'''simple docstring'''

import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


_lowerCamelCase :				Union[str, Any]														= """block_records.npy"""


_lowerCamelCase :				Dict														= logging.get_logger(__name__)



def  _lowerCAmelCase				(				__a			,      __a      )  ->      np.ndarray:
     '''simple docstring'''







     import tensorflow.compat.va as tf

     _UpperCamelCase  :Union[str, Any]       =tf.data.TFRecordDataset(__a			,      buffer_size=5_12 * 10_24 * 10_24      )
     _UpperCamelCase  :List[str]       =blocks_dataset.batch(__a			,      drop_remainder=__a      )
     _UpperCamelCase  :List[str]       =next(blocks_dataset.take(1      ).as_numpy_iterator()      )

     return np_record




class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__=2					,      lowerCAmelCase__=1_000					,      lowerCAmelCase__=100					,      lowerCAmelCase__=100_000					,      )				->     str:
        """simple docstring"""





        from scann.scann_ops.py.scann_ops_pybind import builder as Builder

        _UpperCamelCase  :List[str]       =Builder(db=lowerCAmelCase__					,      num_neighbors=lowerCAmelCase__					,      distance_measure="""dot_product"""     )
        _UpperCamelCase  :int       =builder.tree(
            num_leaves=lowerCAmelCase__					,      num_leaves_to_search=lowerCAmelCase__					,      training_sample_size=lowerCAmelCase__     )
        _UpperCamelCase  :List[Any]       =builder.score_ah(dimensions_per_block=lowerCAmelCase__     )

        _UpperCamelCase  :List[Any]       =builder.build()




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =self.searcher.search_batched(question_projection.detach().cpu()     )
        return retrieved_block_ids.astype("""int64"""     )




class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        super().__init__()
        _UpperCamelCase  :Tuple       =block_records
        _UpperCamelCase  :List[Any]       =tokenizer



   def __call__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__="pt"     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =np.take(self.block_records					,      indices=lowerCAmelCase__					,      axis=0     )

        _UpperCamelCase  :List[Any]       =self.tokenizer.decode(question_input_ids[0]					,      skip_special_tokens=lowerCAmelCase__     )

        _UpperCamelCase  :Any       =[]
        _UpperCamelCase  :int       =[]
        for retrieved_block in retrieved_blocks:
             text.append(lowerCAmelCase__     )
             text_pair.append(retrieved_block.decode()     )

        _UpperCamelCase  :Union[str, Any]       =self.tokenizer(
            lowerCAmelCase__					,      lowerCAmelCase__					,      padding=lowerCAmelCase__					,      truncation=lowerCAmelCase__					,      return_special_tokens_mask=lowerCAmelCase__					,      max_length=lowerCAmelCase__     )
        _UpperCamelCase  :List[str]       =concat_inputs.convert_to_tensors(lowerCAmelCase__     )

        if answer_ids is not None:
             return self.block_has_answer(lowerCAmelCase__					,      lowerCAmelCase__     ) + (concat_inputs_tensors,)
        else:
             return (None, None, None, concat_inputs_tensors)



   @classmethod
   def  _UpperCamelCase		(    cls					,      lowerCAmelCase__					,      *lowerCAmelCase__					,      **lowerCAmelCase__     )				->     Union[str, Any]:
        """simple docstring"""





        if os.path.isdir(lowerCAmelCase__     ):
             _UpperCamelCase  :int       =os.path.join(lowerCAmelCase__					,      _REALM_BLOCK_RECORDS_FILENAME     )
        else:
             _UpperCamelCase  :Optional[Any]       =hf_hub_download(
                 repo_id=lowerCAmelCase__					,      filename=_REALM_BLOCK_RECORDS_FILENAME					,      **lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =np.load(lowerCAmelCase__					,      allow_pickle=lowerCAmelCase__     )

        _UpperCamelCase  :Any       =AutoTokenizer.from_pretrained(lowerCAmelCase__					,      *lowerCAmelCase__					,      **lowerCAmelCase__     )

        return cls(lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Dict:
        """simple docstring"""





        np.save(os.path.join(lowerCAmelCase__					,      _REALM_BLOCK_RECORDS_FILENAME     )					,      self.block_records     )
        # save tokenizer
        self.tokenizer.save_pretrained(lowerCAmelCase__     )




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :Any       =[]
        _UpperCamelCase  :Union[str, Any]       =[]
        _UpperCamelCase  :Dict       =[]
        _UpperCamelCase  :Union[str, Any]       =0

        for input_id in concat_inputs.input_ids:
             _UpperCamelCase  :Optional[int]       =input_id.tolist()
             # Check answers between two [SEP] tokens
             _UpperCamelCase  :Optional[int]       =input_id_list.index(self.tokenizer.sep_token_id     )
             _UpperCamelCase  :Tuple       =first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id     )

             start_pos.append([]     )
             end_pos.append([]     )
             for answer in answer_ids:
                  for idx in range(first_sep_idx + 1					,      lowerCAmelCase__     ):
                       if answer[0] == input_id_list[idx]:
                            if input_id_list[idx : idx + len(lowerCAmelCase__     )] == answer:
                                 start_pos[-1].append(lowerCAmelCase__     )
                                 end_pos[-1].append(idx + len(lowerCAmelCase__     ) - 1     )

             if len(start_pos[-1]     ) == 0:
                  has_answers.append(lowerCAmelCase__     )
             else:
                  has_answers.append(lowerCAmelCase__     )
                  if len(start_pos[-1]     ) > max_answers:
                       _UpperCamelCase  :str       =len(start_pos[-1]     )

        # Pad -1 to max_answers
        for start_pos_, end_pos_ in zip(lowerCAmelCase__					,      lowerCAmelCase__     ):
             if len(start_pos_     ) < max_answers:
                  _UpperCamelCase  :Tuple       =[-1] * (max_answers - len(start_pos_     ))
                  start_pos_ += padded
                  end_pos_ += padded
        return has_answers, start_pos, end_pos


from hashlib import mda, shaaaa

A__							=				(shaaaa, mda)

class 					__UpperCamelCase					:







	def __init__( self:			Optional[int]	,			__UpperCamelCase:			int = 8   ):

						'''simple docstring'''





						__magic_name__				=     0b0
						__magic_name__				=     size







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.hash_(__UpperCamelCase   )
						self.bitarray |= h







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.hash_(__UpperCamelCase   )
						return (h & self.bitarray) == h







	def __contains__( self:			Optional[int]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						return self.exists(__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     bin(__UpperCamelCase   )[2:]
						return res.zfill(self.size   )







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						return self.format_bin(self.bitarray   )







	def 						_SCREAMING_SNAKE_CASE				( self:			str	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     0b0
						for func in HASH_FUNCTIONS:
											__magic_name__				=     (
											    int.from_bytes(func(value.encode()   ).digest()	,			'little'   ) % self.size
											)
											res |= 2**position
						return res







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						return self.format_bin(self.hash_(__UpperCamelCase   )   )







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     bin(self.bitarray   ).count('1'   )
						return (n_ones / self.size) ** len(__UpperCamelCase   )




