





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


UpperCamelCase__ :			str       	=			"""block_records.npy"""


UpperCamelCase__ :			Union[str, Any]       	=			logging.get_logger(__name__)




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

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

	return np_record







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	List[str]			,       __lowercase     :	Optional[int]			,       __lowercase     :	str			,       __lowercase     :	List[Any]=2			,       __lowercase     :	int=10_00			,       __lowercase     :	str=1_00			,       __lowercase     :	Any=10_00_00			,       ):






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    self     :	Any			,       __lowercase     :	List[Any]		):






						'''simple docstring'''







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







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	Dict			,       __lowercase     :	List[Any]			,       __lowercase     :	int		):






						'''simple docstring'''







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





					def __call__(    self     :	Union[str, Any]			,       __lowercase     :	str			,       __lowercase     :	Any			,       __lowercase     :	Any			,       __lowercase     :	int=None			,       __lowercase     :	Union[str, Any]="pt"		):






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    cls     :	Optional[int]			,       __lowercase     :	Optional[Union[str, os.PathLike]]			,       *__lowercase     :	int			,       **__lowercase     :	Tuple		):






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    self     :	List[str]			,       __lowercase     :	Union[str, Any]		):






						'''simple docstring'''







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





					def 							SCREAMING_SNAKE_CASE				(    self     :	Any			,       __lowercase     :	Optional[int]			,       __lowercase     :	Optional[int]		):






						'''simple docstring'''







						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









import doctest

import projectq
from projectq.ops import H, Measure




def  A_(      A ):
	UpperCAmelCase_		    =  quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(A )


if __name__ == "__main__":
				doctest.testmod()

				# initialises a new quantum backend
				UpperCamelCase__ :			Dict       	=			projectq.MainEngine()

				# Generate a list of 10 random numbers
				UpperCamelCase__ :			Dict       	=			[get_random_number(quantum_engine) for _ in range(10)]

				# Flushes the quantum engine from memory
				quantum_engine.flush()

				print("""Random numbers""", random_numbers_list)


