
"""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   __magic_name__     (				__snake_case    :	Union[str, Any]					,			__snake_case    :	Optional[int]=100					,			__snake_case    :	Dict=" "  )			->    Any:
       lowercase   :    Union[str, Any]			=							text.split(A__  )
       return [character.join(text[i : i + n]  ).strip() for i in range(0					,			len(A__  )					,			A__  )]
def   __magic_name__     (				__snake_case    :	List[Any]  )			->    Union[str, Any]:
       lowercase   :    List[str]			=							[], []
       for title, text in zip(documents["title"]					,			documents["text"]  ):
              if text is not None:
                     for passage in split_text(A__  ):
                            titles.append(title if title is not None else ""  )
                            texts.append(A__  )
       return {"title": titles, "text": texts}
def   __magic_name__     (				__snake_case    :	Union[str, Any]					,			__snake_case    :	List[Any]					,			__snake_case    :	Optional[int]					,			__snake_case    :	Optional[int]					,			__snake_case    :	Any					,			__snake_case    :	Optional[int]  )			->    Tuple:
       lowercase   :    Optional[Any]			=							load_dataset(
           "csv"					,			data_files=[csv_path]					,			split="train"					,			delimiter="\t"					,			column_names=["title", "text"]  )
       lowercase   :    str			=							kb_dataset.map(
           A__					,			batched=A__					,			num_proc=1  )  # if you want you can load already splitted csv.
       lowercase   :    Dict			=							[kb_dataset.shard(A__					,			A__					,			contiguous=A__  ) for i in range(A__  )]
       lowercase   :    List[str]			=							kb_list[process_num]

       lowercase   :    List[str]			=							'data_' + str(A__  )
       lowercase   :    Optional[Any]			=							os.path.join(A__					,			A__  )

       lowercase   :    int			=							DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"  )
       lowercase   :    Any			=							ctx_encoder.to(device=A__  )

       def embed(
           __snake_case    :	Any					,			__snake_case    :	str					,			__snake_case    :	Dict					,			__snake_case    :	Optional[Any]  ) -> dict:
              lowercase   :    Optional[int]			=							ctx_tokenizer(
                  documents["title"]					,			documents["text"]					,			truncation=A__					,			padding="longest"					,			return_tensors="pt"  )['input_ids']
              lowercase   :    str			=							ctx_encoder(input_ids.to(device=A__  )					,			return_dict=A__  ).pooler_output
              return {"embeddings": embeddings.detach().cpu().numpy()}

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

       lowercase   :    Optional[Any]			=							data_shrad.map(
           partial(A__					,			ctx_encoder=A__					,			ctx_tokenizer=A__					,			device=A__  )					,			batched=A__					,			batch_size=16					,			features=A__					,			)
       dataset.save_to_disk(A__  )
def   __magic_name__     (				__snake_case    :	List[Any]					,			__snake_case    :	str  )			->    Optional[int]:
       lowercase   :    int			=							[]

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

       lowercase   :    Union[str, Any]			=							concatenate_datasets(A__  )
       faiss.omp_set_num_threads(96  )

       lowercase   :    str			=							faiss.IndexHNSWFlat(768					,			128					,			faiss.METRIC_INNER_PRODUCT  )
       concat.add_faiss_index("embeddings"					,			custom_index=A__  )
       concat.get_index("embeddings"  ).save(
           A__  )  # 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


_A      :	str       		=	logging.get_logger(__name__)

_A      :	Optional[int]       		=	{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """tvlt"""
   def __init__(      self     ,   _a=224     ,   _a=2_048     ,   _a=128     ,   _a=[16, 16]     ,   _a=[16, 16]     ,   _a=3     ,   _a=1     ,   _a=8     ,   _a=768     ,   _a=12     ,   _a=12     ,   _a=3_072     ,   _a="gelu"     ,   _a=0.0     ,   _a=0.0     ,   _a=0.0_2     ,   _a=1E-6     ,   _a=True     ,   _a=False     ,   _a=16     ,   _a=512     ,   _a=8     ,   _a=2_048     ,   _a=0.7_5     ,   _a=0.1_5     ,   _a="frame-level"     ,   _a=True     ,   _a=True     ,   _a="classification"     ,   **_a     ,   ):
          super().__init__(**_a    )

          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}"""    )

          lowercase   :    Union[str, Any]			=							image_size
          lowercase   :    Dict			=							spectrogram_length
          lowercase   :    Union[str, Any]			=							frequency_length
          lowercase   :    Union[str, Any]			=							image_patch_size
          lowercase   :    Any			=							audio_patch_size
          lowercase   :    int			=							num_image_channels
          lowercase   :    Union[str, Any]			=							num_audio_channels
          lowercase   :    Any			=							num_frames

          lowercase   :    List[str]			=							hidden_size
          lowercase   :    List[str]			=							num_hidden_layers
          lowercase   :    List[Any]			=							num_attention_heads
          lowercase   :    int			=							intermediate_size
          lowercase   :    Tuple			=							hidden_act
          lowercase   :    int			=							hidden_dropout_prob
          lowercase   :    Dict			=							attention_probs_dropout_prob
          lowercase   :    List[Any]			=							initializer_range
          lowercase   :    Union[str, Any]			=							layer_norm_eps
          lowercase   :    Dict			=							qkv_bias
          lowercase   :    List[Any]			=							use_mean_pooling

          lowercase   :    List[Any]			=							decoder_num_attention_heads
          lowercase   :    Union[str, Any]			=							decoder_hidden_size
          lowercase   :    Dict			=							decoder_num_hidden_layers
          lowercase   :    Union[str, Any]			=							decoder_intermediate_size
          lowercase   :    List[str]			=							pixel_mask_ratio
          lowercase   :    str			=							audio_mask_ratio
          lowercase   :    Any			=							audio_mask_type

          lowercase   :    Union[str, Any]			=							task_matching
          lowercase   :    Optional[Any]			=							task_mae
          lowercase   :    List[str]			=							loss_type


