






"""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					=     'block_records.npy'


UpperCamelCase					=     logging.get_logger(__name__)






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       import tensorflow.compat.va as tf

       lowercase__:							Union[str, Any]         =     tf.data.TFRecordDataset(__UpperCamelCase		,  buffer_size=512 * 1024 * 1024						)
       lowercase__:							str         =     blocks_dataset.batch(__UpperCamelCase		,  drop_remainder=__UpperCamelCase						)
       lowercase__:							Tuple         =     next(blocks_dataset.take(1						).as_numpy_iterator()						)

       return np_record







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_=2					, UpperCAmelCase_=1_000					, UpperCAmelCase_=100					, UpperCAmelCase_=100_000					, )			->   int:




          '''simple docstring'''




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

          lowercase__:							List[Any]         =     Builder(db=UpperCamelCase__					, num_neighbors=UpperCamelCase__					, distance_measure="dot_product")
          lowercase__:							List[Any]         =     builder.tree(
              num_leaves=UpperCamelCase__					, num_leaves_to_search=UpperCamelCase__					, training_sample_size=UpperCamelCase__)
          lowercase__:							int         =     builder.score_ah(dimensions_per_block=UpperCamelCase__)

          lowercase__:							Union[str, Any]         =     builder.build()





   def __lowercase							(       self					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     self.searcher.search_batched(question_projection.detach().cpu())
          return retrieved_block_ids.astype("int64")







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   int:




          '''simple docstring'''




          super().__init__()
          lowercase__:							Dict         =     block_records
          lowercase__:							Optional[Any]         =     tokenizer





   def __call__(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_=None					, UpperCAmelCase_="pt")			->   Tuple:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     np.take(self.block_records					, indices=UpperCamelCase__					, axis=0)

          lowercase__:							str         =     self.tokenizer.decode(question_input_ids[0]					, skip_special_tokens=UpperCamelCase__)

          lowercase__:							str         =     []
          lowercase__:							str         =     []
          for retrieved_block in retrieved_blocks:
                 text.append(UpperCamelCase__)
                 text_pair.append(retrieved_block.decode())

          lowercase__:							List[Any]         =     self.tokenizer(
              UpperCamelCase__					, UpperCamelCase__					, padding=UpperCamelCase__					, truncation=UpperCamelCase__					, return_special_tokens_mask=UpperCamelCase__					, max_length=UpperCamelCase__)
          lowercase__:							Optional[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 __lowercase							(       cls					, UpperCAmelCase_					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   List[Any]:




          '''simple docstring'''




          if os.path.isdir(UpperCamelCase__):
                 lowercase__:							Optional[int]         =     os.path.join(UpperCamelCase__					, _REALM_BLOCK_RECORDS_FILENAME)
          else:
                 lowercase__:							Optional[int]         =     hf_hub_download(
                     repo_id=UpperCamelCase__					, filename=_REALM_BLOCK_RECORDS_FILENAME					, **UpperCamelCase__)
          lowercase__:							int         =     np.load(UpperCamelCase__					, allow_pickle=UpperCamelCase__)

          lowercase__:							Tuple         =     AutoTokenizer.from_pretrained(UpperCamelCase__					, *UpperCamelCase__					, **UpperCamelCase__)

          return cls(UpperCamelCase__					, UpperCamelCase__)





   def __lowercase							(       self					, UpperCAmelCase_)			->   Optional[Any]:




          '''simple docstring'''




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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   List[Any]:




          '''simple docstring'''




          lowercase__:							str         =     []
          lowercase__:							List[str]         =     []
          lowercase__:							Union[str, Any]         =     []
          lowercase__:							Union[str, Any]         =     0

          for input_id in concat_inputs.input_ids:
                 lowercase__:							List[str]         =     input_id.tolist()
                 # Check answers between two [SEP] tokens
                 lowercase__:							Dict         =     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					, 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:
                               lowercase__:							int         =     len(start_pos[-1])

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








_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.00_0984_2073,
    "short-ton": 0.00_1102_3122,
    "pound": 2.20_4624_4202,
    "stone": 0.15_7473_1728,
    "ounce": 35.2_7399_0723,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.022136652E26,
}

_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1016.0_4608,
    "short-ton": 907.184,
    "pound": 0.45_3592,
    "stone": 6.3_5029,
    "ounce": 0.028_3495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.660540199E-27,
}

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    str				,    __UpperCamelCase			:    str				,    __UpperCamelCase			:    float							)			->							float:

				"""simple docstring"""







				if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
								A__	:						int								=				(
								    F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
								    F"Supported values are: {', '.join(__UpperCamelCase							)}"
								)
								raise ValueError(__UpperCamelCase							)
				return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
					import doctest

					doctest.testmod()