





import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_=100       ,		UpperCamelCase_=" "	)  ->		int:
       UpperCamelCase_ 							=	text.split(UpperCamelCase_	)
       return [character.join(text[i : i + n]	).strip() for i in range(0       ,		len(UpperCamelCase_	)       ,		UpperCamelCase_	)]




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Optional[Any]:
       UpperCamelCase_							,			UpperCamelCase_ 							=	[], []
       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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       UpperCamelCase_ 							=	load_dataset(
           "csv"       ,		data_files=[csv_path]       ,		split="train"       ,		delimiter="\t"       ,		column_names=["title", "text"]	)
       UpperCamelCase_ 							=	kb_dataset.map(
           UpperCamelCase_       ,		batched=UpperCamelCase_       ,		num_proc=1	)  # if you want you can load already splitted csv.
       UpperCamelCase_ 							=	[kb_dataset.shard(UpperCamelCase_       ,		UpperCamelCase_       ,		contiguous=UpperCamelCase_	) for i in range(UpperCamelCase_	)]
       UpperCamelCase_ 							=	kb_list[process_num]

       UpperCamelCase_ 							=	"data_" + str(UpperCamelCase_	)
       UpperCamelCase_ 							=	os.path.join(UpperCamelCase_       ,		UpperCamelCase_	)

       UpperCamelCase_ 							=	DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"	)
       UpperCamelCase_ 							=	ctx_encoder.to(device=UpperCamelCase_	)

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

       UpperCamelCase_ 							=	Features(
           {"text": Value("string"	), "title": Value("string"	), "embeddings": Sequence(Value("float32"	)	)}	)  # optional, save as float32 instead of float64 to save space

       UpperCamelCase_ 							=	data_shrad.map(
           partial(UpperCamelCase_       ,		ctx_encoder=UpperCamelCase_       ,		ctx_tokenizer=UpperCamelCase_       ,		device=UpperCamelCase_	)       ,		batched=UpperCamelCase_       ,		batch_size=16       ,		features=UpperCamelCase_       ,		)
       dataset.save_to_disk(UpperCamelCase_	)







def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		int:
       UpperCamelCase_ 							=	[]

       for shard_address in glob(str(UpperCamelCase_	) + "/*/"	):
              data_shard_list.append(load_from_disk(UpperCamelCase_	)	)

       UpperCamelCase_ 							=	concatenate_datasets(UpperCamelCase_	)
       faiss.omp_set_num_threads(96	)

       UpperCamelCase_ 							=	faiss.IndexHNSWFlat(768       ,		128       ,		faiss.METRIC_INNER_PRODUCT	)
       concat.add_faiss_index("embeddings"       ,		custom_index=UpperCamelCase_	)
       concat.get_index("embeddings"	).save(
           UpperCamelCase_	)  # since we load the index in to memory,we can directly update the index in the disk









from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase					:						Any         =     logging.get_logger(__name__)

_UpperCAmelCase					:						Dict         =     {
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}


class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    Any 	=     '''tvlt'''





     def __init__(	self							, _UpperCAmelCase=224							, _UpperCAmelCase=2048							, _UpperCAmelCase=128							, _UpperCAmelCase=[16, 16]							, _UpperCAmelCase=[16, 16]							, _UpperCAmelCase=3							, _UpperCAmelCase=1							, _UpperCAmelCase=8							, _UpperCAmelCase=768							, _UpperCAmelCase=12							, _UpperCAmelCase=12							, _UpperCAmelCase=3072							, _UpperCAmelCase="gelu"							, _UpperCAmelCase=0.0							, _UpperCAmelCase=0.0							, _UpperCAmelCase=0.02							, _UpperCAmelCase=1E-6							, _UpperCAmelCase=True							, _UpperCAmelCase=False							, _UpperCAmelCase=16							, _UpperCAmelCase=512							, _UpperCAmelCase=8							, _UpperCAmelCase=2048							, _UpperCAmelCase=0.75							, _UpperCAmelCase=0.15							, _UpperCAmelCase="frame-level"							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase="classification"							, **_UpperCAmelCase							, )		->				List[Any]:
        super().__init__(**_UpperCAmelCase  )

        if audio_mask_type not in ("frame-level", "patch_level"):
           raise ValueError(
               """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
               F'''got {audio_mask_type}'''  )

        lowerCamelCase									=image_size
        lowerCamelCase									=spectrogram_length
        lowerCamelCase									=frequency_length
        lowerCamelCase									=image_patch_size
        lowerCamelCase									=audio_patch_size
        lowerCamelCase									=num_image_channels
        lowerCamelCase									=num_audio_channels
        lowerCamelCase									=num_frames

        lowerCamelCase									=hidden_size
        lowerCamelCase									=num_hidden_layers
        lowerCamelCase									=num_attention_heads
        lowerCamelCase									=intermediate_size
        lowerCamelCase									=hidden_act
        lowerCamelCase									=hidden_dropout_prob
        lowerCamelCase									=attention_probs_dropout_prob
        lowerCamelCase									=initializer_range
        lowerCamelCase									=layer_norm_eps
        lowerCamelCase									=qkv_bias
        lowerCamelCase									=use_mean_pooling

        lowerCamelCase									=decoder_num_attention_heads
        lowerCamelCase									=decoder_hidden_size
        lowerCamelCase									=decoder_num_hidden_layers
        lowerCamelCase									=decoder_intermediate_size
        lowerCamelCase									=pixel_mask_ratio
        lowerCamelCase									=audio_mask_ratio
        lowerCamelCase									=audio_mask_type

        lowerCamelCase									=task_matching
        lowerCamelCase									=task_mae
        lowerCamelCase									=loss_type