'''simple docstring'''





import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


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



class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def __init__(					self     :						str):


         """simple docstring"""







         super().__init__()
         if version.parse(torch.__version__) < version.parse('1.12.0'):
              raise ImportError(
                  f"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
                  'PytorchGELUTanh. Please upgrade torch.')







    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return nn.functional.gelu(UpperCAmelCase_							,							approximate='tanh')






class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.04_47_15 * torch.pow(UpperCAmelCase_							,							3.0))))






class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def __init__(					self     :						List[str]							,							UpperCAmelCase_     :						bool = False):


         """simple docstring"""







         super().__init__()
         if use_gelu_python:
              a					:							List[Any]							= self._gelu_python
         else:
              a					:							int							= nn.functional.gelu





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0)))







    def    SCREAMING_SNAKE_CASE_     (					self     :						int							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return self.act(UpperCAmelCase_)






class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return 0.5 * input * (1.0 + torch.tanh(input * 0.79_78_84_56_08 * (1.0 + 0.04_47_15 * input * input)))






class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return input * torch.sigmoid(1.7_02 * input)






class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def __init__(					self     :						Optional[Any]							,							UpperCAmelCase_     :						float							,							UpperCAmelCase_     :						float):


         """simple docstring"""







         if min > max:
              raise ValueError(f"""min should be < max (got min: {min}, max: {max})""")

         super().__init__()
         a					:							Tuple							= min
         a					:							Optional[Any]							= max







    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return torch.clip(gelu(UpperCAmelCase_)							,							self.min							,							self.max)






class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def __init__(					self     :						Dict):


         """simple docstring"""







         super().__init__()
         a					:							int							= math.sqrt(2 / math.pi)







    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_47_15 * torch.pow(UpperCAmelCase_							,							3))))






class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return nn.functional.silu(UpperCAmelCase_)






class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def __init__(					self     :						int):


         """simple docstring"""







         super().__init__()
         if version.parse(torch.__version__) < version.parse('1.9.0'):
              a					:							Any							= self._mish_python
         else:
              a					:							Optional[Any]							= nn.functional.mish





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return input * torch.tanh(nn.functional.softplus(UpperCAmelCase_))







    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return self.act(UpperCAmelCase_)






class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]							,							UpperCAmelCase_     :						Tensor):


         """simple docstring"""







         return input






class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]							,							UpperCAmelCase_     :						List[str]							,							UpperCAmelCase_     :						Any=0.70_71_07							,							UpperCAmelCase_     :						Optional[int]=0.28_20_95):


         """simple docstring"""







         a					:							Optional[int]							= (input - mu).div(sigma * math.sqrt(2.0))
         return 0.5 * (1.0 + torch.erf(UpperCAmelCase_))






class   UpperCamelCase					(       nn.Module       ):







    """simple docstring"""





    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]							,							UpperCAmelCase_     :						Union[str, Any]):


         """simple docstring"""







         a					:							str							= nn.functional.relu(UpperCAmelCase_)
         a					:							Union[str, Any]							= torch.square(UpperCAmelCase_)
         return squared






class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    def __getitem__(					self     :						List[str]							,							UpperCAmelCase_     :						List[str]):


         """simple docstring"""







         a					:							List[Any]							= super().__getitem__(UpperCAmelCase_)
         a							,			a					:							Union[str, Any]							= content if isinstance(UpperCAmelCase_							,							UpperCAmelCase_) else (content, {})
         return cls(**UpperCAmelCase_)






UpperCamelCase       :     Union[str, Any]			=    {
    """gelu""": GELUActivation,
    """gelu_10""": (ClippedGELUActivation, {"""min""": -10, """max""": 10}),
    """gelu_fast""": FastGELUActivation,
    """gelu_new""": NewGELUActivation,
    """gelu_python""": (GELUActivation, {"""use_gelu_python""": True}),
    """gelu_pytorch_tanh""": PytorchGELUTanh,
    """gelu_accurate""": AccurateGELUActivation,
    """laplace""": LaplaceActivation,
    """linear""": LinearActivation,
    """mish""": MishActivation,
    """quick_gelu""": QuickGELUActivation,
    """relu""": nn.ReLU,
    """relu2""": ReLUSquaredActivation,
    """relu6""": nn.ReLUa,
    """sigmoid""": nn.Sigmoid,
    """silu""": SiLUActivation,
    """swish""": SiLUActivation,
    """tanh""": nn.Tanh,
}
UpperCamelCase       :     Optional[int]			=    ClassInstantier(ACTaCLS)






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any  )   ->				Tuple:







     """simple docstring"""
     if activation_string in ACTaFN:
          return ACTaFN[activation_string]
     else:
          raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()  )}"""  )


# For backwards compatibility with: from activations import gelu_python
UpperCamelCase       :     str			=    get_activation("""gelu_python""")
UpperCamelCase       :     Tuple			=    get_activation("""gelu_new""")
UpperCamelCase       :     List[Any]			=    get_activation("""gelu""")
UpperCamelCase       :     str			=    get_activation("""gelu_fast""")
UpperCamelCase       :     Optional[Any]			=    get_activation("""quick_gelu""")
UpperCamelCase       :     Union[str, Any]			=    get_activation("""silu""")
UpperCamelCase       :     str			=    get_activation("""mish""")





UpperCamelCase       :     Tuple			=    get_activation("""linear""")


'''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 SCREAMING_SNAKE_CASE__  (				snake_case       :				List[Any]				,	snake_case       :				List[str]=100				,	snake_case       :				Any=" "  )   ->				Any:







     """simple docstring"""
     a					:							Union[str, Any]							= text.split(snake_case  )
     return [character.join(text[i : i + n]  ).strip() for i in range(0				,	len(snake_case  )				,	snake_case  )]






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Optional[int]  )   ->				Union[str, Any]:







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






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int				,	snake_case       :				str				,	snake_case       :				List[Any]				,	snake_case       :				Dict				,	snake_case       :				Union[str, Any]				,	snake_case       :				int  )   ->				Dict:







     """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(
         snake_case				,	batched=snake_case				,	num_proc=1  )  # if you want you can load already splitted csv.
     a					:							Optional[Any]							= [kb_dataset.shard(snake_case				,	snake_case				,	contiguous=snake_case  ) for i in range(snake_case  )]
     a					:							List[Any]							= kb_list[process_num]

     a					:							Dict							= 'data_' + str(snake_case  )
     a					:							int							= os.path.join(snake_case				,	snake_case  )

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

     def embed(
         snake_case       :				dict				,	snake_case       :				DPRContextEncoder				,	snake_case       :				DPRContextEncoderTokenizerFast				,	snake_case       :				Any  ) -> dict:
          a					:							Tuple							= ctx_tokenizer(
              documents['title']				,	documents['text']				,	truncation=snake_case				,	padding='longest'				,	return_tensors='pt'  )['input_ids']
          a					:							Any							= ctx_encoder(input_ids.to(device=snake_case  )				,	return_dict=snake_case  ).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(snake_case				,	ctx_encoder=snake_case				,	ctx_tokenizer=snake_case				,	device=snake_case  )				,	batched=snake_case				,	batch_size=16				,	features=snake_case				,	)
     dataset.save_to_disk(snake_case  )






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any				,	snake_case       :				Any  )   ->				List[Any]:







     """simple docstring"""
     a					:							int							= []

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

     a					:							Tuple							= concatenate_datasets(snake_case  )
     faiss.omp_set_num_threads(96  )

     a					:							List[str]							= faiss.IndexHNSWFlat(768				,	128				,	faiss.METRIC_INNER_PRODUCT  )
     concat.add_faiss_index('embeddings'				,	custom_index=snake_case  )
     concat.get_index('embeddings'  ).save(
         snake_case  )  # since we load the index in to memory,we can directly update the index in the disk

