


"""simple docstring"""






import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_lowerCamelCase     =  logging.getLogger(__name__)


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      str
				_lowerCAmelCase      :      List[str]
				_lowerCAmelCase      :      Optional[List[str]]


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      List[int]
				_lowerCAmelCase      :      List[int]
				_lowerCAmelCase      :      Optional[List[int]]      							=					None
				_lowerCAmelCase      :      Optional[List[int]]      							=					None


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					"train"
				_lowerCAmelCase      :      List[Any]      							=					"dev"
				_lowerCAmelCase      :      Any      							=					"test"


class 			lowerCamelCase_		:





				"""simple docstring"""







				@staticmethod
				def   lowerCAmelCase__		(						UpperCAmelCase__       ,		UpperCAmelCase__     ):
								raise NotImplementedError



				@staticmethod
				def   lowerCAmelCase__		(						UpperCAmelCase__     ):
								raise NotImplementedError






				@staticmethod
				def   lowerCAmelCase__		(						UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__="[CLS]"       ,		UpperCAmelCase__=1       ,		UpperCAmelCase__="[SEP]"       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__=0       ,		UpperCAmelCase__=0       ,		UpperCAmelCase__=-100       ,		UpperCAmelCase__=0       ,		UpperCAmelCase__=True       ,		):
								SCREAMING_SNAKE_CASE__     							=				{label: i for i, label in enumerate(UpperCAmelCase__     )}

								SCREAMING_SNAKE_CASE__     							=				[]
								for ex_index, example in enumerate(UpperCAmelCase__     ):
												if ex_index % 1_0000 == 0:
																logger.info("Writing example %d of %d"       ,		UpperCAmelCase__       ,		len(UpperCAmelCase__     )     )

												SCREAMING_SNAKE_CASE__     							=				[]
												SCREAMING_SNAKE_CASE__     							=				[]
												for word, label in zip(example.words       ,		example.labels     ):
																SCREAMING_SNAKE_CASE__     							=				tokenizer.tokenize(UpperCAmelCase__     )

																# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																if len(UpperCAmelCase__     ) > 0:
																				tokens.extend(UpperCAmelCase__     )
																				# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																				label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCAmelCase__     ) - 1)     )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
												SCREAMING_SNAKE_CASE__     							=				tokenizer.num_special_tokens_to_add()
												if len(UpperCAmelCase__     ) > max_seq_length - special_tokens_count:
																SCREAMING_SNAKE_CASE__     							=				tokens[: (max_seq_length - special_tokens_count)]
																SCREAMING_SNAKE_CASE__     							=				label_ids[: (max_seq_length - special_tokens_count)]

												# The convention in BERT is:
												# (a) For sequence pairs:
												#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
												#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
												# (b) For single sequences:
												#  tokens:   [CLS] the dog is hairy . [SEP]
												#  type_ids:   0   0   0   0  0     0   0
												#
												# Where "type_ids" are used to indicate whether this is the first
												# sequence or the second sequence. The embedding vectors for `type=0` and
												# `type=1` were learned during pre-training and are added to the wordpiece
												# embedding vector (and position vector). This is not *strictly* necessary
												# since the [SEP] token unambiguously separates the sequences, but it makes
												# it easier for the model to learn the concept of sequences.
												#
												# For classification tasks, the first vector (corresponding to [CLS]) is
												# used as the "sentence vector". Note that this only makes sense because
												# the entire model is fine-tuned.
												tokens += [sep_token]
												label_ids += [pad_token_label_id]
												if sep_token_extra:
																# roberta uses an extra separator b/w pairs of sentences
																tokens += [sep_token]
																label_ids += [pad_token_label_id]
												SCREAMING_SNAKE_CASE__     							=				[sequence_a_segment_id] * len(UpperCAmelCase__     )

												if cls_token_at_end:
																tokens += [cls_token]
																label_ids += [pad_token_label_id]
																segment_ids += [cls_token_segment_id]
												else:
																SCREAMING_SNAKE_CASE__     							=				[cls_token] + tokens
																SCREAMING_SNAKE_CASE__     							=				[pad_token_label_id] + label_ids
																SCREAMING_SNAKE_CASE__     							=				[cls_token_segment_id] + segment_ids

												SCREAMING_SNAKE_CASE__     							=				tokenizer.convert_tokens_to_ids(UpperCAmelCase__     )

												# The mask has 1 for real tokens and 0 for padding tokens. Only real
												# tokens are attended to.
												SCREAMING_SNAKE_CASE__     							=				[1 if mask_padding_with_zero else 0] * len(UpperCAmelCase__     )

												# Zero-pad up to the sequence length.
												SCREAMING_SNAKE_CASE__     							=				max_seq_length - len(UpperCAmelCase__     )
												if pad_on_left:
																SCREAMING_SNAKE_CASE__     							=				([pad_token] * padding_length) + input_ids
																SCREAMING_SNAKE_CASE__     							=				([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																SCREAMING_SNAKE_CASE__     							=				([pad_token_segment_id] * padding_length) + segment_ids
																SCREAMING_SNAKE_CASE__     							=				([pad_token_label_id] * padding_length) + label_ids
												else:
																input_ids += [pad_token] * padding_length
																input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																segment_ids += [pad_token_segment_id] * padding_length
																label_ids += [pad_token_label_id] * padding_length

												assert len(UpperCAmelCase__     ) == max_seq_length
												assert len(UpperCAmelCase__     ) == max_seq_length
												assert len(UpperCAmelCase__     ) == max_seq_length
												assert len(UpperCAmelCase__     ) == max_seq_length

												if ex_index < 5:
																logger.info("*** Example ***"     )
																logger.info("guid: %s"       ,		example.guid     )
																logger.info("tokens: %s"       ,		" ".join([str(UpperCAmelCase__     ) for x in tokens]     )     )
																logger.info("input_ids: %s"       ,		" ".join([str(UpperCAmelCase__     ) for x in input_ids]     )     )
																logger.info("input_mask: %s"       ,		" ".join([str(UpperCAmelCase__     ) for x in input_mask]     )     )
																logger.info("segment_ids: %s"       ,		" ".join([str(UpperCAmelCase__     ) for x in segment_ids]     )     )
																logger.info("label_ids: %s"       ,		" ".join([str(UpperCAmelCase__     ) for x in label_ids]     )     )

												if "token_type_ids" not in tokenizer.model_input_names:
																SCREAMING_SNAKE_CASE__     							=				None

												features.append(
												    InputFeatures(
												        input_ids=UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__       ,		token_type_ids=UpperCAmelCase__       ,		label_ids=UpperCAmelCase__     )     )
								return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset


	class 			lowerCamelCase_		(			lowercase   ):





					"""simple docstring"""







					_lowerCAmelCase      :      List[InputFeatures]
					_lowerCAmelCase      :      int      							=					nn.CrossEntropyLoss().ignore_index



					def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__ = Split.train       ,		):
									# Load data features from cache or dataset file
									SCREAMING_SNAKE_CASE__     							=				os.path.join(
									    UpperCAmelCase__       ,		"cached_{}_{}_{}".format(mode.value       ,		tokenizer.__class__.__name__       ,		str(UpperCAmelCase__     )     )       ,		)

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									SCREAMING_SNAKE_CASE__     							=				cached_features_file + ".lock"
									with FileLock(UpperCAmelCase__     ):
													if os.path.exists(UpperCAmelCase__     ) and not overwrite_cache:
																	logger.info(f'''Loading features from cached file {cached_features_file}'''     )
																	SCREAMING_SNAKE_CASE__     							=				torch.load(UpperCAmelCase__     )
													else:
																	logger.info(f'''Creating features from dataset file at {data_dir}'''     )
																	SCREAMING_SNAKE_CASE__     							=				token_classification_task.read_examples_from_file(UpperCAmelCase__       ,		UpperCAmelCase__     )
																	# TODO clean up all this to leverage built-in features of tokenizers
																	SCREAMING_SNAKE_CASE__     							=				token_classification_task.convert_examples_to_features(
																	    UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		cls_token_at_end=bool(model_type in ["xlnet"]     )       ,		cls_token=tokenizer.cls_token       ,		cls_token_segment_id=2 if model_type in ["xlnet"] else 0       ,		sep_token=tokenizer.sep_token       ,		sep_token_extra=UpperCAmelCase__       ,		pad_on_left=bool(tokenizer.padding_side == "left"     )       ,		pad_token=tokenizer.pad_token_id       ,		pad_token_segment_id=tokenizer.pad_token_type_id       ,		pad_token_label_id=self.pad_token_label_id       ,		)
																	logger.info(f'''Saving features into cached file {cached_features_file}'''     )
																	torch.save(self.features       ,		UpperCAmelCase__     )



					def __len__(						self     ):
									return len(self.features     )






					def __getitem__(						self       ,		UpperCAmelCase__     ):
									return self.features[i]


if is_tf_available():
	import tensorflow as tf


	class 			lowerCamelCase_		:





					"""simple docstring"""







					_lowerCAmelCase      :      List[InputFeatures]
					_lowerCAmelCase      :      int      							=					-100



					def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__ = Split.train       ,		):
									SCREAMING_SNAKE_CASE__     							=				token_classification_task.read_examples_from_file(UpperCAmelCase__       ,		UpperCAmelCase__     )
									# TODO clean up all this to leverage built-in features of tokenizers
									SCREAMING_SNAKE_CASE__     							=				token_classification_task.convert_examples_to_features(
									    UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		cls_token_at_end=bool(model_type in ["xlnet"]     )       ,		cls_token=tokenizer.cls_token       ,		cls_token_segment_id=2 if model_type in ["xlnet"] else 0       ,		sep_token=tokenizer.sep_token       ,		sep_token_extra=UpperCAmelCase__       ,		pad_on_left=bool(tokenizer.padding_side == "left"     )       ,		pad_token=tokenizer.pad_token_id       ,		pad_token_segment_id=tokenizer.pad_token_type_id       ,		pad_token_label_id=self.pad_token_label_id       ,		)

									def gen():
													for ex in self.features:
																	if ex.token_type_ids is None:
																					yield (
																					    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																					    ex.label_ids,
																					)
																	else:
																					yield (
																					    {
																					        "input_ids": ex.input_ids,
																					        "attention_mask": ex.attention_mask,
																					        "token_type_ids": ex.token_type_ids,
																					    },
																					    ex.label_ids,
																					)

									if "token_type_ids" not in tokenizer.model_input_names:
													SCREAMING_SNAKE_CASE__     							=				tf.data.Dataset.from_generator(
													    UpperCAmelCase__       ,		({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)       ,		(
													        {"input_ids": tf.TensorShape([None]     ), "attention_mask": tf.TensorShape([None]     )},
													        tf.TensorShape([None]     ),
													    )       ,		)
									else:
													SCREAMING_SNAKE_CASE__     							=				tf.data.Dataset.from_generator(
													    UpperCAmelCase__       ,		({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)       ,		(
													        {
													            "input_ids": tf.TensorShape([None]     ),
													            "attention_mask": tf.TensorShape([None]     ),
													            "token_type_ids": tf.TensorShape([None]     ),
													        },
													        tf.TensorShape([None]     ),
													    )       ,		)



					def   lowerCAmelCase__		(						self     ):
									SCREAMING_SNAKE_CASE__     							=				self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features     )     )     )

									return self.dataset



					def __len__(						self     ):
									return len(self.features     )






					def __getitem__(						self       ,		UpperCAmelCase__     ):
									return self.features[i]




"""simple docstring"""






def __lowercase     (      lowerCamelCase_  :      List[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				set({"(", "[", "{"}  )
				SCREAMING_SNAKE_CASE__     							=				set({")", "]", "}"}  )
				SCREAMING_SNAKE_CASE__     							=				{"{": "}", "[": "]", "(": ")"}

				for i in range(len(lowerCamelCase_  )  ):
								if s[i] in open_brackets:
												stack.append(s[i]  )

								elif s[i] in closed_brackets and (
								    len(lowerCamelCase_  ) == 0 or (len(lowerCamelCase_  ) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(lowerCamelCase_  ) == 0


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				input("Enter sequence of brackets: "  )
				if is_balanced(lowerCamelCase_  ):
								print(lowerCamelCase_						,					"is balanced"  )
				else:
								print(lowerCamelCase_						,					"is not balanced"  )


if __name__ == "__main__":
	main()
