




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


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


SCREAMING_SNAKE_CASE_						:					Optional[Any]			   =       logging.get_logger(__name__)
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_		)     ->   Any:
    import tensorflow.compat.va as tf

    a__      =       tf.data.TFRecordDataset(UpperCAmelCase_   ,    buffer_size=512 * 1_024 * 1_024		)
    a__      =       blocks_dataset.batch(UpperCAmelCase_   ,    drop_remainder=UpperCAmelCase_		)
    a__      =       next(blocks_dataset.take(1		).as_numpy_iterator()		)

    return np_record







class 		__UpperCAmelCase:





     """simple docstring"""



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






         """simple docstring"""
         from scann.scann_ops.py.scann_ops_pybind import builder as Builder

         a__      =       Builder(db=A						,				num_neighbors=A						,				distance_measure="""dot_product"""    )
         a__      =       builder.tree(
             num_leaves=A						,				num_leaves_to_search=A						,				training_sample_size=A    )
         a__      =       builder.score_ah(dimensions_per_block=A    )

         a__      =       builder.build()






     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]						,				A :       int    )			->			str:






         """simple docstring"""
         a__ ,		a__      =       self.searcher.search_batched(question_projection.detach().cpu()    )
         return retrieved_block_ids.astype("""int64"""    )







class 		__UpperCAmelCase:





     """simple docstring"""



     def __init__(      self :       List[Any]						,				A :       Optional[Any]						,				A :       Any    )			->			str:






         """simple docstring"""
         super().__init__()
         a__      =       block_records
         a__      =       tokenizer

     def __call__(      self :       Dict						,				A :       List[str]						,				A :       Tuple						,				A :       Optional[int]						,				A :       List[str]=None						,				A :       Optional[Any]="pt"    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       np.take(self.block_records						,				indices=A						,				axis=0    )

         a__      =       self.tokenizer.decode(question_input_ids[0]						,				skip_special_tokens=A    )

         a__      =       []
         a__      =       []
         for retrieved_block in retrieved_blocks:
             text.append(A    )
             text_pair.append(retrieved_block.decode()    )

         a__      =       self.tokenizer(
             A						,				A						,				padding=A						,				truncation=A						,				return_special_tokens_mask=A						,				max_length=A    )
         a__      =       concat_inputs.convert_to_tensors(A    )

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

     @classmethod
     def 				__SCREAMING_SNAKE_CASE		(      cls :       Tuple						,				A :       Optional[Union[str, os.PathLike]]						,				*A :       int						,				**A :       Optional[int]    )			->			int:






         """simple docstring"""
         if os.path.isdir(A    ):
             a__      =       os.path.join(A						,				_REALM_BLOCK_RECORDS_FILENAME    )
         else:
             a__      =       hf_hub_download(
                 repo_id=A						,				filename=_REALM_BLOCK_RECORDS_FILENAME						,				**A    )
         a__      =       np.load(A						,				allow_pickle=A    )

         a__      =       AutoTokenizer.from_pretrained(A						,				*A						,				**A    )

         return cls(A						,				A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       str						,				A :       Optional[int]    )			->			Dict:






         """simple docstring"""
         np.save(os.path.join(A						,				_REALM_BLOCK_RECORDS_FILENAME    )						,				self.block_records    )
         # save tokenizer
         self.tokenizer.save_pretrained(A    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Any						,				A :       Union[str, Any]						,				A :       List[str]    )			->			List[str]:






         """simple docstring"""
         a__      =       []
         a__      =       []
         a__      =       []
         a__      =       0

         for input_id in concat_inputs.input_ids:
             a__      =       input_id.tolist()
             # Check answers between two [SEP] tokens
             a__      =       input_id_list.index(self.tokenizer.sep_token_id    )
             a__      =       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						,				A    ):
                     if answer[0] == input_id_list[idx]:
                         if input_id_list[idx : idx + len(A    )] == answer:
                             start_pos[-1].append(A    )
                             end_pos[-1].append(idx + len(A    ) - 1    )

             if len(start_pos[-1]    ) == 0:
                 has_answers.append(A    )
             else:
                 has_answers.append(A    )
                 if len(start_pos[-1]    ) > max_answers:
                     a__      =       len(start_pos[-1]    )

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_						:					str			   =       {
    '''configuration_wav2vec2_conformer''': [
        '''WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Wav2Vec2ConformerConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       [
     '''WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Wav2Vec2ConformerForAudioFrameClassification''',
     '''Wav2Vec2ConformerForCTC''',
     '''Wav2Vec2ConformerForPreTraining''',
     '''Wav2Vec2ConformerForSequenceClassification''',
     '''Wav2Vec2ConformerForXVector''',
     '''Wav2Vec2ConformerModel''',
     '''Wav2Vec2ConformerPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_wavaveca_conformer import (
     WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     WavaVecaConformerConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavaveca_conformer import (
      WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavaVecaConformerForAudioFrameClassification,
      WavaVecaConformerForCTC,
      WavaVecaConformerForPreTraining,
      WavaVecaConformerForSequenceClassification,
      WavaVecaConformerForXVector,
      WavaVecaConformerModel,
      WavaVecaConformerPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






