


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    :				int       =					"block_records.npy"


_SCREAMING_SNAKE_CASE    :				Dict       =					logging.get_logger(__name__)
def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     import tensorflow.compat.va as tf

     snake_case					=       tf.data.TFRecordDataset(UpperCamelCase_							,buffer_size=5_12 * 10_24 * 10_24       )
     snake_case					=       blocks_dataset.batch(UpperCamelCase_							,drop_remainder=UpperCamelCase_       )
     snake_case					=       next(blocks_dataset.take(1       ).as_numpy_iterator()       )

     return np_record

class       A__      :







    """simple docstring"""

    def __init__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case=2    ,      __snake_case=1_0_0_0    ,      __snake_case=1_0_0    ,      __snake_case=1_0_0_0_0_0    ,      ):
         from scann.scann_ops.py.scann_ops_pybind import builder as Builder

         snake_case					=       Builder(db=__snake_case    ,      num_neighbors=__snake_case    ,      distance_measure='''dot_product'''					)
         snake_case					=       builder.tree(
             num_leaves=__snake_case    ,      num_leaves_to_search=__snake_case    ,      training_sample_size=__snake_case					)
         snake_case					=       builder.score_ah(dimensions_per_block=__snake_case					)

         snake_case					=       builder.build()




    def 	a_    ( self    ,      __snake_case					):
         snake_case				,						snake_case					=       self.searcher.search_batched(question_projection.detach().cpu()					)
         return retrieved_block_ids.astype('''int64'''					)






class       A__      :







    """simple docstring"""

    def __init__( self    ,      __snake_case    ,      __snake_case					):
         super().__init__()
         snake_case					=       block_records
         snake_case					=       tokenizer
    def __call__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case=None    ,      __snake_case="pt"					):
         snake_case					=       np.take(self.block_records    ,      indices=__snake_case    ,      axis=0					)

         snake_case					=       self.tokenizer.decode(question_input_ids[0]    ,      skip_special_tokens=__snake_case					)

         snake_case					=       []
         snake_case					=       []
         for retrieved_block in retrieved_blocks:
              text.append(__snake_case					)
              text_pair.append(retrieved_block.decode()					)

         snake_case					=       self.tokenizer(
             __snake_case    ,      __snake_case    ,      padding=__snake_case    ,      truncation=__snake_case    ,      return_special_tokens_mask=__snake_case    ,      max_length=__snake_case					)
         snake_case					=       concat_inputs.convert_to_tensors(__snake_case					)

         if answer_ids is not None:
              return self.block_has_answer(__snake_case    ,      __snake_case					) + (concat_inputs_tensors,)
         else:
              return (None, None, None, concat_inputs_tensors)
    @classmethod
    def 	a_    ( cls    ,      __snake_case    ,      *__snake_case    ,      **__snake_case					):
         if os.path.isdir(__snake_case					):
              snake_case					=       os.path.join(__snake_case    ,      _REALM_BLOCK_RECORDS_FILENAME					)
         else:
              snake_case					=       hf_hub_download(
                  repo_id=__snake_case    ,      filename=_REALM_BLOCK_RECORDS_FILENAME    ,      **__snake_case					)
         snake_case					=       np.load(__snake_case    ,      allow_pickle=__snake_case					)

         snake_case					=       AutoTokenizer.from_pretrained(__snake_case    ,      *__snake_case    ,      **__snake_case					)

         return cls(__snake_case    ,      __snake_case					)
    def 	a_    ( self    ,      __snake_case					):
         # save block records
         np.save(os.path.join(__snake_case    ,      _REALM_BLOCK_RECORDS_FILENAME					)    ,      self.block_records					)
         # save tokenizer
         self.tokenizer.save_pretrained(__snake_case					)




    def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
         snake_case					=       []
         snake_case					=       []
         snake_case					=       []
         snake_case					=       0

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

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

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








from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    "cross_attention",
    "0.20.0",
    "Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.",
    standard_warn=False,
)


_SCREAMING_SNAKE_CASE    :				Optional[int]       =					AttentionProcessor

class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)




