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      lowercase_							(     _lowerCamelCase				:			str						,      _lowerCamelCase				:			int):
    import tensorflow.compat.va as tf

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

    return np_record



class     snake_case_			:






   def __init__( self							:	int      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	Any      ,		lowercase_							:	Any=2      ,		lowercase_							:	List[str]=10_00      ,		lowercase_							:	int=1_00      ,		lowercase_							:	Union[str, Any]=10_00_00      ,		)   ->		Any:
       from scann.scann_ops.py.scann_ops_pybind import builder as Builder

       lowercase__					:  List[str]          =							Builder(db=lowercase_      ,		num_neighbors=lowercase_      ,		distance_measure="dot_product"    )
       lowercase__					:  Tuple          =							builder.tree(
           num_leaves=lowercase_      ,		num_leaves_to_search=lowercase_      ,		training_sample_size=lowercase_    )
       lowercase__					:  str          =							builder.score_ah(dimensions_per_block=lowercase_    )

       lowercase__					:  Optional[int]          =							builder.build()







   def  __UpperCamelCase	( self							:	Optional[Any]      ,		lowercase_							:	Union[str, Any]    )   ->		int:
       lowercase__   ,  lowercase__					:  Dict          =							self.searcher.search_batched(question_projection.detach().cpu()    )
       return retrieved_block_ids.astype("int64"    )



class     snake_case_			:






   def __init__( self							:	Union[str, Any]      ,		lowercase_							:	List[Any]      ,		lowercase_							:	Dict    )   ->		int:
       super().__init__()
       lowercase__					:  str          =							block_records
       lowercase__					:  List[str]          =							tokenizer






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

       lowercase__					:  Optional[Any]          =							self.tokenizer.decode(question_input_ids[0]      ,		skip_special_tokens=lowercase_    )

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

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

       lowercase__					:  int          =							AutoTokenizer.from_pretrained(lowercase_      ,		*lowercase_      ,		**lowercase_    )

       return cls(lowercase_      ,		lowercase_    )






   def  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	Union[str, Any]    )   ->		Any:
       # save block records
       np.save(os.path.join(lowercase_      ,		_REALM_BLOCK_RECORDS_FILENAME    )      ,		self.block_records    )
       # save tokenizer
       self.tokenizer.save_pretrained(lowercase_    )







   def  __UpperCamelCase	( self							:	Optional[int]      ,		lowercase_							:	Tuple      ,		lowercase_							:	Tuple    )   ->		List[str]:
       lowercase__					:  int          =							[]
       lowercase__					:  Dict          =							[]
       lowercase__					:  List[str]          =							[]
       lowercase__					:  Optional[Any]          =							0

       for input_id in concat_inputs.input_ids:
           lowercase__					:  Union[str, Any]          =							input_id.tolist()
           # Check answers between two [SEP] tokens
           lowercase__					:  int          =							input_id_list.index(self.tokenizer.sep_token_id    )
           lowercase__					:  Dict          =							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__					:  Optional[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__					:  Union[str, Any]          =							[-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, is_vision_available


UpperCamelCase    				=		{'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		[
          '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ViTHybridForImageClassification''',
          '''ViTHybridModel''',
          '''ViTHybridPreTrainedModel''',
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
      from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_vit_hybrid import (
                VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
                ViTHybridForImageClassification,
                ViTHybridModel,
                ViTHybridPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
      import sys

      UpperCamelCase    				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



