




"""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


_lowercase						   =     """block_records.npy"""


_lowercase						   =     logging.get_logger(__name__)

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						import tensorflow.compat.va as tf

						snake_case__							:       Any				      =       tf.data.TFRecordDataset(SCREAMING_SNAKE_CASE__   ,      buffer_size=512 * 1024 * 1024		)
						snake_case__							:       Dict				      =       blocks_dataset.batch(SCREAMING_SNAKE_CASE__   ,      drop_remainder=SCREAMING_SNAKE_CASE__		)
						snake_case__							:       Any				      =       next(blocks_dataset.take(1		).as_numpy_iterator()		)

						return np_record

class    __lowercase							:


				'''simple docstring'''


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

										snake_case__							:       List[str]				      =       Builder(db=lowercase_   ,num_neighbors=lowercase_   ,distance_measure='''dot_product'''   )
										snake_case__							:       Tuple				      =       builder.tree(
										    num_leaves=lowercase_   ,num_leaves_to_search=lowercase_   ,training_sample_size=lowercase_   )
										snake_case__							:       str				      =       builder.score_ah(dimensions_per_block=lowercase_   )

										snake_case__							:       Optional[Any]				      =       builder.build()





				def    _lowerCamelCase       ( self :  Any   ,SCREAMING_SNAKE_CASE_ :  Tuple   ):
										snake_case__      ,						snake_case__							:       Any				      =       self.searcher.search_batched(question_projection.detach().cpu()   )
										return retrieved_block_ids.astype('''int64'''   )







class    __lowercase							:


				'''simple docstring'''


				def __init__( self :  str   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  str   ):
										super().__init__()
										snake_case__							:       Dict				      =       block_records
										snake_case__							:       str				      =       tokenizer

				def __call__( self :  List[Any]   ,SCREAMING_SNAKE_CASE_ :  Dict   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  Union[str, Any]   ,SCREAMING_SNAKE_CASE_ :  str=None   ,SCREAMING_SNAKE_CASE_ :  List[str]="pt"   ):
										snake_case__							:       Any				      =       np.take(self.block_records   ,indices=lowercase_   ,axis=0   )

										snake_case__							:       List[Any]				      =       self.tokenizer.decode(question_input_ids[0]   ,skip_special_tokens=lowercase_   )

										snake_case__							:       Optional[Any]				      =       []
										snake_case__							:       int				      =       []
										for retrieved_block in retrieved_blocks:
																text.append(lowercase_   )
																text_pair.append(retrieved_block.decode()   )

										snake_case__							:       Optional[int]				      =       self.tokenizer(
										    lowercase_   ,lowercase_   ,padding=lowercase_   ,truncation=lowercase_   ,return_special_tokens_mask=lowercase_   ,max_length=lowercase_   )
										snake_case__							:       Dict				      =       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    _lowerCamelCase       ( cls :  Dict   ,SCREAMING_SNAKE_CASE_ :  Optional[Union[str, os.PathLike]]   ,*SCREAMING_SNAKE_CASE_ :  str   ,**SCREAMING_SNAKE_CASE_ :  Dict   ):







										if os.path.isdir(lowercase_   ):
																snake_case__							:       Optional[int]				      =       os.path.join(lowercase_   ,_REALM_BLOCK_RECORDS_FILENAME   )
										else:
																snake_case__							:       List[str]				      =       hf_hub_download(
																    repo_id=lowercase_   ,filename=_REALM_BLOCK_RECORDS_FILENAME   ,**lowercase_   )
										snake_case__							:       Tuple				      =       np.load(lowercase_   ,allow_pickle=lowercase_   )

										snake_case__							:       List[Any]				      =       AutoTokenizer.from_pretrained(lowercase_   ,*lowercase_   ,**lowercase_   )

										return cls(lowercase_   ,lowercase_   )

				def    _lowerCamelCase       ( self :  List[str]   ,SCREAMING_SNAKE_CASE_ :  str   ):
										np.save(os.path.join(lowercase_   ,_REALM_BLOCK_RECORDS_FILENAME   )   ,self.block_records   )
										# save tokenizer
										self.tokenizer.save_pretrained(lowercase_   )





				def    _lowerCamelCase       ( self :  Union[str, Any]   ,SCREAMING_SNAKE_CASE_ :  int   ,SCREAMING_SNAKE_CASE_ :  Union[str, Any]   ):
										snake_case__							:       Tuple				      =       []
										snake_case__							:       Dict				      =       []
										snake_case__							:       Dict				      =       []
										snake_case__							:       int				      =       0

										for input_id in concat_inputs.input_ids:
																snake_case__							:       str				      =       input_id.tolist()
																# Check answers between two [SEP] tokens
																snake_case__							:       str				      =       input_id_list.index(self.tokenizer.sep_token_id   )
																snake_case__							:       Union[str, Any]				      =       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__							:       List[Any]				      =       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__							:       List[str]				      =       [-1] * (max_answers - len(start_pos_   ))
																						start_pos_ += padded
																						end_pos_ += padded
										return has_answers, start_pos, end_pos





import timeit


class 					A		:







    """simple docstring"""





    def __init__( self					:			Optional[int],lowercase_					:			Union[str, Any]						)->       List[str]:





        '''simple docstring'''







        if isinstance(lowercase_,lowercase_						):  # Initializes a matrix identical to the one provided.
            A__        =  arg
            A__        =  len(lowercase_						)
        else:  # Initializes a square matrix of the given size and set values to zero.
            A__        =  arg
            A__        =  [[0 for _ in range(self.n						)] for _ in range(self.n						)]


    def __mul__( self					:			Union[str, Any],lowercase_					:			List[Any]						)->       List[str]:





        '''simple docstring'''







        A__        =  Matrix(self.n						)
        for i in range(self.n						):
            for j in range(self.n						):
                for k in range(self.n						):
                    matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix




def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[Any]       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any] )				-> Union[str, Any]:





    '''simple docstring'''






    A__        =  Matrix([[1, 0], [0, 1]] )
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      Any )				-> List[str]:





    '''simple docstring'''






    if n == 1:
        return fa
    elif n == 2:
        return fa
    A__        =  Matrix([[1, 1], [1, 0]] )
    A__        =  modular_exponentiation(SCREAMING_SNAKE_CASE__       ,     n - 2 )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      Tuple       ,     SCREAMING_SNAKE_CASE__     :      Tuple )				-> Dict:





    '''simple docstring'''






    if n == 1:
        return fa
    elif n == 2:
        return fa

    A__        =  fa
    A__        =  fa
    n -= 2

    while n > 0:
        A__ ,		A__        =  fn_a + fn_a, fn_a
        n -= 1

    return fn_a
def 		_snake_case(       )				-> Optional[Any]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n'
    A__        =  'fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print('With matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Optional[int]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import simple_fibonacci\n'
    A__        =  'simple_fibonacci(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print(
        'Without matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Union[str, Any]:





    '''simple docstring'''






    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
     main()

