

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   					=  "block_records.npy"


lowerCamelCase   					=  logging.get_logger(__name__)


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					import tensorflow.compat.va as tf

					__lowerCAmelCase       =				tf.data.TFRecordDataset(UpperCAmelCase__		,       buffer_size=512 * 1_024 * 1_024   )
					__lowerCAmelCase       =				blocks_dataset.batch(UpperCAmelCase__		,       drop_remainder=UpperCAmelCase__   )
					__lowerCAmelCase       =				next(blocks_dataset.take(1   ).as_numpy_iterator()   )

					return np_record





class      snake_case_      :


							"""simple docstring"""





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

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

												__lowerCAmelCase       =				builder.build()




							def 							A__	(			self       ,    _A				):
												__lowerCAmelCase,						__lowerCAmelCase       =				self.searcher.search_batched(question_projection.detach().cpu()				)
												return retrieved_block_ids.astype('int64'				)





class      snake_case_      :


							"""simple docstring"""





							def __init__(			self       ,    _A       ,    _A				):
												super().__init__()
												__lowerCAmelCase       =				block_records
												__lowerCAmelCase       =				tokenizer




							def __call__(			self       ,    _A       ,    _A       ,    _A       ,    _A=None       ,    _A="pt"				):
												__lowerCAmelCase       =				np.take(self.block_records       ,    indices=lowerCamelCase_       ,    axis=0				)

												__lowerCAmelCase       =				self.tokenizer.decode(question_input_ids[0]       ,    skip_special_tokens=lowerCamelCase_				)

												__lowerCAmelCase       =				[]
												__lowerCAmelCase       =				[]
												for retrieved_block in retrieved_blocks:
																	text.append(lowerCamelCase_				)
																	text_pair.append(retrieved_block.decode()				)

												__lowerCAmelCase       =				self.tokenizer(
												    lowerCamelCase_       ,    lowerCamelCase_       ,    padding=lowerCamelCase_       ,    truncation=lowerCamelCase_       ,    return_special_tokens_mask=lowerCamelCase_       ,    max_length=lowerCamelCase_				)
												__lowerCAmelCase       =				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 							A__	(			cls       ,    _A       ,    *_A       ,    **_A				):

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

												__lowerCAmelCase       =				AutoTokenizer.from_pretrained(lowerCamelCase_       ,    *lowerCamelCase_       ,    **lowerCamelCase_				)

												return cls(lowerCamelCase_       ,    lowerCamelCase_				)




							def 							A__	(			self       ,    _A				):
												np.save(os.path.join(lowerCamelCase_       ,    _REALM_BLOCK_RECORDS_FILENAME				)       ,    self.block_records				)
												# save tokenizer
												self.tokenizer.save_pretrained(lowerCamelCase_				)




							def 							A__	(			self       ,    _A       ,    _A				):
												__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       ,    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:
																											__lowerCAmelCase       =				len(start_pos[-1]				)

        # Pad -1 to max_answers
												for start_pos_, end_pos_ in zip(lowerCamelCase_       ,    lowerCamelCase_				):
																	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











def        a( A			:       int					,	A			:       int					,	A			:       int			)						->			float:
					"""simple docstring"""





					if common_ratio == 1:
										# Formula for sum if common ratio is 1
										return num_of_terms * first_term

					# Formula for finding sum of n terms of a GeometricProgression
					return (first_term / (1 - common_ratio)) * (1 - common_ratio**num_of_terms)
