"""simple docstring"""


def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[Any]						):
    __a			      =						[]
    __a			      =						1

    for i in range(len(_lowerCAmelCase						)						):
        if i + 1 < len(_lowerCAmelCase						) and text[i] == text[i + 1]:
            count += 1
        else:
            encoded.append((text[i], count)						)
            __a			      =						1

    return encoded

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]						):
    return "".join(char * length for char, length in encoded						)


if __name__ == "__main__":
 from doctest import testmod

 testmod(name="""run_length_encode""", verbose=True)
 testmod(name="""run_length_decode""", verbose=True)





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__       =			"""block_records.npy"""


A__       =			logging.get_logger(__name__)


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					import tensorflow.compat.va as tf

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

					return np_record





class    __lowerCAmelCase					:




				def __init__(				self		,							_snake_case		,							_snake_case		,							_snake_case=2		,							_snake_case=1000		,							_snake_case=100		,							_snake_case=100000		,							):



									"""simple docstring"""





									from scann.scann_ops.py.scann_ops_pybind import builder as Builder

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

									_lowerCAmelCase         =       builder.build()




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





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





class    __lowerCAmelCase					:




				def __init__(				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									super().__init__()
									_lowerCAmelCase         =       block_records
									_lowerCAmelCase         =       tokenizer




				def __call__(				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case=None		,							_snake_case="pt"						):



									"""simple docstring"""





									_lowerCAmelCase         =       np.take(self.block_records		,							indices=_snake_case		,							axis=0						)

									_lowerCAmelCase         =       self.tokenizer.decode(question_input_ids[0]		,							skip_special_tokens=_snake_case						)

									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       []
									for retrieved_block in retrieved_blocks:
														text.append(_snake_case						)
														text_pair.append(retrieved_block.decode()						)

									_lowerCAmelCase         =       self.tokenizer(
									    _snake_case		,							_snake_case		,							padding=_snake_case		,							truncation=_snake_case		,							return_special_tokens_mask=_snake_case		,							max_length=_snake_case						)
									_lowerCAmelCase         =       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 				snake_case  (				cls		,							_snake_case		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





									if os.path.isdir(_snake_case						):
														_lowerCAmelCase         =       os.path.join(_snake_case		,							_REALM_BLOCK_RECORDS_FILENAME						)
									else:
														_lowerCAmelCase         =       hf_hub_download(
														    repo_id=_snake_case		,							filename=_REALM_BLOCK_RECORDS_FILENAME		,							**_snake_case						)
									_lowerCAmelCase         =       np.load(_snake_case		,							allow_pickle=_snake_case						)

									_lowerCAmelCase         =       AutoTokenizer.from_pretrained(_snake_case		,							*_snake_case		,							**_snake_case						)

									return cls(_snake_case		,							_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									np.save(os.path.join(_snake_case		,							_REALM_BLOCK_RECORDS_FILENAME						)		,							self.block_records						)
									# save tokenizer
									self.tokenizer.save_pretrained(_snake_case						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       0

									for input_id in concat_inputs.input_ids:
														_lowerCAmelCase         =       input_id.tolist()
														# Check answers between two [SEP] tokens
														_lowerCAmelCase         =       input_id_list.index(self.tokenizer.sep_token_id						)
														_lowerCAmelCase         =       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:
																								_lowerCAmelCase         =       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:
																			_lowerCAmelCase         =       [-1] * (max_answers - len(start_pos_						))
																			start_pos_ += padded
																			end_pos_ += padded
									return has_answers, start_pos, end_pos



