





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


SCREAMING_SNAKE_CASE__							:      List[Any]         =    logging.getLogger(__name__)







@dataclass
class 	__lowerCAmelCase      :
   _UpperCamelCase			:    str
   _UpperCamelCase			:    List[str]
   _UpperCamelCase			:    Optional[List[str]]







@dataclass
class 	__lowerCAmelCase      :
   _UpperCamelCase			:    List[int]
   _UpperCamelCase			:    List[int]
   _UpperCamelCase			:    Optional[List[int]]  		=					None
   _UpperCamelCase			:    Optional[List[int]]  		=					None







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Union[str, Any]  		=					"""train"""
   _UpperCamelCase			:    Tuple  		=					"""dev"""
   _UpperCamelCase			:    List[Any]  		=					"""test"""







class 	__lowerCAmelCase      :





   @staticmethod
   def 						_snake_case			(			snake_case       ,      snake_case   )					->	List[InputExample]:
      """simple docstring"""





      raise NotImplementedError





   @staticmethod
   def 						_snake_case			(			snake_case   )					->	List[str]:
      """simple docstring"""





      raise NotImplementedError





   @staticmethod
   def 						_snake_case			(			snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case=False       ,      snake_case="[CLS]"       ,      snake_case=1       ,      snake_case="[SEP]"       ,      snake_case=False       ,      snake_case=False       ,      snake_case=0       ,      snake_case=0       ,      snake_case=-100       ,      snake_case=0       ,      snake_case=True       ,      )					->	List[InputFeatures]:
      """simple docstring"""





      a__						:    str   		=	{label: i for i, label in enumerate(snake_case   )}

      a__						:    Dict   		=	[]
      for ex_index, example in enumerate(snake_case   ):
         if ex_index % 10_000 == 0:
            logger.info("Writing example %d of %d"       ,      snake_case       ,      len(snake_case   )   )

         a__						:    Any   		=	[]
         a__						:    Optional[Any]   		=	[]
         for word, label in zip(example.words       ,      example.labels   ):
            a__						:    Dict   		=	tokenizer.tokenize(snake_case   )

            # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
            if len(snake_case   ) > 0:
               tokens.extend(snake_case   )
               # 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(snake_case   ) - 1)   )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
         a__						:    Dict   		=	tokenizer.num_special_tokens_to_add()
         if len(snake_case   ) > max_seq_length - special_tokens_count:
            a__						:    Any   		=	tokens[: (max_seq_length - special_tokens_count)]
            a__						:    str   		=	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]
         a__						:    List[Any]   		=	[sequence_a_segment_id] * len(snake_case   )

         if cls_token_at_end:
            tokens += [cls_token]
            label_ids += [pad_token_label_id]
            segment_ids += [cls_token_segment_id]
         else:
            a__						:    Dict   		=	[cls_token] + tokens
            a__						:    Dict   		=	[pad_token_label_id] + label_ids
            a__						:    Optional[int]   		=	[cls_token_segment_id] + segment_ids

         a__						:    Optional[Any]   		=	tokenizer.convert_tokens_to_ids(snake_case   )

         # The mask has 1 for real tokens and 0 for padding tokens. Only real
         # tokens are attended to.
         a__						:    List[Any]   		=	[1 if mask_padding_with_zero else 0] * len(snake_case   )

         # Zero-pad up to the sequence length.
         a__						:    Optional[int]   		=	max_seq_length - len(snake_case   )
         if pad_on_left:
            a__						:    List[Any]   		=	([pad_token] * padding_length) + input_ids
            a__						:    int   		=	([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
            a__						:    Dict   		=	([pad_token_segment_id] * padding_length) + segment_ids
            a__						:    Optional[Any]   		=	([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(snake_case   ) == max_seq_length
         assert len(snake_case   ) == max_seq_length
         assert len(snake_case   ) == max_seq_length
         assert len(snake_case   ) == max_seq_length

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

         if "token_type_ids" not in tokenizer.model_input_names:
            a__						:    Tuple   		=	None

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


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







  class 	__lowerCAmelCase      (       _UpperCamelCase	):
     _UpperCamelCase			:    List[InputFeatures]
     _UpperCamelCase			:    int  		=					nn.CrossEntropyLoss().ignore_index





     def __init__(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      snake_case=False       ,      snake_case = Split.train       ,      )					->	str:
        """simple docstring"""





        a__						:    List[str]   		=	os.path.join(
            snake_case       ,      "cached_{}_{}_{}".format(mode.value       ,      tokenizer.__class__.__name__       ,      str(snake_case   )   )       ,      )

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        a__						:    Dict   		=	cached_features_file + ".lock"
        with FileLock(snake_case   ):
           if os.path.exists(snake_case   ) and not overwrite_cache:
              logger.info(F"""Loading features from cached file {cached_features_file}"""   )
              a__						:    str   		=	torch.load(snake_case   )
           else:
              logger.info(F"""Creating features from dataset file at {data_dir}"""   )
              a__						:    Union[str, Any]   		=	token_classification_task.read_examples_from_file(snake_case       ,      snake_case   )
              # TODO clean up all this to leverage built-in features of tokenizers
              a__						:    Dict   		=	token_classification_task.convert_examples_to_features(
                  snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      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=snake_case       ,      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       ,      snake_case   )





     def __len__(			self   )					->	Union[str, Any]:
        """simple docstring"""





        return len(self.features   )





     def __getitem__(			self       ,      snake_case   )					->	InputFeatures:
        """simple docstring"""





        return self.features[i]


if is_tf_available():
  import tensorflow as tf







  class 	__lowerCAmelCase      :
     _UpperCamelCase			:    List[InputFeatures]
     _UpperCamelCase			:    int  		=					-100





     def __init__(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      snake_case=False       ,      snake_case = Split.train       ,      )					->	str:
        """simple docstring"""





        a__						:    Dict   		=	token_classification_task.read_examples_from_file(snake_case       ,      snake_case   )
        # TODO clean up all this to leverage built-in features of tokenizers
        a__						:    Optional[int]   		=	token_classification_task.convert_examples_to_features(
            snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      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=snake_case       ,      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:
           a__						:    Union[str, Any]   		=	tf.data.Dataset.from_generator(
               snake_case       ,      ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)       ,      (
                   {"input_ids": tf.TensorShape([None]   ), "attention_mask": tf.TensorShape([None]   )},
                   tf.TensorShape([None]   ),
               )       ,      )
        else:
           a__						:    str   		=	tf.data.Dataset.from_generator(
               snake_case       ,      ({"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 						_snake_case			(			self   )					->	Tuple:
        """simple docstring"""





        a__						:    List[str]   		=	self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

        return self.dataset





     def __len__(			self   )					->	List[Any]:
        """simple docstring"""





        return len(self.features   )





     def __getitem__(			self       ,      snake_case   )					->	InputFeatures:
        """simple docstring"""





        return self.features[i]








def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	[]
   a__						:    str   		=	set({"(", "[", "{"} )
   a__						:    List[str]   		=	set({")", "]", "}"} )
   a__						:    int   		=	{"{": "}", "[": "]", "(": ")"}

   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    _A	(   ):
   a__						:    Optional[int]   		=	input("Enter sequence of brackets: " )
   if is_balanced(lowerCamelCase ):
      print(lowerCamelCase				,			"is balanced" )
   else:
      print(lowerCamelCase				,			"is not balanced" )


if __name__ == "__main__":
  main()

