






"""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


A_     :					str							=     "block_records.npy"


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






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 import tensorflow.compat.va as tf

 lowerCamelCase__	:     int								=		tf.data.TFRecordDataset(UpperCAmelCase__     ,  buffer_size=512 * 1024 * 1024							)
 lowerCamelCase__	:     int								=		blocks_dataset.batch(UpperCAmelCase__     ,  drop_remainder=UpperCAmelCase__							)
 lowerCamelCase__	:     List[Any]								=		next(blocks_dataset.take(1							).as_numpy_iterator()							)

 return np_record



class 						a_ :







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_=2,	lowerCamelCase_=1_0_0_0,	lowerCamelCase_=1_0_0,	lowerCamelCase_=1_0_0_0_0_0,	):

       '''simple docstring'''



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

       lowerCamelCase__	:     List[Any]								=		Builder(db=_SCREAMING_SNAKE_CASE,	num_neighbors=_SCREAMING_SNAKE_CASE,	distance_measure='dot_product'   )
       lowerCamelCase__	:     Union[str, Any]								=		builder.tree(
           num_leaves=_SCREAMING_SNAKE_CASE,	num_leaves_to_search=_SCREAMING_SNAKE_CASE,	training_sample_size=_SCREAMING_SNAKE_CASE   )
       lowerCamelCase__	:     Tuple								=		builder.score_ah(dimensions_per_block=_SCREAMING_SNAKE_CASE   )

       lowerCamelCase__	:     Optional[Any]								=		builder.build()





      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		self.searcher.search_batched(question_projection.detach().cpu()   )
       return retrieved_block_ids.astype('int64'   )



class 						a_ :







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       super().__init__()
       lowerCamelCase__	:     int								=		block_records
       lowerCamelCase__	:     str								=		tokenizer




      def __call__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_=None,	lowerCamelCase_="pt"   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		np.take(self.block_records,	indices=_SCREAMING_SNAKE_CASE,	axis=0   )

       lowerCamelCase__	:     Optional[int]								=		self.tokenizer.decode(question_input_ids[0],	skip_special_tokens=_SCREAMING_SNAKE_CASE   )

       lowerCamelCase__	:     List[Any]								=		[]
       lowerCamelCase__	:     Dict								=		[]
       for retrieved_block in retrieved_blocks:
        text.append(_SCREAMING_SNAKE_CASE   )
        text_pair.append(retrieved_block.decode()   )

       lowerCamelCase__	:     Optional[int]								=		self.tokenizer(
           _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	padding=_SCREAMING_SNAKE_CASE,	truncation=_SCREAMING_SNAKE_CASE,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE,	max_length=_SCREAMING_SNAKE_CASE   )
       lowerCamelCase__	:     str								=		concat_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE   )

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




      @classmethod
      def      a__				(cls,	lowerCamelCase_,	*lowerCamelCase_,	**lowerCamelCase_   ):

       '''simple docstring'''



       if os.path.isdir(_SCREAMING_SNAKE_CASE   ):
        lowerCamelCase__	:     List[str]								=		os.path.join(_SCREAMING_SNAKE_CASE,	_REALM_BLOCK_RECORDS_FILENAME   )
       else:
        lowerCamelCase__	:     int								=		hf_hub_download(
            repo_id=_SCREAMING_SNAKE_CASE,	filename=_REALM_BLOCK_RECORDS_FILENAME,	**_SCREAMING_SNAKE_CASE   )
       lowerCamelCase__	:     int								=		np.load(_SCREAMING_SNAKE_CASE,	allow_pickle=_SCREAMING_SNAKE_CASE   )

       lowerCamelCase__	:     Dict								=		AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE,	*_SCREAMING_SNAKE_CASE,	**_SCREAMING_SNAKE_CASE   )

       return cls(_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE   )




      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



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





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Tuple								=		[]
       lowerCamelCase__	:     Union[str, Any]								=		[]
       lowerCamelCase__	:     Optional[int]								=		[]
       lowerCamelCase__	:     List[str]								=		0

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

        if len(start_pos[-1]   ) == 0:
         has_answers.append(_SCREAMING_SNAKE_CASE   )
        else:
         has_answers.append(_SCREAMING_SNAKE_CASE   )
         if len(start_pos[-1]   ) > max_answers:
          lowerCamelCase__	:     List[Any]								=		len(start_pos[-1]   )

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



'''simple docstring'''


__SCREAMING_SNAKE_CASE						=      [chr(i) for i in range(32, 126)]
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(reversed(range(len(alphabets))))
__SCREAMING_SNAKE_CASE						=      []
__SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      0
def 					__magic_name__(       ):
						global gear_one_pos
						global gear_two_pos
						global gear_three_pos
						UpperCamelCase_     :	List[Any]					     =     gear_one[0]
						gear_one.append(UpperCAmelCase__	)
						del gear_one[0]
						gear_one_pos += 1
						if gear_one_pos % int(len(UpperCAmelCase__	)	) == 0:
												UpperCamelCase_     :	List[str]					     =     gear_two[0]
												gear_two.append(UpperCAmelCase__	)
												del gear_two[0]
												gear_two_pos += 1
												if gear_two_pos % int(len(UpperCAmelCase__	)	) == 0:
																		UpperCamelCase_     :	List[Any]					     =     gear_three[0]
																		gear_three.append(UpperCAmelCase__	)
																		del gear_three[0]
																		gear_three_pos += 1
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Any					     =     alphabets.index(UpperCAmelCase__	)
						UpperCamelCase_     :	List[str]					     =     gear_one[target]
						UpperCamelCase_     :	str					     =     gear_two[target]
						UpperCamelCase_     :	int					     =     gear_three[target]
						UpperCamelCase_     :	Union[str, Any]					     =     reflector[target]
						UpperCamelCase_     :	Dict					     =     gear_three.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_two.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_one.index(UpperCAmelCase__	)
						code.append(alphabets[target]	)
						rotator()


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      list(input("Type your message:\n"))
	while True:
		try:
			__SCREAMING_SNAKE_CASE						=      int(input("Please set token:(must be only digits)\n"))
			break
		except Exception as error:
			print(error)
	for _ in range(token):
		rotator()
	for j in decode:
		engine(j)
	print("\n" + "".join(code))
	print(
	    f"\nYour Token is {token} please write it down.\nIf you want to decode "
	    "this message again you should input same digits as token!"
	)




