





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


__UpperCAmelCase						=      'block_records.npy'


__UpperCAmelCase						=      logging.get_logger(__name__)
def 			__UpperCamelCase				(			lowercase__	:       str		,    lowercase__	:       int		)	->      np.ndarray:
    '''simple docstring'''





    import tensorflow.compat.va as tf

    lowerCAmelCase_					:	int						=   tf.data.TFRecordDataset(lowercase__		,    buffer_size=512 * 1024 * 1024		)
    lowerCAmelCase_					:	Optional[int]						=   blocks_dataset.batch(lowercase__		,    drop_remainder=lowercase__		)
    lowerCAmelCase_					:	Optional[int]						=   next(blocks_dataset.take(1		).as_numpy_iterator()		)

    return np_record

class 		__a							:



       def __init__(     self	:   Any					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Any					,			UpperCAmelCase	:   Union[str, Any]=2					,			UpperCAmelCase	:   Any=10_00					,			UpperCAmelCase	:   str=1_00					,			UpperCAmelCase	:   Optional[int]=10_00_00					,			):
           from scann.scann_ops.py.scann_ops_pybind import builder as Builder

           lowerCAmelCase_					:	Union[str, Any]						=   Builder(db=UpperCAmelCase					,			num_neighbors=UpperCAmelCase					,			distance_measure="""dot_product"""			)
           lowerCAmelCase_					:	Any						=   builder.tree(
               num_leaves=UpperCAmelCase					,			num_leaves_to_search=UpperCAmelCase					,			training_sample_size=UpperCAmelCase			)
           lowerCAmelCase_					:	Dict						=   builder.score_ah(dimensions_per_block=UpperCAmelCase			)

           lowerCAmelCase_					:	str						=   builder.build()



       def  A							(     self	:   int					,			UpperCAmelCase	:   Optional[int]			):
           lowerCAmelCase_					:	Any						=   self.searcher.search_batched(question_projection.detach().cpu()			)
           return retrieved_block_ids.astype("""int64"""			)






class 		__a							:



       def __init__(     self	:   Union[str, Any]					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   Union[str, Any]			):
           super().__init__()
           lowerCAmelCase_					:	Optional[int]						=   block_records
           lowerCAmelCase_					:	Union[str, Any]						=   tokenizer



       def __call__(     self	:   str					,			UpperCAmelCase	:   Optional[Any]					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   Union[str, Any]=None					,			UpperCAmelCase	:   Union[str, Any]="pt"			):
           lowerCAmelCase_					:	List[Any]						=   np.take(self.block_records					,			indices=UpperCAmelCase					,			axis=0			)

           lowerCAmelCase_					:	int						=   self.tokenizer.decode(question_input_ids[0]					,			skip_special_tokens=UpperCAmelCase			)

           lowerCAmelCase_					:	List[str]						=   []
           lowerCAmelCase_					:	int						=   []
           for retrieved_block in retrieved_blocks:
               text.append(UpperCAmelCase			)
               text_pair.append(retrieved_block.decode()			)

           lowerCAmelCase_					:	str						=   self.tokenizer(
               UpperCAmelCase					,			UpperCAmelCase					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase					,			return_special_tokens_mask=UpperCAmelCase					,			max_length=UpperCAmelCase			)
           lowerCAmelCase_					:	Union[str, Any]						=   concat_inputs.convert_to_tensors(UpperCAmelCase			)

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



       @classmethod
       def  A							(     cls	:   Union[str, Any]					,			UpperCAmelCase	:   Optional[Union[str, os.PathLike]]					,			*UpperCAmelCase	:   str					,			**UpperCAmelCase	:   str			):
           if os.path.isdir(UpperCAmelCase			):
               lowerCAmelCase_					:	Tuple						=   os.path.join(UpperCAmelCase					,			_REALM_BLOCK_RECORDS_FILENAME			)
           else:
               lowerCAmelCase_					:	Dict						=   hf_hub_download(
                   repo_id=UpperCAmelCase					,			filename=_REALM_BLOCK_RECORDS_FILENAME					,			**UpperCAmelCase			)
           lowerCAmelCase_					:	Tuple						=   np.load(UpperCAmelCase					,			allow_pickle=UpperCAmelCase			)

           lowerCAmelCase_					:	Optional[int]						=   AutoTokenizer.from_pretrained(UpperCAmelCase					,			*UpperCAmelCase					,			**UpperCAmelCase			)

           return cls(UpperCAmelCase					,			UpperCAmelCase			)



       def  A							(     self	:   List[Any]					,			UpperCAmelCase	:   Optional[int]			):
           # save block records
           np.save(os.path.join(UpperCAmelCase					,			_REALM_BLOCK_RECORDS_FILENAME			)					,			self.block_records			)
           # save tokenizer
           self.tokenizer.save_pretrained(UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]					,			UpperCAmelCase	:   List[Any]					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Tuple						=   []
           lowerCAmelCase_					:	str						=   []
           lowerCAmelCase_					:	Optional[Any]						=   []
           lowerCAmelCase_					:	Any						=   0

           for input_id in concat_inputs.input_ids:
               lowerCAmelCase_					:	Optional[int]						=   input_id.tolist()
               # Check answers between two [SEP] tokens
               lowerCAmelCase_					:	List[str]						=   input_id_list.index(self.tokenizer.sep_token_id			)
               lowerCAmelCase_					:	int						=   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					,			UpperCAmelCase			):
                       if answer[0] == input_id_list[idx]:
                           if input_id_list[idx : idx + len(UpperCAmelCase			)] == answer:
                               start_pos[-1].append(UpperCAmelCase			)
                               end_pos[-1].append(idx + len(UpperCAmelCase			) - 1			)

               if len(start_pos[-1]			) == 0:
                   has_answers.append(UpperCAmelCase			)
               else:
                   has_answers.append(UpperCAmelCase			)
                   if len(start_pos[-1]			) > max_answers:
                       lowerCAmelCase_					:	Dict						=   len(start_pos[-1]			)

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











import math
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      list:
    '''simple docstring'''





    if n <= 0:
        raise ValueError("""Only positive integers have prime factors"""		)
    lowerCAmelCase_					:	str						=   []
    while n % 2 == 0:
        pf.append(2		)
        lowerCAmelCase_					:	str						=   int(n / 2		)
    for i in range(3		,    int(math.sqrt(lowercase__		)		) + 1		,    2		):
        while n % i == 0:
            pf.append(lowercase__		)
            lowerCAmelCase_					:	List[Any]						=   int(n / i		)
    if n > 2:
        pf.append(lowercase__		)
    return pf
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      int:
    '''simple docstring'''





    if n <= 0:
        raise ValueError("""Only positive numbers are accepted"""		)
    lowerCAmelCase_					:	Union[str, Any]						=   1
    lowerCAmelCase_					:	str						=   1
    while n % 2 == 0:
        temp += 1
        lowerCAmelCase_					:	Optional[Any]						=   int(n / 2		)
    div *= temp
    for i in range(3		,    int(math.sqrt(lowercase__		)		) + 1		,    2		):
        lowerCAmelCase_					:	Tuple						=   1
        while n % i == 0:
            temp += 1
            lowerCAmelCase_					:	Any						=   int(n / i		)
        div *= temp
    if n > 1:
        div *= 2
    return div
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      int:
    '''simple docstring'''





    if n <= 0:
        raise ValueError("""Only positive numbers are accepted"""		)
    lowerCAmelCase_					:	Tuple						=   1
    lowerCAmelCase_					:	str						=   1
    while n % 2 == 0:
        temp += 1
        lowerCAmelCase_					:	Optional[int]						=   int(n / 2		)
    if temp > 1:
        s *= (2**temp - 1) / (2 - 1)
    for i in range(3		,    int(math.sqrt(lowercase__		)		) + 1		,    2		):
        lowerCAmelCase_					:	int						=   1
        while n % i == 0:
            temp += 1
            lowerCAmelCase_					:	Any						=   int(n / i		)
        if temp > 1:
            s *= (i**temp - 1) / (i - 1)
    return int(lowercase__		)
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      int:
    '''simple docstring'''





    lowerCAmelCase_					:	str						=   n
    for x in set(prime_factors(lowercase__		)		):
        s *= (x - 1) / x
    return int(lowercase__		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


