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






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






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






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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  ):
       ######################################
       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(lowerCAmelCase__     ,  batched=lowerCAmelCase__     ,  num_proc=processing_args.num_proc     )

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

       # And finally save your dataset
       lowerCAmelCase__					     = os.path.join(rag_example_args.output_dir     ,  'my_knowledge_dataset'     )
       dataset.save_to_disk(lowerCAmelCase__     )
       # 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=lowerCAmelCase__     )

       # And save the index
       lowerCAmelCase__					     = os.path.join(rag_example_args.output_dir     ,  'my_knowledge_dataset_hnsw_index.faiss'     )
       dataset.get_index('embeddings'     ).save(lowerCAmelCase__     )
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index
@dataclass
class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= field(
     default=str(Path(SCREAMING_SNAKE_CASE						).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'						)	,					metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}	,					)
 UpperCAmelCase_     			= field(
     default=SCREAMING_SNAKE_CASE	,					metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}	,					)
 UpperCAmelCase_     			= field(
     default='facebook/rag-sequence-nq'	,					metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}	,					)
 UpperCAmelCase_     			= 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_     			= field(
     default=str(Path(SCREAMING_SNAKE_CASE						).parent / 'test_run' / 'dummy-kb'						)	,					metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}	,					)






@dataclass
class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= field(
     default=SCREAMING_SNAKE_CASE	,					metadata={
         'help': 'The number of processes to use to split the documents into passages. Default is single process.'
     }	,					)
 UpperCAmelCase_     			= field(
     default=16	,					metadata={
         'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
     }	,					)






@dataclass
class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= field(
     default=768	,					metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}	,					)
 UpperCAmelCase_     			= 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)

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

from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)
@flax.struct.dataclass
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= 42






@flax_register_to_config
class     a_			(						nn.Module	,					SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= 32
 UpperCAmelCase_     			= 4
 UpperCAmelCase_     			= 4
 UpperCAmelCase_     			= (
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "DownBlock2D",
 )
 UpperCAmelCase_     			= ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= (320, 640, 1_280, 1_280)
 UpperCAmelCase_     			= 2
 UpperCAmelCase_     			= 8
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= 1_280
 UpperCAmelCase_     			= 0.0
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= jnp.floataa
 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= 0
 UpperCAmelCase_     			= False




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  jax.random.KeyArray):







        '''simple docstring'''



        lowerCAmelCase__					     = (1, self.in_channels, self.sample_size, self.sample_size)
        lowerCAmelCase__					     = jnp.zeros(lowercase__      ,   dtype=jnp.floataa)
        lowerCAmelCase__					     = jnp.ones((1,)      ,   dtype=jnp.intaa)
        lowerCAmelCase__					     = jnp.zeros((1, 1, self.cross_attention_dim)      ,   dtype=jnp.floataa)

        lowerCAmelCase__     , lowerCAmelCase__					     = jax.random.split(lowercase__)
        lowerCAmelCase__					     = {'params': params_rng, 'dropout': dropout_rng}

        return self.init(lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__)["params"]




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.block_out_channels
        lowerCAmelCase__					     = block_out_channels[0] * 4

        if self.num_attention_heads is not None:
               raise ValueError(
                   'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.')

        # If `num_attention_heads` is not defined (which is the case for most models)
        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
        # The reason for this behavior is to correct for incorrectly named variables that were introduced
        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
        # which is why we correct for the naming here.
        lowerCAmelCase__					     = self.num_attention_heads or self.attention_head_dim

        # input
        lowerCAmelCase__					     = nn.Conv(
            block_out_channels[0]      ,   kernel_size=(3, 3)      ,   strides=(1, 1)      ,   padding=((1, 1), (1, 1))      ,   dtype=self.dtype      ,   )

        # time
        lowerCAmelCase__					     = FlaxTimesteps(
            block_out_channels[0]      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   freq_shift=self.config.freq_shift)
        lowerCAmelCase__					     = FlaxTimestepEmbedding(lowercase__      ,   dtype=self.dtype)

        lowerCAmelCase__					     = self.only_cross_attention
        if isinstance(lowercase__      ,   lowercase__):
               lowerCAmelCase__					     = (only_cross_attention,) * len(self.down_block_types)

        if isinstance(lowercase__      ,   lowercase__):
               lowerCAmelCase__					     = (num_attention_heads,) * len(self.down_block_types)

        # down
        lowerCAmelCase__					     = []
        lowerCAmelCase__					     = block_out_channels[0]
        for i, down_block_type in enumerate(self.down_block_types):
               lowerCAmelCase__					     = output_channel
               lowerCAmelCase__					     = block_out_channels[i]
               lowerCAmelCase__					     = i == len(lowercase__) - 1

               if down_block_type == "CrossAttnDownBlock2D":
                      lowerCAmelCase__					     = FlaxCrossAttnDownBlockaD(
                          in_channels=lowercase__      ,   out_channels=lowercase__      ,   dropout=self.dropout      ,   num_layers=self.layers_per_block      ,   num_attention_heads=num_attention_heads[i]      ,   add_downsample=not is_final_block      ,   use_linear_projection=self.use_linear_projection      ,   only_cross_attention=only_cross_attention[i]      ,   use_memory_efficient_attention=self.use_memory_efficient_attention      ,   dtype=self.dtype      ,   )
               else:
                      lowerCAmelCase__					     = FlaxDownBlockaD(
                          in_channels=lowercase__      ,   out_channels=lowercase__      ,   dropout=self.dropout      ,   num_layers=self.layers_per_block      ,   add_downsample=not is_final_block      ,   dtype=self.dtype      ,   )

               down_blocks.append(lowercase__)
        lowerCAmelCase__					     = down_blocks

        # mid
        lowerCAmelCase__					     = FlaxUNetMidBlockaDCrossAttn(
            in_channels=block_out_channels[-1]      ,   dropout=self.dropout      ,   num_attention_heads=num_attention_heads[-1]      ,   use_linear_projection=self.use_linear_projection      ,   use_memory_efficient_attention=self.use_memory_efficient_attention      ,   dtype=self.dtype      ,   )

        # up
        lowerCAmelCase__					     = []
        lowerCAmelCase__					     = list(reversed(lowercase__))
        lowerCAmelCase__					     = list(reversed(lowercase__))
        lowerCAmelCase__					     = list(reversed(lowercase__))
        lowerCAmelCase__					     = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(self.up_block_types):
               lowerCAmelCase__					     = output_channel
               lowerCAmelCase__					     = reversed_block_out_channels[i]
               lowerCAmelCase__					     = reversed_block_out_channels[min(i + 1      ,   len(lowercase__) - 1)]

               lowerCAmelCase__					     = i == len(lowercase__) - 1

               if up_block_type == "CrossAttnUpBlock2D":
                      lowerCAmelCase__					     = FlaxCrossAttnUpBlockaD(
                          in_channels=lowercase__      ,   out_channels=lowercase__      ,   prev_output_channel=lowercase__      ,   num_layers=self.layers_per_block + 1      ,   num_attention_heads=reversed_num_attention_heads[i]      ,   add_upsample=not is_final_block      ,   dropout=self.dropout      ,   use_linear_projection=self.use_linear_projection      ,   only_cross_attention=only_cross_attention[i]      ,   use_memory_efficient_attention=self.use_memory_efficient_attention      ,   dtype=self.dtype      ,   )
               else:
                      lowerCAmelCase__					     = FlaxUpBlockaD(
                          in_channels=lowercase__      ,   out_channels=lowercase__      ,   prev_output_channel=lowercase__      ,   num_layers=self.layers_per_block + 1      ,   add_upsample=not is_final_block      ,   dropout=self.dropout      ,   dtype=self.dtype      ,   )

               up_blocks.append(lowercase__)
               lowerCAmelCase__					     = output_channel
        lowerCAmelCase__					     = up_blocks

        # out
        lowerCAmelCase__					     = nn.GroupNorm(num_groups=32      ,   epsilon=1e-5)
        lowerCAmelCase__					     = nn.Conv(
            self.out_channels      ,   kernel_size=(3, 3)      ,   strides=(1, 1)      ,   padding=((1, 1), (1, 1))      ,   dtype=self.dtype      ,   )




 def __call__(      self :  Optional[Any]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  List[str]      ,   lowercase__ :  int      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  Union[str, Any]=None      ,   lowercase__ :  bool = True      ,   lowercase__ :  bool = False      ,   ):







        '''simple docstring'''



        if not isinstance(lowercase__      ,   jnp.ndarray):
               lowerCAmelCase__					     = jnp.array([timesteps]      ,   dtype=jnp.intaa)
        elif isinstance(lowercase__      ,   jnp.ndarray) and len(timesteps.shape) == 0:
               lowerCAmelCase__					     = timesteps.astype(dtype=jnp.floataa)
               lowerCAmelCase__					     = jnp.expand_dims(lowercase__      ,   0)

        lowerCAmelCase__					     = self.time_proj(lowercase__)
        lowerCAmelCase__					     = self.time_embedding(lowercase__)

        # 2. pre-process
        lowerCAmelCase__					     = jnp.transpose(lowercase__      ,   (0, 2, 3, 1))
        lowerCAmelCase__					     = self.conv_in(lowercase__)

        # 3. down
        lowerCAmelCase__					     = (sample,)
        for down_block in self.down_blocks:
               if isinstance(lowercase__      ,   lowercase__):
                      lowerCAmelCase__     , lowerCAmelCase__					     = down_block(lowercase__      ,   lowercase__      ,   lowercase__      ,   deterministic=not train)
               else:
                      lowerCAmelCase__     , lowerCAmelCase__					     = down_block(lowercase__      ,   lowercase__      ,   deterministic=not train)
               down_block_res_samples += res_samples

        if down_block_additional_residuals is not None:
               lowerCAmelCase__					     = ()

               for down_block_res_sample, down_block_additional_residual in zip(
                   lowercase__      ,   lowercase__):
                      down_block_res_sample += down_block_additional_residual
                      new_down_block_res_samples += (down_block_res_sample,)

               lowerCAmelCase__					     = new_down_block_res_samples

        # 4. mid
        lowerCAmelCase__					     = self.mid_block(lowercase__      ,   lowercase__      ,   lowercase__      ,   deterministic=not train)

        if mid_block_additional_residual is not None:
               sample += mid_block_additional_residual

        # 5. up
        for up_block in self.up_blocks:
               lowerCAmelCase__					     = down_block_res_samples[-(self.layers_per_block + 1) :]
               lowerCAmelCase__					     = down_block_res_samples[: -(self.layers_per_block + 1)]
               if isinstance(lowercase__      ,   lowercase__):
                      lowerCAmelCase__					     = up_block(
                          lowercase__      ,   temb=lowercase__      ,   encoder_hidden_states=lowercase__      ,   res_hidden_states_tuple=lowercase__      ,   deterministic=not train      ,   )
               else:
                      lowerCAmelCase__					     = up_block(lowercase__      ,   temb=lowercase__      ,   res_hidden_states_tuple=lowercase__      ,   deterministic=not train)

        # 6. post-process
        lowerCAmelCase__					     = self.conv_norm_out(lowercase__)
        lowerCAmelCase__					     = nn.silu(lowercase__)
        lowerCAmelCase__					     = self.conv_out(lowercase__)
        lowerCAmelCase__					     = jnp.transpose(lowercase__      ,   (0, 3, 1, 2))

        if not return_dict:
               return (sample,)

        return FlaxUNetaDConditionOutput(sample=lowercase__)
