





'''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


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


snake_case_  :							Dict            =	logging.get_logger(__name__)





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      int )			->						np.ndarray:
						import tensorflow.compat.va as tf

						UpperCAmelCase_      :   Union[str, Any]      =	tf.data.TFRecordDataset(__a,      buffer_size=512 * 1024 * 1024 )
						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        __a   :

							def __init__(				self				:  Tuple							,  __magic_name__				:  Dict							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  List[Any]=2							,  __magic_name__				:  List[Any]=10_00							,  __magic_name__				:  str=1_00							,  __magic_name__				:  int=10_00_00							,  )       ->							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				:  Dict							,  __magic_name__				:  Tuple )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	self.searcher.search_batched(question_projection.detach().cpu() )
													return retrieved_block_ids.astype('''int64''' )





class        __a   :

							def __init__(				self				:  Union[str, Any]							,  __magic_name__				:  Optional[Any]							,  __magic_name__				:  Tuple )       ->							str:







													"""simple docstring"""
													super().__init__()
													UpperCAmelCase_      :   Tuple      =	block_records
													UpperCAmelCase_      :   List[Any]      =	tokenizer

							def __call__(				self				:  Optional[int]							,  __magic_name__				:  Any							,  __magic_name__				:  Optional[Any]							,  __magic_name__				:  int							,  __magic_name__				:  int=None							,  __magic_name__				:  Optional[int]="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				:  Union[str, Any]							,  __magic_name__				:  Optional[int]							,  *__magic_name__				:  List[str]							,  **__magic_name__				:  Tuple )       ->							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				:  Optional[int]							,  __magic_name__				:  Tuple )       ->							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				:  Optional[Any]							,  __magic_name__				:  Any							,  __magic_name__				:  Optional[Any] )       ->							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






'''simple docstring'''

from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def  _lowerCAmelCase				(				__a      )  ->      int:
     '''simple docstring'''







     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(__a			,      -1      )



def  _lowerCAmelCase				(				__a      )  ->      str:
     '''simple docstring'''







     if not balanced_parentheses(__a      ):
          raise ValueError("""Mismatched parentheses"""      )
     _UpperCamelCase  :Stack[str]       =Stack()
     _UpperCamelCase  :Optional[int]       =[]
     for char in expression_str:
          if char.isalpha() or char.isdigit():
               postfix.append(__a      )
          elif char == "(":
               stack.push(__a      )
          elif char == ")":
               while not stack.is_empty() and stack.peek() != "(":
                    postfix.append(stack.pop()      )
               stack.pop()
          else:
               while not stack.is_empty() and precedence(__a      ) <= precedence(stack.peek()      ):
                    postfix.append(stack.pop()      )
               stack.push(__a      )
     while not stack.is_empty():
          postfix.append(stack.pop()      )
     return " ".join(__a      )


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      _lowerCamelCase :				Tuple														= """a+b*(c^d-e)^(f+g*h)-i"""

      print("""Infix to Postfix Notation demonstration:\n""")
      print("""Infix notation: """ + expression)
      print("""Postfix notation: """ + infix_to_postfix(expression))