

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 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						import tensorflow.compat.va as tf

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

						return np_record





class 			snake_case_     :







			'''simple docstring'''

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

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

									snake_case_     =			builder.build()







			def        snake_case__(  self				:       List[str]							,     _UpperCamelCase				:       int							)   ->Dict:
									snake_case_     =			self.searcher.search_batched(question_projection.detach().cpu()							)
									return retrieved_block_ids.astype('''int64'''							)






class 			snake_case_     :







			'''simple docstring'''

			def __init__(  self				:       str							,     _UpperCamelCase				:       Any							,     _UpperCamelCase				:       List[Any]							)   ->Tuple:
									super().__init__()
									snake_case_     =			block_records
									snake_case_     =			tokenizer







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

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

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

									snake_case_     =			self.tokenizer(
									    lowercase_							,     lowercase_							,     padding=lowercase_							,     truncation=lowercase_							,     return_special_tokens_mask=lowercase_							,     max_length=lowercase_							)
									snake_case_     =			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        snake_case__(  cls				:       Any							,     _UpperCamelCase				:       Tuple							,     *_UpperCamelCase				:       Optional[int]							,     **_UpperCamelCase				:       List[Any]							)   ->str:

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

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

									return cls(lowercase_							,     lowercase_							)







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







			def        snake_case__(  self				:       str							,     _UpperCamelCase				:       Optional[int]							,     _UpperCamelCase				:       Tuple							)   ->Any:
									snake_case_     =			[]
									snake_case_     =			[]
									snake_case_     =			[]
									snake_case_     =			0

									for input_id in concat_inputs.input_ids:
															snake_case_     =			input_id.tolist()
															# Check answers between two [SEP] tokens
															snake_case_     =			input_id_list.index(self.tokenizer.sep_token_id							)
															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							,     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:
																											snake_case_     =			len(start_pos[-1]							)

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


lowerCAmelCase_ 			=			(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
lowerCAmelCase_ 			=			(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False

						# Initialization of sum and number of digits.
						snake_case_     =			0
						snake_case_     =			0
						snake_case_     =			n
						# Calculation of digits of the number
						while temp > 0:
												number_of_digits += 1
												temp //= 10
						# Dividing number into separate digits and find Armstrong number
						snake_case_     =			n
						while temp > 0:
												snake_case_     =			temp % 10
												total += rem**number_of_digits
												temp //= 10
						return n == total


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False

						# Init a "histogram" of the digits
						snake_case_     =			[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
						snake_case_     =			0
						snake_case_     =			0
						snake_case_     =			n
						while temp > 0:
												snake_case_,					snake_case_     =			divmod(SCREAMING_SNAKE_CASE__						,	10 )
												digit_histogram[rem] += 1
												digit_total += 1

						for cnt, i in zip(SCREAMING_SNAKE_CASE__						,	range(len(SCREAMING_SNAKE_CASE__ ) ) ):
												total += cnt * i**digit_total

						return n == total


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False
						snake_case_     =			len(str(SCREAMING_SNAKE_CASE__ ) )  # the power that all digits will be raised to
						# check if sum of each digit multiplied expo times is equal to number
						return n == sum(int(SCREAMING_SNAKE_CASE__ ) ** expo for i in str(SCREAMING_SNAKE_CASE__ ) )


def 						__SCREAMING_SNAKE_CASE      ():
						snake_case_     =			int(input('''Enter an integer to see if it is an Armstrong number: ''' ).strip() )
						print(F'''{num} is {'' if armstrong_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )
						print(F'''{num} is {'' if narcissistic_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )
						print(F'''{num} is {'' if pluperfect_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()