



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


__a      :       List[str]				  =   logging.getLogger(__name__)
torch.set_grad_enabled(False)
__a      :       Optional[Any]				  =   "cuda" if torch.cuda.is_available() else "cpu"



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			Tuple=1_0_0			,				__lowercase   :			str=" "    )						->      List[str]:





    """simple docstring"""

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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			dict    )						->      dict:





    """simple docstring"""

    __A    ,	__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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			dict			,				__lowercase   :			DPRContextEncoder			,				__lowercase   :			DPRContextEncoderTokenizerFast    )						->      dict:





    """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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			"RagExampleArguments"			,				__lowercase   :			"ProcessingArguments"			,				__lowercase   :			"IndexHnswArguments"			,				)						->      Tuple:





    """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      :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				field(
         default=str(Path(lowercase_  ).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=lowercase_			,      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(lowercase_  ).parent / "test_run" / "dummy-kb"  )			,      metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}			,      )


@dataclass
class       __lowercase      :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				field(
         default=lowercase_			,      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      :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				field(
         default=7_68			,      metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}			,      )
     SCREAMING_SNAKE_CASE   						=				field(
         default=1_28			,      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)

  __a      :       List[str]				  =   HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
  __a  ,__a  ,__a      :       str				  =   parser.parse_args_into_dataclasses()
  with TemporaryDirectory() as tmp_dir:
    __a      :       Tuple				  =   rag_example_args.output_dir or tmp_dir
    main(rag_example_args, processing_args, index_hnsw_args)












import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str    )						->      str:





    """simple docstring"""

    if "cls_token" in name:
        __A         =	name.replace("""cls_token"""			,				"""vit.embeddings.cls_token"""    )
    if "mask_token" in name:
        __A         =	name.replace("""mask_token"""			,				"""decoder.mask_token"""    )
    if "decoder_pos_embed" in name:
        __A         =	name.replace("""decoder_pos_embed"""			,				"""decoder.decoder_pos_embed"""    )
    if "pos_embed" in name and "decoder" not in name:
        __A         =	name.replace("""pos_embed"""			,				"""vit.embeddings.position_embeddings"""    )
    if "patch_embed.proj" in name:
        __A         =	name.replace("""patch_embed.proj"""			,				"""vit.embeddings.patch_embeddings.projection"""    )
    if "patch_embed.norm" in name:
        __A         =	name.replace("""patch_embed.norm"""			,				"""vit.embeddings.norm"""    )
    if "decoder_blocks" in name:
        __A         =	name.replace("""decoder_blocks"""			,				"""decoder.decoder_layers"""    )
    if "blocks" in name:
        __A         =	name.replace("""blocks"""			,				"""vit.encoder.layer"""    )
    if "attn.proj" in name:
        __A         =	name.replace("""attn.proj"""			,				"""attention.output.dense"""    )
    if "attn" in name:
        __A         =	name.replace("""attn"""			,				"""attention.self"""    )
    if "norm1" in name:
        __A         =	name.replace("""norm1"""			,				"""layernorm_before"""    )
    if "norm2" in name:
        __A         =	name.replace("""norm2"""			,				"""layernorm_after"""    )
    if "mlp.fc1" in name:
        __A         =	name.replace("""mlp.fc1"""			,				"""intermediate.dense"""    )
    if "mlp.fc2" in name:
        __A         =	name.replace("""mlp.fc2"""			,				"""output.dense"""    )
    if "decoder_embed" in name:
        __A         =	name.replace("""decoder_embed"""			,				"""decoder.decoder_embed"""    )
    if "decoder_norm" in name:
        __A         =	name.replace("""decoder_norm"""			,				"""decoder.decoder_norm"""    )
    if "decoder_pred" in name:
        __A         =	name.replace("""decoder_pred"""			,				"""decoder.decoder_pred"""    )
    if "norm.weight" in name and "decoder" not in name:
        __A         =	name.replace("""norm.weight"""			,				"""vit.layernorm.weight"""    )
    if "norm.bias" in name and "decoder" not in name:
        __A         =	name.replace("""norm.bias"""			,				"""vit.layernorm.bias"""    )

    return name



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any			,				__lowercase   :			Dict    )						->      str:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        __A         =	orig_state_dict.pop(__lowercase    )

        if "qkv" in key:
            __A         =	key.split("""."""    )
            __A         =	int(key_split[1]    )
            if "decoder_blocks" in key:
                __A         =	config.decoder_hidden_size
                __A         =	"""decoder.decoder_layers."""
                if "weight" in key:
                    __A         =	val[:dim, :]
                    __A         =	val[dim : dim * 2, :]
                    __A         =	val[-dim:, :]
                elif "bias" in key:
                    __A         =	val[:dim]
                    __A         =	val[dim : dim * 2]
                    __A         =	val[-dim:]
            else:
                __A         =	config.hidden_size
                __A         =	"""vit.encoder.layer."""
                if "weight" in key:
                    __A         =	val[:dim, :]
                    __A         =	val[dim : dim * 2, :]
                    __A         =	val[-dim:, :]
                elif "bias" in key:
                    __A         =	val[:dim]
                    __A         =	val[dim : dim * 2]
                    __A         =	val[-dim:]

        else:
            __A         =	val

    return orig_state_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			str    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	ViTMAEConfig()
    if "large" in checkpoint_url:
        __A         =	1_0_2_4
        __A         =	4_0_9_6
        __A         =	2_4
        __A         =	1_6
    elif "huge" in checkpoint_url:
        __A         =	1_4
        __A         =	1_2_8_0
        __A         =	5_1_2_0
        __A         =	3_2
        __A         =	1_6

    __A         =	ViTMAEForPreTraining(__lowercase    )

    __A         =	torch.hub.load_state_dict_from_url(__lowercase			,				map_location="""cpu"""    )["""model"""]

    __A         =	ViTMAEImageProcessor(size=config.image_size    )

    __A         =	convert_state_dict(__lowercase			,				__lowercase    )

    model.load_state_dict(__lowercase    )
    model.eval()

    __A         =	"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

    __A         =	Image.open(requests.get(__lowercase			,				stream=__lowercase    ).raw    )
    __A         =	ViTMAEImageProcessor(size=config.image_size    )
    __A         =	image_processor(images=__lowercase			,				return_tensors="""pt"""    )

    # forward pass
    torch.manual_seed(2    )
    __A         =	model(**__lowercase    )
    __A         =	outputs.logits

    if "large" in checkpoint_url:
        __A         =	torch.tensor(
            [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]    )
    elif "huge" in checkpoint_url:
        __A         =	torch.tensor(
            [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]    )
    else:
        __A         =	torch.tensor(
            [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]    )

    # verify logits
    assert torch.allclose(logits[0, :3, :3]			,				__lowercase			,				atol=1E-4    )

    print(f"Saving model to {pytorch_dump_folder_path}"    )
    model.save_pretrained(__lowercase    )

    print(f"Saving image processor to {pytorch_dump_folder_path}"    )
    image_processor.save_pretrained(__lowercase    )


if __name__ == "__main__":
  __a      :       List[str]				  =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
      type=str,
      help="URL of the checkpoint you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )

  __a      :       List[str]				  =   parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




