
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


_SCREAMING_SNAKE_CASE     :   Tuple							 =      'block_records.npy'


_SCREAMING_SNAKE_CASE     :   int							 =      logging.get_logger(__name__)

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    str				,    __UpperCamelCase			:    int							)			->							np.ndarray:

				"""simple docstring"""







				import tensorflow.compat.va as tf

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

				return np_record







class 			UpperCamelCase__		:







	'''simple docstring'''





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__=2					,							UpperCamelCase__=1000					,							UpperCamelCase__=100					,							UpperCamelCase__=10_0000					,							):
					from scann.scann_ops.py.scann_ops_pybind import builder as Builder

					A__	:						List[Any]								=				Builder(db=UpperCamelCase__					,							num_neighbors=UpperCamelCase__					,							distance_measure='''dot_product'''		)
					A__	:						List[Any]								=				builder.tree(
					    num_leaves=UpperCamelCase__					,							num_leaves_to_search=UpperCamelCase__					,							training_sample_size=UpperCamelCase__		)
					A__	:						int								=				builder.score_ah(dimensions_per_block=UpperCamelCase__		)

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





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__							,				A__	:						Union[str, Any]								=				self.searcher.search_batched(question_projection.detach().cpu()		)
					return retrieved_block_ids.astype('''int64'''		)







class 			UpperCamelCase__		:







	'''simple docstring'''





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__		):
					super().__init__()
					A__	:						Dict								=				block_records
					A__	:						Optional[Any]								=				tokenizer





	def __call__(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__=None					,							UpperCamelCase__="pt"		):
					A__	:						Optional[Any]								=				np.take(self.block_records					,							indices=UpperCamelCase__					,							axis=0		)

					A__	:						str								=				self.tokenizer.decode(question_input_ids[0]					,							skip_special_tokens=UpperCamelCase__		)

					A__	:						str								=				[]
					A__	:						str								=				[]
					for retrieved_block in retrieved_blocks:
									text.append(UpperCamelCase__		)
									text_pair.append(retrieved_block.decode()		)

					A__	:						List[Any]								=				self.tokenizer(
					    UpperCamelCase__					,							UpperCamelCase__					,							padding=UpperCamelCase__					,							truncation=UpperCamelCase__					,							return_special_tokens_mask=UpperCamelCase__					,							max_length=UpperCamelCase__		)
					A__	:						Optional[Any]								=				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 	__snake_case					(      cls					,							UpperCamelCase__					,							*UpperCamelCase__					,							**UpperCamelCase__		):
					if os.path.isdir(UpperCamelCase__		):
									A__	:						Optional[int]								=				os.path.join(UpperCamelCase__					,							_REALM_BLOCK_RECORDS_FILENAME		)
					else:
									A__	:						Optional[int]								=				hf_hub_download(
									    repo_id=UpperCamelCase__					,							filename=_REALM_BLOCK_RECORDS_FILENAME					,							**UpperCamelCase__		)
					A__	:						int								=				np.load(UpperCamelCase__					,							allow_pickle=UpperCamelCase__		)

					A__	:						Tuple								=				AutoTokenizer.from_pretrained(UpperCamelCase__					,							*UpperCamelCase__					,							**UpperCamelCase__		)

					return cls(UpperCamelCase__					,							UpperCamelCase__		)





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					# save block records
					np.save(os.path.join(UpperCamelCase__					,							_REALM_BLOCK_RECORDS_FILENAME		)					,							self.block_records		)
					# save tokenizer
					self.tokenizer.save_pretrained(UpperCamelCase__		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						str								=				[]
					A__	:						List[str]								=				[]
					A__	:						Union[str, Any]								=				[]
					A__	:						Union[str, Any]								=				0

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

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

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Dict				,    __UpperCamelCase			:    Dict							)			->							List[Any]:

				"""simple docstring"""







				if len(str(__UpperCamelCase							)							) == 1 or len(str(__UpperCamelCase							)							) == 1:
								return a * b

				A__	:						List[str]								=				max(len(str(__UpperCamelCase							)							)				,    len(str(__UpperCamelCase							)							)							)
				A__	:						Any								=				ma // 2

				A__							,				A__	:						Union[str, Any]								=				divmod(__UpperCamelCase				,    10**ma							)
				A__							,				A__	:						Tuple								=				divmod(__UpperCamelCase				,    10**ma							)

				A__	:						Optional[int]								=				karatsuba(__UpperCamelCase				,    __UpperCamelCase							)
				A__	:						Tuple								=				karatsuba((aa + aa)				,    (ba + ba)							)
				A__	:						Optional[int]								=				karatsuba(__UpperCamelCase				,    __UpperCamelCase							)

				return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)

def 					SCREAMING_SNAKE_CASE  (   )			->							Dict:

				"""simple docstring"""







				print(karatsuba(1_54_63				,    2_34_89							)							)


if __name__ == "__main__":
					main()