




'''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 				lowerCamelCase_   (   __UpperCamelCase				:					Union[str, Any]       ,						__UpperCamelCase				:					Union[str, Any]=1_0_0       ,						__UpperCamelCase				:					Optional[Any]=" "		)				->  int:







     """simple docstring"""
     _A				=       text.split(__UpperCamelCase		)
     return [character.join(text[i : i + n]		).strip() for i in range(0       ,						len(__UpperCamelCase		)       ,						__UpperCamelCase		)]

def 				lowerCamelCase_   (   __UpperCamelCase				:					Any		)				->  str:







     """simple docstring"""
     _A		,							_A				=       [], []
     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 				lowerCamelCase_   (   __UpperCamelCase				:					str       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					int       ,						__UpperCamelCase				:					List[Any]		)				->  Optional[int]:







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

     _A				=       'data_' + str(__UpperCamelCase		)
     _A				=       os.path.join(__UpperCamelCase       ,						__UpperCamelCase		)

     _A				=       DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'		)
     _A				=       ctx_encoder.to(device=__UpperCamelCase		)

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

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

     _A				=       data_shrad.map(
         partial(__UpperCamelCase       ,						ctx_encoder=__UpperCamelCase       ,						ctx_tokenizer=__UpperCamelCase       ,						device=__UpperCamelCase		)       ,						batched=__UpperCamelCase       ,						batch_size=1_6       ,						features=__UpperCamelCase       ,						)
     dataset.save_to_disk(__UpperCamelCase		)

def 				lowerCamelCase_   (   __UpperCamelCase				:					Any       ,						__UpperCamelCase				:					str		)				->  Optional[int]:







     """simple docstring"""
     _A				=       []

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

     _A				=       concatenate_datasets(__UpperCamelCase		)
     faiss.omp_set_num_threads(9_6		)

     _A				=       faiss.IndexHNSWFlat(7_6_8       ,						1_2_8       ,						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'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase  =				{
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase  =				[
        """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvNextV2ForImageClassification""",
        """ConvNextV2Model""",
        """ConvNextV2PreTrainedModel""",
        """ConvNextV2Backbone""",
    ]


if TYPE_CHECKING:
    from .configuration_convnextva import (
        CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ConvNextVaConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnextva import (
            CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextVaBackbone,
            ConvNextVaForImageClassification,
            ConvNextVaModel,
            ConvNextVaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase  =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)





