



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  __a  (					lowerCAmelCase_     :      List[str]    ,lowerCAmelCase_     :      Union[str, Any]=1_00    ,lowerCAmelCase_     :      Union[str, Any]=" "       )   ->				Tuple:






						'''simple docstring'''



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



def  __a  (					lowerCAmelCase_     :      str       )   ->				Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_,     UpperCAmelCase_=     [], []
						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  __a  (					lowerCAmelCase_     :      Optional[int]    ,lowerCAmelCase_     :      List[Any]    ,lowerCAmelCase_     :      Dict    ,lowerCAmelCase_     :      Dict    ,lowerCAmelCase_     :      Optional[Any]    ,lowerCAmelCase_     :      Union[str, Any]       )   ->				Dict:






						'''simple docstring'''



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

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

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

						def embed(
						    lowerCAmelCase_     :      dict    ,lowerCAmelCase_     :      DPRContextEncoder    ,lowerCAmelCase_     :      DPRContextEncoderTokenizerFast    ,lowerCAmelCase_     :      Any       ) -> dict:
												UpperCAmelCase_=     ctx_tokenizer(
												    documents["""title"""]    ,documents["""text"""]    ,truncation=lowerCAmelCase_    ,padding="""longest"""    ,return_tensors="""pt"""       )["""input_ids"""]
												UpperCAmelCase_=     ctx_encoder(input_ids.to(device=lowerCAmelCase_       )    ,return_dict=lowerCAmelCase_       ).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(lowerCAmelCase_    ,ctx_encoder=lowerCAmelCase_    ,ctx_tokenizer=lowerCAmelCase_    ,device=lowerCAmelCase_       )    ,batched=lowerCAmelCase_    ,batch_size=16    ,features=lowerCAmelCase_    ,)
						dataset.save_to_disk(lowerCAmelCase_       )



def  __a  (					lowerCAmelCase_     :      int    ,lowerCAmelCase_     :      List[Any]       )   ->				Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=     []

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

						UpperCAmelCase_=     concatenate_datasets(lowerCAmelCase_       )
						faiss.omp_set_num_threads(96       )

						UpperCAmelCase_=     faiss.IndexHNSWFlat(7_68    ,1_28    ,faiss.METRIC_INNER_PRODUCT       )
						concat.add_faiss_index("""embeddings"""    ,custom_index=lowerCAmelCase_       )
						concat.get_index("""embeddings"""       ).save(
						    lowerCAmelCase_       )  # 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


__A       					=		logging.get_logger(__name__)

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






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Tuple	     =							"tvlt"




	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		List[str]=224		,						__UpperCAmelCase			:		List[str]=2_048		,						__UpperCAmelCase			:		Tuple=128		,						__UpperCAmelCase			:		Union[str, Any]=[16, 16]		,						__UpperCAmelCase			:		str=[16, 16]		,						__UpperCAmelCase			:		str=3		,						__UpperCAmelCase			:		List[str]=1		,						__UpperCAmelCase			:		Tuple=8		,						__UpperCAmelCase			:		Optional[Any]=768		,						__UpperCAmelCase			:		List[Any]=12		,						__UpperCAmelCase			:		Tuple=12		,						__UpperCAmelCase			:		List[str]=3_072		,						__UpperCAmelCase			:		List[Any]="gelu"		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		List[Any]=0.02		,						__UpperCAmelCase			:		Optional[int]=1E-6		,						__UpperCAmelCase			:		Tuple=True		,						__UpperCAmelCase			:		List[Any]=False		,						__UpperCAmelCase			:		Union[str, Any]=16		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Optional[Any]=8		,						__UpperCAmelCase			:		Dict=2_048		,						__UpperCAmelCase			:		Dict=0.75		,						__UpperCAmelCase			:		Union[str, Any]=0.15		,						__UpperCAmelCase			:		Any="frame-level"		,						__UpperCAmelCase			:		int=True		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Union[str, Any]="classification"		,						**__UpperCAmelCase			:		Any		,						)    ->				Tuple:
							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}"""  )

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

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

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

							UpperCAmelCase_=     task_matching
							UpperCAmelCase_=     task_mae
							UpperCAmelCase_=     loss_type




