

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


lowerCAmelCase							:						List[Any]     			=		'''block_records.npy'''


lowerCAmelCase							:						Tuple     			=		logging.get_logger(__name__)







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						import tensorflow.compat.va as tf

						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= tf.data.TFRecordDataset(__lowerCAmelCase       ,			buffer_size=5_12 * 10_24 * 10_24					)
						SCREAMING_SNAKE_CASE_:				List[str]							= blocks_dataset.batch(__lowerCAmelCase       ,			drop_remainder=__lowerCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Any							= next(blocks_dataset.take(1					).as_numpy_iterator()					)

						return np_record



class 						__lowercase       :




			"""simple docstring"""







			def __init__(				self						:  str     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  Optional[int]=2     ,		lowerCAmelCase__						:  Optional[Any]=1000     ,		lowerCAmelCase__						:  Optional[Any]=100     ,		lowerCAmelCase__						:  int=10_0000     ,		):
									from scann.scann_ops.py.scann_ops_pybind import builder as Builder

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

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= builder.build()




			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]     ,		lowerCAmelCase__						:  Union[str, Any]):
									SCREAMING_SNAKE_CASE_:				Tuple							= self.searcher.search_batched(question_projection.detach().cpu())
									return retrieved_block_ids.astype("int64")







class 						__lowercase       :




			"""simple docstring"""







			def __init__(				self						:  Any     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  List[Any]):
									super().__init__()
									SCREAMING_SNAKE_CASE_:				str							= block_records
									SCREAMING_SNAKE_CASE_:				List[str]							= tokenizer



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

									SCREAMING_SNAKE_CASE_:				Dict							= self.tokenizer.decode(question_input_ids[0]     ,		skip_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Dict							= []
									SCREAMING_SNAKE_CASE_:				Tuple							= []
									for retrieved_block in retrieved_blocks:
															text.append(lowerCAmelCase__)
															text_pair.append(retrieved_block.decode())

									SCREAMING_SNAKE_CASE_:				List[str]							= self.tokenizer(
									    lowerCAmelCase__     ,		lowerCAmelCase__     ,		padding=lowerCAmelCase__     ,		truncation=lowerCAmelCase__     ,		return_special_tokens_mask=lowerCAmelCase__     ,		max_length=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Dict							= 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  _SCREAMING_SNAKE_CASE    (				cls						:  int     ,		lowerCAmelCase__						:  Optional[Union[str, os.PathLike]]     ,		*lowerCAmelCase__						:  Tuple     ,		**lowerCAmelCase__						:  Union[str, Any]):






									if os.path.isdir(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= os.path.join(lowerCAmelCase__     ,		_REALM_BLOCK_RECORDS_FILENAME)
									else:
															SCREAMING_SNAKE_CASE_:				List[str]							= hf_hub_download(
															    repo_id=lowerCAmelCase__     ,		filename=_REALM_BLOCK_RECORDS_FILENAME     ,		**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= np.load(lowerCAmelCase__     ,		allow_pickle=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				List[Any]							= AutoTokenizer.from_pretrained(lowerCAmelCase__     ,		*lowerCAmelCase__     ,		**lowerCAmelCase__)

									return cls(lowerCAmelCase__     ,		lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  Tuple):
									np.save(os.path.join(lowerCAmelCase__     ,		_REALM_BLOCK_RECORDS_FILENAME)     ,		self.block_records)
									# save tokenizer
									self.tokenizer.save_pretrained(lowerCAmelCase__)




			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  Any):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= []
									SCREAMING_SNAKE_CASE_:				Optional[int]							= []
									SCREAMING_SNAKE_CASE_:				Any							= []
									SCREAMING_SNAKE_CASE_:				int							= 0

									for input_id in concat_inputs.input_ids:
															SCREAMING_SNAKE_CASE_:				List[str]							= input_id.tolist()
															# Check answers between two [SEP] tokens
															SCREAMING_SNAKE_CASE_:				Optional[int]							= input_id_list.index(self.tokenizer.sep_token_id)
															SCREAMING_SNAKE_CASE_:				str							= 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:
																											SCREAMING_SNAKE_CASE_:				str							= len(start_pos[-1])

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









def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= []
						SCREAMING_SNAKE_CASE_:				Any							= 1

						for i in range(len(_UpperCAmelCase					)					):
												if i + 1 < len(_UpperCAmelCase					) and text[i] == text[i + 1]:
																		count += 1
												else:
																		encoded.append((text[i], count)					)
																		SCREAMING_SNAKE_CASE_:				List[Any]							= 1

						return encoded







def     A_   (					_UpperCAmelCase					):
						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)






