
          _a							=	self.scheduler_classes[0]
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          scheduler.step_plms(self.dummy_sample     ,     1     ,     self.dummy_sample	).prev_sample






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	self.full_loop()
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 198.1_318	) < 1E-2
        assert abs(result_mean.item() - 0.2_580	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	self.full_loop(prediction_type="v_prediction"	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 67.3_986	) < 1E-2
        assert abs(result_mean.item() - 0.0_878	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	self.full_loop(set_alpha_to_one=a_     ,     beta_start=0.01	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 230.0_399	) < 1E-2
        assert abs(result_mean.item() - 0.2_995	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        _a							=	self.full_loop(set_alpha_to_one=a_     ,     beta_start=0.01	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 186.9_482	) < 1E-2
        assert abs(result_mean.item() - 0.2_434	) < 1E-3


