



"""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


snake_case_         = """block_records.npy"""


snake_case_         = logging.get_logger(__name__)

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						import tensorflow.compat.va as tf

						UpperCAmelCase    =				tf.data.TFRecordDataset(lowercase_	,			buffer_size=512 * 1024 * 1024		)
						UpperCAmelCase    =				blocks_dataset.batch(lowercase_	,			drop_remainder=lowercase_		)
						UpperCAmelCase    =				next(blocks_dataset.take(1		).as_numpy_iterator()		)

						return np_record







class    A_    :



						"""simple docstring"""






						def __init__(      self				:Optional[int]     ,						lowercase_				:Optional[Any]     ,						lowercase_				:Tuple     ,						lowercase_				:Any=2     ,						lowercase_				:Optional[Any]=10_00     ,						lowercase_				:Union[str, Any]=1_00     ,						lowercase_				:List[Any]=10_00_00     ,						)       ->    List[str]:
												from scann.scann_ops.py.scann_ops_pybind import builder as Builder

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

												UpperCAmelCase    =				builder.build()






						def 			UpperCAmelCase__  (      self				:Any     ,						lowercase_				:int      )       ->    int:
												UpperCAmelCase   ,					UpperCAmelCase    =				self.searcher.search_batched(question_projection.detach().cpu()      )
												return retrieved_block_ids.astype('int64'      )







class    A_    :



						"""simple docstring"""






						def __init__(      self				:int     ,						lowercase_				:List[str]     ,						lowercase_				:str      )       ->    Union[str, Any]:
												super().__init__()
												UpperCAmelCase    =				block_records
												UpperCAmelCase    =				tokenizer






						def __call__(      self				:str     ,						lowercase_				:Union[str, Any]     ,						lowercase_				:Any     ,						lowercase_				:Any     ,						lowercase_				:str=None     ,						lowercase_				:Tuple="pt"      )       ->    List[Any]:
												UpperCAmelCase    =				np.take(self.block_records     ,						indices=lowercase_     ,						axis=0      )

												UpperCAmelCase    =				self.tokenizer.decode(question_input_ids[0]     ,						skip_special_tokens=lowercase_      )

												UpperCAmelCase    =				[]
												UpperCAmelCase    =				[]
												for retrieved_block in retrieved_blocks:
																		text.append(lowercase_      )
																		text_pair.append(retrieved_block.decode()      )

												UpperCAmelCase    =				self.tokenizer(
												    lowercase_     ,						lowercase_     ,						padding=lowercase_     ,						truncation=lowercase_     ,						return_special_tokens_mask=lowercase_     ,						max_length=lowercase_      )
												UpperCAmelCase    =				concat_inputs.convert_to_tensors(lowercase_      )

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






						@classmethod
						def 			UpperCAmelCase__  (      cls				:int     ,						lowercase_				:Optional[Union[str, os.PathLike]]     ,						*lowercase_				:Union[str, Any]     ,						**lowercase_				:Dict      )       ->    Dict:
												if os.path.isdir(lowercase_      ):
																		UpperCAmelCase    =				os.path.join(lowercase_     ,						_REALM_BLOCK_RECORDS_FILENAME      )
												else:
																		UpperCAmelCase    =				hf_hub_download(
																		    repo_id=lowercase_     ,						filename=_REALM_BLOCK_RECORDS_FILENAME     ,						**lowercase_      )
												UpperCAmelCase    =				np.load(lowercase_     ,						allow_pickle=lowercase_      )

												UpperCAmelCase    =				AutoTokenizer.from_pretrained(lowercase_     ,						*lowercase_     ,						**lowercase_      )

												return cls(lowercase_     ,						lowercase_      )






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






						def 			UpperCAmelCase__  (      self				:Tuple     ,						lowercase_				:Dict     ,						lowercase_				:List[Any]      )       ->    List[str]:
												UpperCAmelCase    =				[]
												UpperCAmelCase    =				[]
												UpperCAmelCase    =				[]
												UpperCAmelCase    =				0

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

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

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










"""simple docstring"""


def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						UpperCAmelCase    =				1.0
						for i in range(1	,			r + 1		):
												ret *= (n + 1 - i) / i
						return round(lowercase_		)

def 				_lowerCAmelCase   (     lowercase_		):
						return choose(8 + n	,			lowercase_		) + choose(9 + n	,			lowercase_		) - 10

def 				_lowerCAmelCase   (     lowercase_		):
						return sum(non_bouncy_exact(lowercase_		) for i in range(1	,			n + 1		)		)

def 				_lowerCAmelCase   (     lowercase_ = 100		):
						return non_bouncy_upto(lowercase_		)


if __name__ == "__main__":
				print(f'''{solution() = }''')





