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_ (				__lowerCAmelCase		,					__lowerCAmelCase=1_00		,					__lowerCAmelCase=" "						)->  List[Any]:






      '''simple docstring'''





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





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  Any:






      '''simple docstring'''





      UpperCAmelCase  ,						UpperCAmelCase    : List[str]      =[], []
      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   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)->  Union[str, Any]:






      '''simple docstring'''





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

      UpperCAmelCase    : List[str]      ='''data_''' + str(__lowerCAmelCase						)
      UpperCAmelCase    : Tuple      =os.path.join(__lowerCAmelCase		,					__lowerCAmelCase						)

      UpperCAmelCase    : int      =DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''						)
      UpperCAmelCase    : Any      =ctx_encoder.to(device=__lowerCAmelCase						)

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

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

      UpperCAmelCase    : List[Any]      =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   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  Optional[Any]:






      '''simple docstring'''





      UpperCAmelCase    : Optional[Any]      =[]

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

      UpperCAmelCase    : int      =concatenate_datasets(__lowerCAmelCase						)
      faiss.omp_set_num_threads(96						)

      UpperCAmelCase    : List[Any]      =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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case			      =    {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''EsmForMaskedLM''',
         '''EsmForSequenceClassification''',
         '''EsmForTokenClassification''',
         '''EsmModel''',
         '''EsmPreTrainedModel''',
     ]
     __snake_case			      =    ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFEsmForMaskedLM''',
         '''TFEsmForSequenceClassification''',
         '''TFEsmForTokenClassification''',
         '''TFEsmModel''',
         '''TFEsmPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
     from .tokenization_esm import EsmTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_esm import (
              ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              EsmForMaskedLM,
              EsmForSequenceClassification,
              EsmForTokenClassification,
              EsmModel,
              EsmPreTrainedModel,
          )
          from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_esm import (
              TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
              TFEsmModel,
              TFEsmPreTrainedModel,
          )


else:
     import sys

     __snake_case			      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
