




'''simple docstring'''





import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger




class 			_snake_case		(     lowercase_							):




			def __init__( self    ,							a__    ,							a__							)    ->    int:

										'''simple docstring'''




										snake_case_      =				params

										snake_case_      =				np.array(a__							)
										snake_case_      =				np.array([len(a__							) for t in data]							)

										self.check()
										self.remove_long_sequences()
										self.remove_empty_sequences()
										self.remove_unknown_sequences()
										self.check()
										self.print_statistics()




			def __getitem__( self    ,							a__							)    ->    List[Any]:

										'''simple docstring'''




										return (self.token_ids[index], self.lengths[index])




			def __len__( self							)    ->    Optional[int]:

										'''simple docstring'''




										return len(self.lengths							)




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										assert len(self.token_ids							) == len(self.lengths							)
										assert all(self.lengths[i] == len(self.token_ids[i]							) for i in range(len(self.lengths							)							)							)




			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				self.params.max_model_input_size
										snake_case_      =				self.lengths > max_len
										logger.info(F'Splitting {sum(a__							)} too long sequences.'							)

										def divide_chunks(a__    ,							a__							):
																	return [l[i : i + n] for i in range(0    ,							len(a__							)    ,							a__							)]

										snake_case_      =				[]
										snake_case_      =				[]
										if self.params.mlm:
																	snake_case_ ,				snake_case_      =				self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
										else:
																	snake_case_ ,				snake_case_      =				self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

										for seq_, len_ in zip(self.token_ids    ,							self.lengths							):
																	assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
																	if len_ <= max_len:
																								new_tok_ids.append(seq_							)
																								new_lengths.append(len_							)
																	else:
																								snake_case_      =				[]
																								for sub_s in divide_chunks(seq_    ,							max_len - 2							):
																															if sub_s[0] != cls_id:
																																						snake_case_      =				np.insert(a__    ,							0    ,							a__							)
																															if sub_s[-1] != sep_id:
																																						snake_case_      =				np.insert(a__    ,							len(a__							)    ,							a__							)
																															assert len(a__							) <= max_len
																															assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																															sub_seqs.append(a__							)

																								new_tok_ids.extend(a__							)
																								new_lengths.extend([len(a__							) for l in sub_seqs]							)

										snake_case_      =				np.array(a__							)
										snake_case_      =				np.array(a__							)




			def        lowerCAmelCase__    ( self							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				len(self							)
										snake_case_      =				self.lengths > 11
										snake_case_      =				self.token_ids[indices]
										snake_case_      =				self.lengths[indices]
										snake_case_      =				len(self							)
										logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.'							)




			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										if "unk_token" not in self.params.special_tok_ids:
																	return
										else:
																	snake_case_      =				self.params.special_tok_ids["unk_token"]
										snake_case_      =				len(self							)
										snake_case_      =				np.array([np.count_nonzero(a == unk_token_id							) for a in self.token_ids]							)
										snake_case_      =				(unk_occs / self.lengths) < 0.5
										snake_case_      =				self.token_ids[indices]
										snake_case_      =				self.lengths[indices]
										snake_case_      =				len(self							)
										logger.info(F'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'							)




			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										if not self.params.is_master:
																	return
										logger.info(F'{len(self							)} sequences'							)
										# data_len = sum(self.lengths)
										# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
										# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

										# unk_idx = self.params.special_tok_ids['unk_token']
										# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
										# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				[t[0] for t in batch]
										snake_case_      =				[t[1] for t in batch]
										assert len(a__							) == len(a__							)

										# Max for paddings
										snake_case_      =				max(a__							)

										# Pad token ids
										if self.params.mlm:
																	snake_case_      =				self.params.special_tok_ids["pad_token"]
										else:
																	snake_case_      =				self.params.special_tok_ids["unk_token"]
										snake_case_      =				[list(t.astype(a__							)							) + [pad_idx] * (max_seq_len_ - len(a__							)) for t in token_ids]
										assert len(tk_							) == len(a__							)
										assert all(len(a__							) == max_seq_len_ for t in tk_							)

										snake_case_      =				torch.tensor(tk_							)  # (bs, max_seq_len_)
										snake_case_      =				torch.tensor(a__							)  # (bs)
										return tk_t, lg_t










'''simple docstring'''





_SCREAMING_SNAKE_CASE							:       Optional[int]						=			"Alexander Joslin"

import operator as op

from .stack import Stack


def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				{"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

							snake_case_      =				Stack()
							snake_case_      =				Stack()

							for i in equation:
														if i.isdigit():
																					# RULE 1
																					operand_stack.push(int(snake_case					)					)
														elif i in operators:
																					# RULE 2
																					operator_stack.push(snake_case					)
														elif i == ")":
																					# RULE 4
																					snake_case_      =				operator_stack.peek()
																					operator_stack.pop()
																					snake_case_      =				operand_stack.peek()
																					operand_stack.pop()
																					snake_case_      =				operand_stack.peek()
																					operand_stack.pop()

																					snake_case_      =				operators[opr](snake_case							,    snake_case					)
																					operand_stack.push(snake_case					)

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       List[Any]						=			"(5 + ((4 * 2) * (2 + 3)))"
		# answer = 45
		print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")




