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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


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

def      lowercase_							(     _lowerCamelCase				:			str						,      _lowerCamelCase				:			Optional[Any]=100						,      _lowerCamelCase				:			Optional[int]=" "):
    lowercase__					:  Optional[int]          =							text.split(_lowerCamelCase)
    return [character.join(text[i : i + n]).strip() for i in range(0						,      len(_lowerCamelCase)						,      _lowerCamelCase)]

def      lowercase_							(     _lowerCamelCase				:			dict):
    lowercase__   ,  lowercase__					:  Tuple          =							[], []
    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      lowercase_							(     _lowerCamelCase				:			dict						,      _lowerCamelCase				:			DPRContextEncoder						,      _lowerCamelCase				:			DPRContextEncoderTokenizerFast):
    lowercase__					:  Optional[int]          =							ctx_tokenizer(
        documents["title"]						,      documents["text"]						,      truncation=_lowerCamelCase						,      padding="longest"						,      return_tensors="pt")["input_ids"]
    lowercase__					:  Tuple          =							ctx_encoder(input_ids.to(device=_lowerCamelCase)						,      return_dict=_lowerCamelCase).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}

def      lowercase_							(     _lowerCamelCase				:			"RagExampleArguments"						,      _lowerCamelCase				:			"ProcessingArguments"						,      _lowerCamelCase				:			"IndexHnswArguments"						,      ):
    ######################################
    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
    lowercase__					:  Optional[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
    lowercase__					:  Tuple          =							dataset.map(_lowerCamelCase						,      batched=_lowerCamelCase						,      num_proc=processing_args.num_proc)

    # And compute the embeddings
    lowercase__					:  Optional[Any]          =							DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=_lowerCamelCase)
    lowercase__					:  Optional[Any]          =							DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name)
    lowercase__					:  int          =							Features(
        {"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))})  # optional, save as float32 instead of float64 to save space
    lowercase__					:  Any          =							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
    lowercase__					:  int          =							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
    lowercase__					:  Any          =							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
    lowercase__					:  Union[str, Any]          =							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

    ######################################
    logger.info("Step 3 - Load RAG")
    ######################################

    # Easy way to load the model
    lowercase__					:  Any          =							RagRetriever.from_pretrained(
        rag_example_args.rag_model_name						,      index_name="custom"						,      indexed_dataset=_lowerCamelCase)
    lowercase__					:  Optional[Any]          =							RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name						,      retriever=_lowerCamelCase)
    lowercase__					:  str          =							RagTokenizer.from_pretrained(rag_example_args.rag_model_name)

    # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
    # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

    ######################################
    logger.info("Step 4 - Have fun")
    ######################################

    lowercase__					:  Tuple          =							rag_example_args.question or "What does Moses' rod turn into ?"
    lowercase__					:  Optional[int]          =							tokenizer.question_encoder(_lowerCamelCase						,      return_tensors="pt")["input_ids"]
    lowercase__					:  Optional[Any]          =							model.generate(_lowerCamelCase)
    lowercase__					:  Any          =							tokenizer.batch_decode(_lowerCamelCase						,      skip_special_tokens=_lowerCamelCase)[0]
    logger.info("Q: " + question)
    logger.info("A: " + generated_string)



@dataclass
class     snake_case_			:
   __A      :						str   =							field(
       default=str(Path(__A			).parent / "test_data" / "my_knowledge_dataset.csv"			)    ,metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}    ,)
   __A      :						Optional[str]   =							field(
       default=__A    ,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=__A    ,metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}    ,)



@dataclass
class     snake_case_			:
   __A      :						Optional[int]   =							field(
       default=__A    ,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     snake_case_			:
   __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)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase    				=		{'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		[
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase    				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



