



"""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


_UpperCamelCase:					Union[str, Any]     				=	'block_records.npy'


_UpperCamelCase:					List[str]     				=	logging.get_logger(__name__)


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase					)  ->      np.ndarray:






      '''simple docstring'''


      import tensorflow.compat.va as tf

      lowercase	:   Optional[int]											= tf.data.TFRecordDataset(_UpperCAmelCase						,   buffer_size=5_12 * 10_24 * 10_24					)
      lowercase	:   str											= blocks_dataset.batch(_UpperCAmelCase						,   drop_remainder=_UpperCAmelCase					)
      lowercase	:   Dict											= next(blocks_dataset.take(1					).as_numpy_iterator()					)

      return np_record


class 	a__		:


   def __init__(				self   :				List[Any],  lowerCAmelCase   :				Optional[int],  lowerCAmelCase   :				Union[str, Any],  lowerCAmelCase   :				int=2,  lowerCAmelCase   :				Optional[int]=1000,  lowerCAmelCase   :				List[Any]=100,  lowerCAmelCase   :				str=100000,  )     ->							int:
         from scann.scann_ops.py.scann_ops_pybind import builder as Builder

         lowercase	:   Optional[Any]											= Builder(db=lowerCAmelCase,  num_neighbors=lowerCAmelCase,  distance_measure='dot_product'      )
         lowercase	:   Optional[int]											= builder.tree(
             num_leaves=lowerCAmelCase,  num_leaves_to_search=lowerCAmelCase,  training_sample_size=lowerCAmelCase      )
         lowercase	:   List[str]											= builder.score_ah(dimensions_per_block=lowerCAmelCase      )

         lowercase	:   Dict											= builder.build()






   def 							lowercase					(				self   :				str,  lowerCAmelCase   :				Tuple      )     ->							Optional[Any]:
         lowercase						,     lowercase	:   str											= self.searcher.search_batched(question_projection.detach().cpu()      )
         return retrieved_block_ids.astype('int64'      )




class 	a__		:


   def __init__(				self   :				Any,  lowerCAmelCase   :				str,  lowerCAmelCase   :				List[Any]      )     ->							Union[str, Any]:
         super().__init__()
         lowercase	:   Union[str, Any]											= block_records
         lowercase	:   List[str]											= tokenizer


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

         lowercase	:   int											= self.tokenizer.decode(question_input_ids[0],  skip_special_tokens=lowerCAmelCase      )

         lowercase	:   Any											= []
         lowercase	:   Any											= []
         for retrieved_block in retrieved_blocks:
               text.append(lowerCAmelCase      )
               text_pair.append(retrieved_block.decode()      )

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

         lowercase	:   List[str]											= AutoTokenizer.from_pretrained(lowerCAmelCase,  *lowerCAmelCase,  **lowerCAmelCase      )

         return cls(lowerCAmelCase,  lowerCAmelCase      )


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






   def 							lowercase					(				self   :				Optional[Any],  lowerCAmelCase   :				Dict,  lowerCAmelCase   :				Dict      )     ->							Tuple:
         lowercase	:   Tuple											= []
         lowercase	:   List[str]											= []
         lowercase	:   Tuple											= []
         lowercase	:   Optional[int]											= 0

         for input_id in concat_inputs.input_ids:
               lowercase	:   Union[str, Any]											= input_id.tolist()
               # Check answers between two [SEP] tokens
               lowercase	:   List[Any]											= input_id_list.index(self.tokenizer.sep_token_id      )
               lowercase	:   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:
                           lowercase	:   List[Any]											= len(start_pos[-1]      )

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





"""simple docstring"""




from __future__ import annotations

# Define the Gravitational Constant G and the function
_UpperCamelCase:					Tuple     				=	6.6_7_4_3e-1_1  # unit of G : m^3 * kg^-1 * s^-2


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					)  ->      dict[str, float]:






      '''simple docstring'''


      lowercase	:   str											= mass_a * mass_a

      if (force, mass_a, mass_a, distance).count(0					) != 1:
            raise ValueError('One and only one argument must be 0'					)
      if force < 0:
            raise ValueError('Gravitational force can not be negative'					)
      if distance < 0:
            raise ValueError('Distance can not be negative'					)
      if mass_a < 0 or mass_a < 0:
            raise ValueError('Mass can not be negative'					)
      if force == 0:
            lowercase	:   str											= GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
            return {"force": force}
      elif mass_a == 0:
            lowercase	:   Optional[int]											= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_1": mass_a}
      elif mass_a == 0:
            lowercase	:   Optional[int]											= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_2": mass_a}
      elif distance == 0:
            lowercase	:   List[Any]											= (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
            return {"distance": distance}
      raise ValueError('One and only one argument must be 0'					)


# Run doctest
if __name__ == "__main__":
     import doctest

     doctest.testmod()
