

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	 =     '''block_records.npy'''


_a	 =     logging.get_logger(__name__)







def _a				( SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    int	)					->					np.ndarray:

 """simple docstring"""






 import tensorflow.compat.va as tf

 __lowerCAmelCase:     Union[str, Any]     			=   tf.data.TFRecordDataset(SCREAMING_SNAKE_CASE					,      buffer_size=5_12 * 10_24 * 10_24	)
 __lowerCAmelCase:     Tuple     			=   blocks_dataset.batch(SCREAMING_SNAKE_CASE					,      drop_remainder=SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     int     			=   next(blocks_dataset.take(1	).as_numpy_iterator()	)

 return np_record






class 							A_				:


     def __init__(							self    :	Tuple    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	Optional[int]=2    ,						UpperCAmelCase    :	Dict=1_0_0_0    ,						UpperCAmelCase    :	int=1_0_0    ,						UpperCAmelCase    :	Tuple=1_0_0_0_0_0    ,						)							->			Any:
      from scann.scann_ops.py.scann_ops_pybind import builder as Builder

      __lowerCAmelCase:     Tuple     			=   Builder(db=UpperCAmelCase    ,						num_neighbors=UpperCAmelCase    ,						distance_measure='dot_product'      )
      __lowerCAmelCase:     Optional[Any]     			=   builder.tree(
          num_leaves=UpperCAmelCase    ,						num_leaves_to_search=UpperCAmelCase    ,						training_sample_size=UpperCAmelCase      )
      __lowerCAmelCase:     Tuple     			=   builder.score_ah(dimensions_per_block=UpperCAmelCase      )

      __lowerCAmelCase:     Any     			=   builder.build()


     def 						UpperCAmelCase						(							self    :	Union[str, Any]    ,						UpperCAmelCase    :	List[str]      )							->			List[Any]:
      __lowerCAmelCase			,							__lowerCAmelCase:     int     			=   self.searcher.search_batched(question_projection.detach().cpu()      )
      return retrieved_block_ids.astype('int64'      )







class 							A_				:


     def __init__(							self    :	Tuple    ,						UpperCAmelCase    :	Tuple    ,						UpperCAmelCase    :	int      )							->			Any:
      super().__init__()
      __lowerCAmelCase:     int     			=   block_records
      __lowerCAmelCase:     List[Any]     			=   tokenizer


     def __call__(							self    :	Tuple    ,						UpperCAmelCase    :	Dict    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	Dict    ,						UpperCAmelCase    :	Tuple=None    ,						UpperCAmelCase    :	Dict="pt"      )							->			List[Any]:
      __lowerCAmelCase:     Optional[int]     			=   np.take(self.block_records    ,						indices=UpperCAmelCase    ,						axis=0      )

      __lowerCAmelCase:     Union[str, Any]     			=   self.tokenizer.decode(question_input_ids[0]    ,						skip_special_tokens=UpperCAmelCase      )

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

      __lowerCAmelCase:     List[str]     			=   self.tokenizer(
          UpperCAmelCase    ,						UpperCAmelCase    ,						padding=UpperCAmelCase    ,						truncation=UpperCAmelCase    ,						return_special_tokens_mask=UpperCAmelCase    ,						max_length=UpperCAmelCase      )
      __lowerCAmelCase:     int     			=   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 						UpperCAmelCase						(							cls    :	Union[str, Any]    ,						UpperCAmelCase    :	Optional[Union[str, os.PathLike]]    ,						*UpperCAmelCase    :	Dict    ,						**UpperCAmelCase    :	Any      )							->			Union[str, Any]:
      if os.path.isdir(UpperCAmelCase      ):
       __lowerCAmelCase:     str     			=   os.path.join(UpperCAmelCase    ,						_REALM_BLOCK_RECORDS_FILENAME      )
      else:
       __lowerCAmelCase:     List[str]     			=   hf_hub_download(
           repo_id=UpperCAmelCase    ,						filename=_REALM_BLOCK_RECORDS_FILENAME    ,						**UpperCAmelCase      )
      __lowerCAmelCase:     Optional[Any]     			=   np.load(UpperCAmelCase    ,						allow_pickle=UpperCAmelCase      )

      __lowerCAmelCase:     Tuple     			=   AutoTokenizer.from_pretrained(UpperCAmelCase    ,						*UpperCAmelCase    ,						**UpperCAmelCase      )

      return cls(UpperCAmelCase    ,						UpperCAmelCase      )


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


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	str      )							->			List[str]:
      __lowerCAmelCase:     Union[str, Any]     			=   []
      __lowerCAmelCase:     Optional[Any]     			=   []
      __lowerCAmelCase:     List[Any]     			=   []
      __lowerCAmelCase:     Tuple     			=   0

      for input_id in concat_inputs.input_ids:
       __lowerCAmelCase:     Dict     			=   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    ,						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:     List[Any]     			=   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:     Optional[int]     			=   [-1] * (max_answers - len(start_pos_      ))
        start_pos_ += padded
        end_pos_ += padded
      return has_answers, start_pos, end_pos








import math






class 							A_				:


     def __init__(							self    :	Any    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	Any      )							->			Any:
      __lowerCAmelCase:     Optional[Any]     			=   x
      __lowerCAmelCase:     Any     			=   y
      __lowerCAmelCase:     Tuple     			=   step_size
      __lowerCAmelCase:     Dict     			=   function_to_optimize


     def 						UpperCAmelCase						(							self    :	Dict      )							->			int:
      return self.function(self.x    ,						self.y      )


     def 						UpperCAmelCase						(							self    :	str      )							->			int:
      __lowerCAmelCase:     Union[str, Any]     			=   self.step_size
      return [
          SearchProblem(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						self.function      )
          for x, y in (
              (self.x - step_size, self.y - step_size),
              (self.x - step_size, self.y),
              (self.x - step_size, self.y + step_size),
              (self.x, self.y - step_size),
              (self.x, self.y + step_size),
              (self.x + step_size, self.y - step_size),
              (self.x + step_size, self.y),
              (self.x + step_size, self.y + step_size),
          )
      ]


     def __hash__(							self    :	Optional[Any]      )							->			int:
      return hash(str(self      )      )


     def __eq__(							self    :	Dict    ,						UpperCAmelCase    :	Tuple      )							->			Any:
      if isinstance(UpperCAmelCase    ,						UpperCAmelCase      ):
       return hash(str(self      )      ) == hash(str(UpperCAmelCase      )      )
      return False


     def __str__(							self    :	Optional[Any]      )							->			Dict:
      return F'''x: {self.x} y: {self.y}'''







def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    bool = True					,      SCREAMING_SNAKE_CASE	:    float = math.inf					,      SCREAMING_SNAKE_CASE	:    float = -math.inf					,      SCREAMING_SNAKE_CASE	:    float = math.inf					,      SCREAMING_SNAKE_CASE	:    float = -math.inf					,      SCREAMING_SNAKE_CASE	:    bool = False					,      SCREAMING_SNAKE_CASE	:    int = 1_00_00					,      )					->					SearchProblem:

 """simple docstring"""






 __lowerCAmelCase:     Dict     			=   search_prob
 __lowerCAmelCase:     str     			=   []  # list to store the current score at each iteration
 __lowerCAmelCase:     Optional[int]     			=   0
 __lowerCAmelCase:     int     			=   False
 __lowerCAmelCase:     Any     			=   set()
 while not solution_found and iterations < max_iter:
  visited.add(SCREAMING_SNAKE_CASE	)
  iterations += 1
  __lowerCAmelCase:     int     			=   current_state.score()
  scores.append(SCREAMING_SNAKE_CASE	)
  __lowerCAmelCase:     Any     			=   current_state.get_neighbors()
  __lowerCAmelCase:     Tuple     			=   -math.inf
  __lowerCAmelCase:     Any     			=   math.inf
  __lowerCAmelCase:     Dict     			=   None  # to hold the next best neighbor
  for neighbor in neighbors:
   if neighbor in visited:
    continue  # do not want to visit the same state again
   if (
       neighbor.x > max_x
       or neighbor.x < min_x
       or neighbor.y > max_y
       or neighbor.y < min_y
   ):
    continue  # neighbor outside our bounds
   __lowerCAmelCase:     List[str]     			=   neighbor.score() - current_score
   if find_max:  # finding max
    # going to direction with greatest ascent
    if change > max_change and change > 0:
     __lowerCAmelCase:     int     			=   change
     __lowerCAmelCase:     int     			=   neighbor
   else:  # finding min
    # to direction with greatest descent
    if change < min_change and change < 0:
     __lowerCAmelCase:     str     			=   change
     __lowerCAmelCase:     str     			=   neighbor
  if next_state is not None:
   # we found at least one neighbor which improved the current state
   __lowerCAmelCase:     Optional[Any]     			=   next_state
  else:
   # since we have no neighbor that improves the solution we stop the search
   __lowerCAmelCase:     Dict     			=   True

 if visualization:
  from matplotlib import pyplot as plt

  plt.plot(range(SCREAMING_SNAKE_CASE	)					,      SCREAMING_SNAKE_CASE	)
  plt.xlabel('Iterations'	)
  plt.ylabel('Function values'	)
  plt.show()

 return current_state


if __name__ == "__main__":
  import doctest

  doctest.testmod()







  def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    List[str]	)					->					Optional[Any]:

   """simple docstring"""






   return (x**2) + (y**2)

  # starting the problem with initial coordinates (3, 4)
  _a	 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(prob, find_max=False)
  print(
      '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
      f"{local_min.score()}"
  )

  # starting the problem with initial coordinates (12, 47)
  _a	 =     SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(
      prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
  )
  print(
      '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
      f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
  )







  def _a				( SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    List[Any]	)					->					Dict:

   """simple docstring"""






   return (3 * x**2) - (6 * y)

  _a	 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(prob, find_max=True)



  print(
      '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
      f"{local_min.score()}"
  )





