

"""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


A_							:     Any     =	"block_records.npy"


A_							:     str     =	logging.get_logger(__name__)

def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


			import tensorflow.compat.va as tf

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

			return np_record





class 				lowerCamelCase  :

			def __init__(   self			:  Any      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  Union[str, Any]=2      ,			__UpperCAmelCase			:  Optional[Any]=1_0_0_0      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0      ,			__UpperCAmelCase			:  List[str]=1_0_0_0_0_0      ,			)				->      Optional[Any]:
						from scann.scann_ops.py.scann_ops_pybind import builder as Builder

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

						SCREAMING_SNAKE_CASE__									= builder.build()





			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  List[Any]    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.searcher.search_batched(question_projection.detach().cpu()    )
						return retrieved_block_ids.astype("""int64"""    )





class 				lowerCamelCase  :

			def __init__(   self			:  Any      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  int    )				->      Optional[int]:
						super().__init__()
						SCREAMING_SNAKE_CASE__									= block_records
						SCREAMING_SNAKE_CASE__									= tokenizer

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

						SCREAMING_SNAKE_CASE__									= self.tokenizer.decode(question_input_ids[0]      ,			skip_special_tokens=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= []
						SCREAMING_SNAKE_CASE__									= []
						for retrieved_block in retrieved_blocks:
									text.append(__UpperCAmelCase    )
									text_pair.append(retrieved_block.decode()    )

						SCREAMING_SNAKE_CASE__									= self.tokenizer(
						    __UpperCAmelCase      ,			__UpperCAmelCase      ,			padding=__UpperCAmelCase      ,			truncation=__UpperCAmelCase      ,			return_special_tokens_mask=__UpperCAmelCase      ,			max_length=__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= concat_inputs.convert_to_tensors(__UpperCAmelCase    )

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

			@classmethod
			def    SCREAMING_SNAKE_CASE			(   cls			:  Tuple      ,			__UpperCAmelCase			:  Optional[Union[str, os.PathLike]]      ,			*__UpperCAmelCase			:  int      ,			**__UpperCAmelCase			:  Optional[int]    )				->      Tuple:
						if os.path.isdir(__UpperCAmelCase    ):
									SCREAMING_SNAKE_CASE__									= os.path.join(__UpperCAmelCase      ,			_REALM_BLOCK_RECORDS_FILENAME    )
						else:
									SCREAMING_SNAKE_CASE__									= hf_hub_download(
									    repo_id=__UpperCAmelCase      ,			filename=_REALM_BLOCK_RECORDS_FILENAME      ,			**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= np.load(__UpperCAmelCase      ,			allow_pickle=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= AutoTokenizer.from_pretrained(__UpperCAmelCase      ,			*__UpperCAmelCase      ,			**__UpperCAmelCase    )

						return cls(__UpperCAmelCase      ,			__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]      ,			__UpperCAmelCase			:  Optional[int]    )				->      str:
						# save block records
						np.save(os.path.join(__UpperCAmelCase      ,			_REALM_BLOCK_RECORDS_FILENAME    )      ,			self.block_records    )
						# save tokenizer
						self.tokenizer.save_pretrained(__UpperCAmelCase    )





			def    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Dict    )				->      Dict:
						SCREAMING_SNAKE_CASE__									= []
						SCREAMING_SNAKE_CASE__									= []
						SCREAMING_SNAKE_CASE__									= []
						SCREAMING_SNAKE_CASE__									= 0

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

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

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





"""simple docstring"""




def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


			return a + b


if __name__ == "__main__":
						A_							:     List[str]     =	5
						A_							:     Optional[int]     =	6
						print(F'The sum of {a} + {b} is {add(a, b)}')


