






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


_UpperCamelCase						:    List[str]		=						logging.get_logger(__name__)
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





  import tensorflow.compat.va as tf

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

  return np_record

class 			a	:







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

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

      lowercase				       =			builder.build()







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


class 			a	:







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      super().__init__()
      lowercase				       =			block_records
      lowercase				       =			tokenizer







    def __call__(     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase=None				,						_lowerCamelCase="pt"			):
      lowercase				       =			np.take(self.block_records				,						indices=_lowerCamelCase				,						axis=0			)

      lowercase				       =			self.tokenizer.decode(question_input_ids[0]				,						skip_special_tokens=_lowerCamelCase			)

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

      lowercase				       =			self.tokenizer(
          _lowerCamelCase				,						_lowerCamelCase				,						padding=_lowerCamelCase				,						truncation=_lowerCamelCase				,						return_special_tokens_mask=_lowerCamelCase				,						max_length=_lowerCamelCase			)
      lowercase				       =			concat_inputs.convert_to_tensors(_lowerCamelCase			)

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







    @classmethod
    def      UpperCamelCase_ (     cls				,						_lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			):
      if os.path.isdir(_lowerCamelCase			):
        lowercase				       =			os.path.join(_lowerCamelCase				,						_REALM_BLOCK_RECORDS_FILENAME			)
      else:
        lowercase				       =			hf_hub_download(
            repo_id=_lowerCamelCase				,						filename=_REALM_BLOCK_RECORDS_FILENAME				,						**_lowerCamelCase			)
      lowercase				       =			np.load(_lowerCamelCase				,						allow_pickle=_lowerCamelCase			)

      lowercase				       =			AutoTokenizer.from_pretrained(_lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			)

      return cls(_lowerCamelCase				,						_lowerCamelCase			)







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







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			[]
      lowercase				       =			[]
      lowercase				       =			[]
      lowercase				       =			0

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

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

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







"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase:					Tuple     				=	logging.get_logger(__name__)

_UpperCamelCase:					List[str]     				=	{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					'regnet'
   _lowerCamelCase       				=					['x', 'y']


   def __init__(				self   :				Union[str, Any],  lowerCAmelCase   :				Union[str, Any]=3,  lowerCAmelCase   :				Tuple=32,  lowerCAmelCase   :				Optional[int]=[128, 192, 512, 1088],  lowerCAmelCase   :				Optional[int]=[2, 6, 12, 2],  lowerCAmelCase   :				str=64,  lowerCAmelCase   :				List[str]="y",  lowerCAmelCase   :				Optional[int]="relu",  **lowerCAmelCase   :				List[Any],  )     ->							Union[str, Any]:
         super().__init__(**lowerCAmelCase      )
         if layer_type not in self.layer_types:
               raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types      )}'''      )
         lowercase	:   str											= num_channels
         lowercase	:   str											= embedding_size
         lowercase	:   int											= hidden_sizes
         lowercase	:   Tuple											= depths
         lowercase	:   str											= groups_width
         lowercase	:   List[str]											= layer_type
         lowercase	:   Optional[int]											= hidden_act
         # always downsample in the first stage
         lowercase	:   Optional[Any]											= True
