





'''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


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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=100				,						__UpperCAmelCase=" "   )    ->       List[str]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							[], []
   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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						)    ->       Any:

   '''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
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							dataset.map(__UpperCAmelCase				,						batched=__UpperCAmelCase				,						num_proc=processing_args.num_proc   )

   # And compute the embeddings
   __SCREAMING_SNAKE_CASE   				=							DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
   __SCREAMING_SNAKE_CASE   				=							Features(
       {"""text""": Value("""string"""   ), """title""": Value("""string"""   ), """embeddings""": Sequence(Value("""float32"""   )   )}   )  # optional, save as float32 instead of float64 to save space
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							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			:
   __UpperCamelCase :			str						   =						field(
       default=str(Path(_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 :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}, )
   __UpperCamelCase :			str						   =						field(
       default='facebook/rag-sequence-nq', metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}, )
   __UpperCamelCase :			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\''
           )
       }, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=str(Path(_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			:
   __UpperCamelCase :			Optional[int]						   =						field(
       default=_snake_case, metadata={
           'help': 'The number of processes to use to split the documents into passages. Default is single process.'
       }, )
   __UpperCamelCase :			int						   =						field(
       default=16, metadata={
           'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
       }, )







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

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







import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)


def      UpperCamelCase   (       __lowerCamelCase	:					Dict       ):
       snake_case			:  Any     							= VideoMAEConfig()

       set_architecture_configs(__lowerCamelCase							,   __lowerCamelCase       )

       if "finetuned" not in model_name:
              snake_case			:  int     							= False

       if "finetuned" in model_name:
              snake_case			:  Dict     							= "huggingface/label-files"
              if "kinetics" in model_name:
                     snake_case			:  List[str]     							= 400
                     snake_case			:  List[str]     							= "kinetics400-id2label.json"
              elif "ssv2" in model_name:
                     snake_case			:  List[str]     							= 174
                     snake_case			:  Optional[int]     							= "something-something-v2-id2label.json"
              else:
                     raise ValueError("Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."       )
              snake_case			:  Tuple     							= json.load(open(hf_hub_download(__lowerCamelCase							,   __lowerCamelCase							,   repo_type="dataset"       )							,   "r"       )       )
              snake_case			:  Dict     							= {int(__lowerCamelCase       ): v for k, v in idalabel.items()}
              snake_case			:  Optional[Any]     							= idalabel
              snake_case			:  Tuple     							= {v: k for k, v in idalabel.items()}

       return config


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					int       ):
       if "small" in model_name:
              snake_case			:  Dict     							= 384
              snake_case			:  Optional[Any]     							= 1536
              snake_case			:  Union[str, Any]     							= 12
              snake_case			:  Dict     							= 16
              snake_case			:  List[str]     							= 12
              snake_case			:  List[str]     							= 3
              snake_case			:  str     							= 192
              snake_case			:  Union[str, Any]     							= 768
       elif "large" in model_name:
              snake_case			:  Tuple     							= 1024
              snake_case			:  List[Any]     							= 4096
              snake_case			:  Any     							= 24
              snake_case			:  Optional[int]     							= 16
              snake_case			:  List[str]     							= 12
              snake_case			:  int     							= 8
              snake_case			:  Any     							= 512
              snake_case			:  List[Any]     							= 2048
       elif "huge" in model_name:
              snake_case			:  Any     							= 1280
              snake_case			:  List[str]     							= 5120
              snake_case			:  Dict     							= 32
              snake_case			:  int     							= 16
              snake_case			:  int     							= 12
              snake_case			:  Tuple     							= 8
              snake_case			:  str     							= 640
              snake_case			:  List[Any]     							= 2560
       elif "base" not in model_name:
              raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""       )


def      UpperCamelCase   (       __lowerCamelCase	:					Any       ):
       if "encoder." in name:
              snake_case			:  Dict     							= name.replace("encoder."							,   ""       )
       if "cls_token" in name:
              snake_case			:  str     							= name.replace("cls_token"							,   "videomae.embeddings.cls_token"       )
       if "decoder_pos_embed" in name:
              snake_case			:  Tuple     							= name.replace("decoder_pos_embed"							,   "decoder.decoder_pos_embed"       )
       if "pos_embed" in name and "decoder" not in name:
              snake_case			:  str     							= name.replace("pos_embed"							,   "videomae.embeddings.position_embeddings"       )
       if "patch_embed.proj" in name:
              snake_case			:  int     							= name.replace("patch_embed.proj"							,   "videomae.embeddings.patch_embeddings.projection"       )
       if "patch_embed.norm" in name:
              snake_case			:  List[Any]     							= name.replace("patch_embed.norm"							,   "videomae.embeddings.norm"       )
       if "decoder.blocks" in name:
              snake_case			:  Tuple     							= name.replace("decoder.blocks"							,   "decoder.decoder_layers"       )
       if "blocks" in name:
              snake_case			:  Any     							= name.replace("blocks"							,   "videomae.encoder.layer"       )
       if "attn.proj" in name:
              snake_case			:  int     							= name.replace("attn.proj"							,   "attention.output.dense"       )
       if "attn" in name and "bias" not in name:
              snake_case			:  Optional[Any]     							= name.replace("attn"							,   "attention.self"       )
       if "attn" in name:
              snake_case			:  List[str]     							= name.replace("attn"							,   "attention.attention"       )
       if "norm1" in name:
              snake_case			:  Optional[int]     							= name.replace("norm1"							,   "layernorm_before"       )
       if "norm2" in name:
              snake_case			:  Any     							= name.replace("norm2"							,   "layernorm_after"       )
       if "mlp.fc1" in name:
              snake_case			:  Optional[int]     							= name.replace("mlp.fc1"							,   "intermediate.dense"       )
       if "mlp.fc2" in name:
              snake_case			:  Union[str, Any]     							= name.replace("mlp.fc2"							,   "output.dense"       )
       if "decoder_embed" in name:
              snake_case			:  Optional[int]     							= name.replace("decoder_embed"							,   "decoder.decoder_embed"       )
       if "decoder_norm" in name:
              snake_case			:  Tuple     							= name.replace("decoder_norm"							,   "decoder.decoder_norm"       )
       if "decoder_pred" in name:
              snake_case			:  str     							= name.replace("decoder_pred"							,   "decoder.decoder_pred"       )
       if "norm.weight" in name and "decoder" not in name and "fc" not in name:
              snake_case			:  List[Any]     							= name.replace("norm.weight"							,   "videomae.layernorm.weight"       )
       if "norm.bias" in name and "decoder" not in name and "fc" not in name:
              snake_case			:  Dict     							= name.replace("norm.bias"							,   "videomae.layernorm.bias"       )
       if "head" in name and "decoder" not in name:
              snake_case			:  Union[str, Any]     							= name.replace("head"							,   "classifier"       )

       return name


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Any       ):
       for key in orig_state_dict.copy().keys():
              snake_case			:  Optional[Any]     							= orig_state_dict.pop(__lowerCamelCase       )

              if key.startswith("encoder."       ):
                     snake_case			:  Optional[Any]     							= key.replace("encoder."							,   ""       )

              if "qkv" in key:
                     snake_case			:  Tuple     							= key.split("."       )
                     if key.startswith("decoder.blocks"       ):
                            snake_case			:  str     							= config.decoder_hidden_size
                            snake_case			:  Optional[Any]     							= int(key_split[2]       )
                            snake_case			:  Tuple     							= "decoder.decoder_layers."
                            if "weight" in key:
                                   snake_case			:  Optional[Any]     							= val[:dim, :]
                                   snake_case			:  List[str]     							= val[dim : dim * 2, :]
                                   snake_case			:  str     							= val[-dim:, :]
                     else:
                            snake_case			:  str     							= config.hidden_size
                            snake_case			:  str     							= int(key_split[1]       )
                            snake_case			:  Union[str, Any]     							= "videomae.encoder.layer."
                            if "weight" in key:
                                   snake_case			:  int     							= val[:dim, :]
                                   snake_case			:  int     							= val[dim : dim * 2, :]
                                   snake_case			:  Union[str, Any]     							= val[-dim:, :]
              else:
                     snake_case			:  Optional[Any]     							= val

       return orig_state_dict


def      UpperCamelCase   (       ):
       snake_case			:  Union[str, Any]     							= hf_hub_download(
           repo_id="hf-internal-testing/spaghetti-video"							,   filename="eating_spaghetti.npy"							,   repo_type="dataset"       )
       snake_case			:  Optional[Any]     							= np.load(__lowerCamelCase       )
       return list(__lowerCamelCase       )


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Any       ):
       snake_case			:  List[str]     							= get_videomae_config(__lowerCamelCase       )

       if "finetuned" in model_name:
              snake_case			:  List[Any]     							= VideoMAEForVideoClassification(__lowerCamelCase       )
       else:
              snake_case			:  List[str]     							= VideoMAEForPreTraining(__lowerCamelCase       )

       # download original checkpoint, hosted on Google Drive
       snake_case			:  List[Any]     							= "pytorch_model.bin"
       gdown.cached_download(__lowerCamelCase							,   __lowerCamelCase							,   quiet=__lowerCamelCase       )
       snake_case			:  str     							= torch.load(__lowerCamelCase							,   map_location="cpu"       )
       if "model" in files:
              snake_case			:  List[str]     							= files["model"]
       else:
              snake_case			:  List[str]     							= files["module"]
       snake_case			:  List[str]     							= convert_state_dict(__lowerCamelCase							,   __lowerCamelCase       )

       model.load_state_dict(__lowerCamelCase       )
       model.eval()

       # verify model on basic input
       snake_case			:  Tuple     							= VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]							,   image_std=[0.5, 0.5, 0.5]       )
       snake_case			:  Dict     							= prepare_video()
       snake_case			:  Dict     							= image_processor(__lowerCamelCase							,   return_tensors="pt"       )

       if "finetuned" not in model_name:
              snake_case			:  List[Any]     							= hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"							,   filename="bool_masked_pos.pt"       )
              snake_case			:  Union[str, Any]     							= torch.load(__lowerCamelCase       )

       snake_case			:  Any     							= model(**__lowerCamelCase       )
       snake_case			:  List[Any]     							= outputs.logits

       snake_case			:  Any     							= [
           "videomae-small-finetuned-kinetics",
           "videomae-small-finetuned-ssv2",
           # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
           "videomae-base-short",
           "videomae-base-short-finetuned-kinetics",
           "videomae-base",
           "videomae-base-finetuned-kinetics",
           "videomae-large",
           "videomae-large-finetuned-kinetics",
           "videomae-huge-finetuned-kinetics",
           # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
           "videomae-base-short-ssv2",
           "videomae-base-short-finetuned-ssv2",
           "videomae-base-ssv2",
           "videomae-base-finetuned-ssv2",
       ]

       # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
       if model_name == "videomae-small-finetuned-kinetics":
              snake_case			:  List[str]     							= torch.Size([1, 400]       )
              snake_case			:  List[str]     							= torch.tensor([-0.9291, -0.4061, -0.9307]       )
       elif model_name == "videomae-small-finetuned-ssv2":
              snake_case			:  Dict     							= torch.Size([1, 174]       )
              snake_case			:  Tuple     							= torch.tensor([0.2671, -0.4689, -0.8235]       )
       elif model_name == "videomae-base":
              snake_case			:  Union[str, Any]     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  Union[str, Any]     							= torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]       )
       elif model_name == "videomae-base-short":
              snake_case			:  str     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  List[Any]     							= torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]       )
              # we verified the loss both for normalized and unnormalized targets for this one
              snake_case			:  int     							= torch.tensor([0.5142]       ) if config.norm_pix_loss else torch.tensor([0.6469]       )
       elif model_name == "videomae-large":
              snake_case			:  Optional[int]     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  Tuple     							= torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]       )
       elif model_name == "videomae-large-finetuned-kinetics":
              snake_case			:  Dict     							= torch.Size([1, 400]       )
              snake_case			:  Any     							= torch.tensor([0.0771, 0.0011, -0.3625]       )
       elif model_name == "videomae-huge-finetuned-kinetics":
              snake_case			:  str     							= torch.Size([1, 400]       )
              snake_case			:  List[str]     							= torch.tensor([0.2433, 0.1632, -0.4894]       )
       elif model_name == "videomae-base-short-finetuned-kinetics":
              snake_case			:  int     							= torch.Size([1, 400]       )
              snake_case			:  Tuple     							= torch.tensor([0.6588, 0.0990, -0.2493]       )
       elif model_name == "videomae-base-finetuned-kinetics":
              snake_case			:  Optional[Any]     							= torch.Size([1, 400]       )
              snake_case			:  Dict     							= torch.tensor([0.3669, -0.0688, -0.2421]       )
       elif model_name == "videomae-base-short-ssv2":
              snake_case			:  List[Any]     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  Union[str, Any]     							= torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]       )
       elif model_name == "videomae-base-short-finetuned-ssv2":
              snake_case			:  Any     							= torch.Size([1, 174]       )
              snake_case			:  str     							= torch.tensor([-0.0537, -0.1539, -0.3266]       )
       elif model_name == "videomae-base-ssv2":
              snake_case			:  Dict     							= torch.Size([1, 1408, 1536]       )
              snake_case			:  List[Any]     							= torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]       )
       elif model_name == "videomae-base-finetuned-ssv2":
              snake_case			:  List[Any]     							= torch.Size([1, 174]       )
              snake_case			:  Optional[int]     							= torch.tensor([0.1961, -0.8337, -0.6389]       )
       else:
              raise ValueError(f"""Model name not supported. Should be one of {model_names}"""       )

       # verify logits
       assert logits.shape == expected_shape
       if "finetuned" in model_name:
              assert torch.allclose(logits[0, :3]							,   __lowerCamelCase							,   atol=1E-4       )
       else:
              print("Logits:"							,   logits[0, :3, :3]       )
              assert torch.allclose(logits[0, :3, :3]							,   __lowerCamelCase							,   atol=1E-4       )
       print("Logits ok!"       )

       # verify loss, if applicable
       if model_name == "videomae-base-short":
              snake_case			:  List[Any]     							= outputs.loss
              assert torch.allclose(__lowerCamelCase							,   __lowerCamelCase							,   atol=1E-4       )
              print("Loss ok!"       )

       if pytorch_dump_folder_path is not None:
              print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""       )
              image_processor.save_pretrained(__lowerCamelCase       )
              model.save_pretrained(__lowerCamelCase       )

       if push_to_hub:
              print("Pushing to the hub..."       )
              model.push_to_hub(__lowerCamelCase							,   organization="nielsr"       )


if __name__ == "__main__":
   __lowerCamelCase				=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint_url""",
       default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
       type=str,
       help=(
           """URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
           """ download link."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
       type=str,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )

   __lowerCamelCase				=			parser.parse_args()
   convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





