






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


lowerCamelCase      :				int							=logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase      :				Dict							='''cuda''' if torch.cuda.is_available() else '''cpu'''







def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase   ,       __lowerCAmelCase=100   ,       __lowerCAmelCase=" "     )					->      List[str]:
     UpperCamelCase__     :  Dict										=    text.split(UpperCAmelCase_     )
     return [character.join(text[i : i + n]     ).strip() for i in range(0   ,       len(UpperCAmelCase_     )   ,       UpperCAmelCase_     )]







def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase     )					->      dict:
     UpperCamelCase__     :  Dict										=    [], []
     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 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase   ,       __lowerCAmelCase   ,       __lowerCAmelCase     )					->      dict:
     UpperCamelCase__     :  Tuple										=    ctx_tokenizer(
         documents["title"]   ,       documents["text"]   ,       truncation=UpperCAmelCase_   ,       padding="longest"   ,       return_tensors="pt"     )['''input_ids''']
     UpperCamelCase__     :  Dict										=    ctx_encoder(input_ids.to(device=UpperCAmelCase_     )   ,       return_dict=UpperCAmelCase_     ).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}







def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase   ,       __lowerCAmelCase   ,       __lowerCAmelCase   ,       )					->      int:
     ######################################
     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
     UpperCamelCase__     :  Union[str, 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
     UpperCamelCase__     :  Optional[Any]										=    dataset.map(UpperCAmelCase_   ,       batched=UpperCAmelCase_   ,       num_proc=processing_args.num_proc     )

     # And compute the embeddings
     UpperCamelCase__     :  Union[str, Any]										=    DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     ).to(device=UpperCAmelCase_     )
     UpperCamelCase__     :  Optional[int]										=    DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     )
     UpperCamelCase__     :  Optional[int]										=    Features(
         {"text": Value("string"     ), "title": Value("string"     ), "embeddings": Sequence(Value("float32"     )     )}     )  # optional, save as float32 instead of float64 to save space
     UpperCamelCase__     :  Union[str, Any]										=    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
     UpperCamelCase__     :  Optional[Any]										=    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
     UpperCamelCase__     :  Tuple										=    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
     UpperCamelCase__     :  str										=    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      __a    :
 _lowerCAmelCase			:			List[str]								=					field(
     default=str(Path(A__			).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''			)			,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}			,  )
 _lowerCAmelCase			:			Optional[int]								=					field(
     default=A__			,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}			,  )
 _lowerCAmelCase			:			Any								=					field(
     default='''facebook/rag-sequence-nq'''			,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}			,  )
 _lowerCAmelCase			:			Optional[Any]								=					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			:			List[Any]								=					field(
     default=str(Path(A__			).parent / '''test_run''' / '''dummy-kb'''			)			,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}			,  )







@dataclass
class      __a    :
 _lowerCAmelCase			:			List[str]								=					field(
     default=A__			,  metadata={
         '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
     }			,  )
 _lowerCAmelCase			:			List[Any]								=					field(
     default=1_6			,  metadata={
         '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
     }			,  )







@dataclass
class      __a    :
 _lowerCAmelCase			:			Dict								=					field(
     default=7_6_8			,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}			,  )
 _lowerCAmelCase			:			Tuple								=					field(
     default=1_2_8			,  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      :				Union[str, Any]							=HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 lowerCamelCase							,     lowerCamelCase							,     lowerCamelCase      :				str							=parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  lowerCamelCase      :				Optional[Any]							=rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						42
       __lowerCamelCase				       =						42


       def __init__(    self						,       __A						,       __A				)						->    List[str]:
              super().__init__()
              self.register_modules(unet=__A						,       scheduler=__A				)





       @torch.no_grad()
       def __call__(    self						,       __A = 1						,       __A = 50						,       __A = None						,       __A = "pil"						,       __A = True						,       **__A						,       )						->    Union[Tuple, ImagePipelineOutput]:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.unet.config.sample_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=(batch_size, 3, img_size, img_size)

              SCREAMING_SNAKE_CASE_							:				int		=self.unet

              # sample x_0 ~ N(0, sigma_0^2 * I)
              SCREAMING_SNAKE_CASE_							:				Tuple		=randn_tensor(__A						,       generator=__A						,       device=self.device				) * self.scheduler.init_noise_sigma

              self.scheduler.set_timesteps(__A				)

              for t in self.progress_bar(self.scheduler.timesteps				):
                     # here sigma_t == t_i from the paper
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler.schedule[t]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.schedule[t - 1] if t > 0 else 0

                     # 1. Select temporarily increased noise level sigma_hat
                     # 2. Add new noise to move from sample_i to sample_hat
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.add_noise_to_input(__A						,       __A						,       generator=__A				)

                     # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                     # The model inputs and output are adjusted by following eq. (213) in [1].
                     SCREAMING_SNAKE_CASE_							:				Tuple		=(sigma_hat / 2) * model((sample_hat + 1) / 2						,       sigma_hat / 2				).sample

                     # 4. Evaluate dx/dt at sigma_hat
                     # 5. Take Euler step from sigma to sigma_prev
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.step(__A						,       __A						,       __A						,       __A				)

                     if sigma_prev != 0:
                            # 6. Apply 2nd order correction
                            # The model inputs and output are adjusted by following eq. (213) in [1].
                            SCREAMING_SNAKE_CASE_							:				int		=(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2						,       sigma_prev / 2				).sample
                            SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.step_correct(
                                __A						,       __A						,       __A						,       __A						,       step_output.prev_sample						,       step_output['''derivative''']						,       )
                     SCREAMING_SNAKE_CASE_							:				Any		=step_output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=(sample / 2 + 0.5).clamp(0						,       1				)
              SCREAMING_SNAKE_CASE_							:				Any		=sample.cpu().permute(0						,       2						,       3						,       1				).numpy()
              if output_type == "pil":
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.numpy_to_pil(__A				)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=__A				)
