'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A		:			Optional[Any]	      ={'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:			List[str]	      =[
     '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''QDQBertForMaskedLM''',
     '''QDQBertForMultipleChoice''',
     '''QDQBertForNextSentencePrediction''',
     '''QDQBertForQuestionAnswering''',
     '''QDQBertForSequenceClassification''',
     '''QDQBertForTokenClassification''',
     '''QDQBertLayer''',
     '''QDQBertLMHeadModel''',
     '''QDQBertModel''',
     '''QDQBertPreTrainedModel''',
     '''load_tf_weights_in_qdqbert''',
 ]


if TYPE_CHECKING:
 from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_qdqbert import (
      QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      QDQBertForMaskedLM,
      QDQBertForMultipleChoice,
      QDQBertForNextSentencePrediction,
      QDQBertForQuestionAnswering,
      QDQBertForSequenceClassification,
      QDQBertForTokenClassification,
      QDQBertLayer,
      QDQBertLMHeadModel,
      QDQBertModel,
      QDQBertPreTrainedModel,
      load_tf_weights_in_qdqbert,
  )


else:
 import sys

 _A		:			Tuple	      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
'''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		:			List[str]	      ='''block_records.npy'''


_A		:			Optional[int]	      =logging.get_logger(__name__)







def 							__UpperCamelCase   (				__UpperCAmelCase     :				str   ,      __UpperCAmelCase     :				int )       ->  np.ndarray:
  import tensorflow.compat.va as tf

  lowercase							:       Optional[Any]											=      tf.data.TFRecordDataset(__UpperCAmelCase   ,      buffer_size=512 * 1_024 * 1_024 )
  lowercase							:       Optional[Any]											=      blocks_dataset.batch(__UpperCAmelCase   ,      drop_remainder=__UpperCAmelCase )
  lowercase							:       Tuple											=      next(blocks_dataset.take(1 ).as_numpy_iterator() )

  return np_record




class _lowerCAmelCase :







      def __init__(     self     ,						lowercase     ,						lowercase     ,						lowercase=2     ,						lowercase=1000     ,						lowercase=100     ,						lowercase=10_0000     ,						):
        from scann.scann_ops.py.scann_ops_pybind import builder as Builder

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

        lowercase							:       Any											=      builder.build()







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





class _lowerCAmelCase :







      def __init__(     self     ,						lowercase     ,						lowercase    ):
        super().__init__()
        lowercase							:       int											=      block_records
        lowercase							:       List[str]											=      tokenizer







      def __call__(     self     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase=None     ,						lowercase="pt"    ):
        lowercase							:       List[str]											=      np.take(self.block_records     ,						indices=lowercase     ,						axis=0    )

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

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

        lowercase							:       Optional[Any]											=      self.tokenizer(
            lowercase     ,						lowercase     ,						padding=lowercase     ,						truncation=lowercase     ,						return_special_tokens_mask=lowercase     ,						max_length=lowercase    )
        lowercase							:       Optional[int]											=      concat_inputs.convert_to_tensors(lowercase    )

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







      @classmethod
      def     lowerCamelCase__			(     cls     ,						lowercase     ,						*lowercase     ,						**lowercase    ):
        if os.path.isdir(lowercase    ):
          lowercase							:       Optional[Any]											=      os.path.join(lowercase     ,						_REALM_BLOCK_RECORDS_FILENAME    )
        else:
          lowercase							:       Tuple											=      hf_hub_download(
              repo_id=lowercase     ,						filename=_REALM_BLOCK_RECORDS_FILENAME     ,						**lowercase    )
        lowercase							:       int											=      np.load(lowercase     ,						allow_pickle=lowercase    )

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

        return cls(lowercase     ,						lowercase    )







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        # save block records
        np.save(os.path.join(lowercase     ,						_REALM_BLOCK_RECORDS_FILENAME    )     ,						self.block_records    )
        # save tokenizer
        self.tokenizer.save_pretrained(lowercase    )







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase    ):
        lowercase							:       Union[str, Any]											=      []
        lowercase							:       Any											=      []
        lowercase							:       List[str]											=      []
        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							:       Dict											=      input_id_list.index(self.tokenizer.sep_token_id    )
          lowercase							:       int											=      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     ,						lowercase    ):
              if answer[0] == input_id_list[idx]:
                if input_id_list[idx : idx + len(lowercase    )] == answer:
                  start_pos[-1].append(lowercase    )
                  end_pos[-1].append(idx + len(lowercase    ) - 1    )

          if len(start_pos[-1]    ) == 0:
            has_answers.append(lowercase    )
          else:
            has_answers.append(lowercase    )
            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(lowercase     ,						lowercase    ):
          if len(start_pos_    ) < max_answers:
            lowercase							:       List[str]											=      [-1] * (max_answers - len(start_pos_    ))
            start_pos_ += padded
            end_pos_ += padded
        return has_answers, start_pos, end_pos