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				(		__SCREAMING_SNAKE_CASE       :					List[str]		,							__SCREAMING_SNAKE_CASE       :					Optional[int]=1_0_0		,							__SCREAMING_SNAKE_CASE       :					str=" "   ):







    '''simple docstring'''
    __snake_case       :				List[Any] 			=    text.split(__SCREAMING_SNAKE_CASE   )
    return [character.join(text[i : i + n]   ).strip() for i in range(0		,							len(__SCREAMING_SNAKE_CASE   )		,							__SCREAMING_SNAKE_CASE   )]







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					str   ):







    '''simple docstring'''
    __snake_case			, __snake_case       :				Dict 			=    [], []
    for title, text in zip(documents["""title"""]		,							documents["""text"""]   ):
        if text is not None:
            for passage in split_text(__SCREAMING_SNAKE_CASE   ):
                titles.append(title if title is not None else """"""   )
                texts.append(__SCREAMING_SNAKE_CASE   )
    return {"title": titles, "text": texts}







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Any		,							__SCREAMING_SNAKE_CASE       :					Optional[int]		,							__SCREAMING_SNAKE_CASE       :					List[Any]		,							__SCREAMING_SNAKE_CASE       :					Union[str, Any]		,							__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					Optional[Any]   ):







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

    __snake_case       :				Union[str, Any] 			=    """data_""" + str(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Tuple 			=    os.path.join(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

    __snake_case       :				List[Any] 			=    DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""   )
    __snake_case       :				int 			=    ctx_encoder.to(device=__SCREAMING_SNAKE_CASE   )

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

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

    __snake_case       :				str 			=    data_shrad.map(
        partial(__SCREAMING_SNAKE_CASE		,							ctx_encoder=__SCREAMING_SNAKE_CASE		,							ctx_tokenizer=__SCREAMING_SNAKE_CASE		,							device=__SCREAMING_SNAKE_CASE   )		,							batched=__SCREAMING_SNAKE_CASE		,							batch_size=1_6		,							features=__SCREAMING_SNAKE_CASE		,							)
    dataset.save_to_disk(__SCREAMING_SNAKE_CASE   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					List[str]		,							__SCREAMING_SNAKE_CASE       :					Union[str, Any]   ):







    '''simple docstring'''
    __snake_case       :				Union[str, Any] 			=    []

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

    __snake_case       :				Tuple 			=    concatenate_datasets(__SCREAMING_SNAKE_CASE   )
    faiss.omp_set_num_threads(9_6   )

    __snake_case       :				int 			=    faiss.IndexHNSWFlat(7_6_8		,							1_2_8		,							faiss.METRIC_INNER_PRODUCT   )
    concat.add_faiss_index("""embeddings"""		,							custom_index=__SCREAMING_SNAKE_CASE   )
    concat.get_index("""embeddings"""   ).save(
        __SCREAMING_SNAKE_CASE   )  # since we load the index in to memory,we can directly update the index in the disk




import warnings

from .generation import FlaxGenerationMixin







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       # warning at import time
       warnings.warn(
           "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
           "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."   ,							__UpperCamelCase   ,							)



