




"""simple docstring"""

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 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase=100	,   _UpperCAmelCase=" "						):




	"""simple docstring"""






	A_	:					Union[str, Any]						 =		text.split(_UpperCAmelCase						)
	return [character.join(text[i : i + n]						).strip() for i in range(0	,   len(_UpperCAmelCase						)	,   _UpperCAmelCase						)]

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					int						 =		[], []
	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 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Tuple						 =		load_dataset(
	    '''csv'''	,   data_files=[csv_path]	,   split='''train'''	,   delimiter='''\t'''	,   column_names=['''title''', '''text''']						)
	A_	:					Union[str, Any]						 =		kb_dataset.map(
	    _UpperCAmelCase	,   batched=_UpperCAmelCase	,   num_proc=1						)  # if you want you can load already splitted csv.
	A_	:					Optional[Any]						 =		[kb_dataset.shard(_UpperCAmelCase	,   _UpperCAmelCase	,   contiguous=_UpperCAmelCase						) for i in range(_UpperCAmelCase						)]
	A_	:					List[Any]						 =		kb_list[process_num]

	A_	:					Dict						 =		'data_' + str(_UpperCAmelCase						)
	A_	:					int						 =		os.path.join(_UpperCAmelCase	,   _UpperCAmelCase						)

	A_	:					Any						 =		DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''						)
	A_	:					List[str]						 =		ctx_encoder.to(device=_UpperCAmelCase						)

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

	A_	:					Union[str, Any]						 =		Features(
	    {'''text''': Value('''string'''						), '''title''': Value('''string'''						), '''embeddings''': Sequence(Value('''float32'''						)						)}						)  # optional, save as float32 instead of float64 to save space

	A_	:					List[str]						 =		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 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					int						 =		[]

	for shard_address in glob(str(_UpperCAmelCase						) + '''/*/'''						):
		data_shard_list.append(load_from_disk(_UpperCAmelCase						)						)

	A_	:					Tuple						 =		concatenate_datasets(_UpperCAmelCase						)
	faiss.omp_set_num_threads(96						)

	A_	:					List[str]						 =		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






'''simple docstring'''





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


UpperCamelCase       :     Optional[Any]			=    logging.get_logger(__name__)

UpperCamelCase       :     Union[str, Any]			=    {
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    int				=			"tvlt"





    def __init__(					self     :						List[str]							,							UpperCAmelCase_     :						Any=2_2_4							,							UpperCAmelCase_     :						Optional[Any]=2_0_4_8							,							UpperCAmelCase_     :						Dict=1_2_8							,							UpperCAmelCase_     :						str=[1_6, 1_6]							,							UpperCAmelCase_     :						int=[1_6, 1_6]							,							UpperCAmelCase_     :						int=3							,							UpperCAmelCase_     :						Optional[int]=1							,							UpperCAmelCase_     :						List[str]=8							,							UpperCAmelCase_     :						List[Any]=7_6_8							,							UpperCAmelCase_     :						int=1_2							,							UpperCAmelCase_     :						List[Any]=1_2							,							UpperCAmelCase_     :						Optional[Any]=3_0_7_2							,							UpperCAmelCase_     :						List[str]="gelu"							,							UpperCAmelCase_     :						Tuple=0.0							,							UpperCAmelCase_     :						Optional[Any]=0.0							,							UpperCAmelCase_     :						Tuple=0.02							,							UpperCAmelCase_     :						Any=1e-6							,							UpperCAmelCase_     :						Dict=True							,							UpperCAmelCase_     :						Union[str, Any]=False							,							UpperCAmelCase_     :						Tuple=1_6							,							UpperCAmelCase_     :						str=5_1_2							,							UpperCAmelCase_     :						List[str]=8							,							UpperCAmelCase_     :						Union[str, Any]=2_0_4_8							,							UpperCAmelCase_     :						Tuple=0.75							,							UpperCAmelCase_     :						Optional[Any]=0.15							,							UpperCAmelCase_     :						Union[str, Any]="frame-level"							,							UpperCAmelCase_     :						List[str]=True							,							UpperCAmelCase_     :						Any=True							,							UpperCAmelCase_     :						Optional[int]="classification"							,							**UpperCAmelCase_     :						Any							,							):


         """simple docstring"""







         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}""")

         a					:							str							= image_size
         a					:							List[Any]							= spectrogram_length
         a					:							Union[str, Any]							= frequency_length
         a					:							str							= image_patch_size
         a					:							Dict							= audio_patch_size
         a					:							Dict							= num_image_channels
         a					:							Tuple							= num_audio_channels
         a					:							Optional[int]							= num_frames

         a					:							str							= hidden_size
         a					:							List[Any]							= num_hidden_layers
         a					:							Optional[Any]							= num_attention_heads
         a					:							Any							= intermediate_size
         a					:							List[Any]							= hidden_act
         a					:							int							= hidden_dropout_prob
         a					:							Optional[Any]							= attention_probs_dropout_prob
         a					:							Optional[Any]							= initializer_range
         a					:							List[str]							= layer_norm_eps
         a					:							List[Any]							= qkv_bias
         a					:							Dict							= use_mean_pooling

         a					:							List[Any]							= decoder_num_attention_heads
         a					:							Any							= decoder_hidden_size
         a					:							int							= decoder_num_hidden_layers
         a					:							Tuple							= decoder_intermediate_size
         a					:							Tuple							= pixel_mask_ratio
         a					:							List[Any]							= audio_mask_ratio
         a					:							Optional[Any]							= audio_mask_type

         a					:							Any							= task_matching
         a					:							Optional[Any]							= task_mae
         a					:							List[str]							= loss_type

