






"""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


A__					: List[str]=	logging.getLogger(__name__)
@dataclass
class    __lowerCamelCase    :
   a      :				str
   a      :				List[str]
   a      :				Optional[List[str]]







@dataclass
class    __lowerCamelCase    :
   a      :				List[int]
   a      :				List[int]
   a      :				Optional[List[int]] 						=None
   a      :				Optional[List[int]] 						=None







class    __lowerCamelCase    (  _a  ):
   a      :				Optional[Any] 						="""train"""
   a      :				List[Any] 						="""dev"""
   a      :				Optional[Any] 						="""test"""







class    __lowerCamelCase    :

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_ ,				snake_case_      )		->       List[InputExample]:
       raise NotImplementedError

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       List[str]:
       raise NotImplementedError







   @staticmethod
   def 						SCREAMING_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]:
       UpperCamelCase__         =    {label: i for i, label in enumerate(snake_case_      )}

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

           UpperCamelCase__         =    []
           UpperCamelCase__         =    []
           for word, label in zip(example.words ,				example.labels      ):
               UpperCamelCase__         =    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.
           UpperCamelCase__         =    tokenizer.num_special_tokens_to_add()
           if len(snake_case_      ) > max_seq_length - special_tokens_count:
               UpperCamelCase__         =    tokens[: (max_seq_length - special_tokens_count)]
               UpperCamelCase__         =    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]
           UpperCamelCase__         =    [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:
               UpperCamelCase__         =    [cls_token] + tokens
               UpperCamelCase__         =    [pad_token_label_id] + label_ids
               UpperCamelCase__         =    [cls_token_segment_id] + segment_ids

           UpperCamelCase__         =    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.
           UpperCamelCase__         =    [1 if mask_padding_with_zero else 0] * len(snake_case_      )

           # Zero-pad up to the sequence length.
           UpperCamelCase__         =    max_seq_length - len(snake_case_      )
           if pad_on_left:
               UpperCamelCase__         =    ([pad_token] * padding_length) + input_ids
               UpperCamelCase__         =    ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
               UpperCamelCase__         =    ([pad_token_segment_id] * padding_length) + segment_ids
               UpperCamelCase__         =    ([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:
               UpperCamelCase__         =    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    (  _a  ):
     a      :				List[InputFeatures]
     a      :				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 ,				)		->       Tuple:
         # Load data features from cache or dataset file
         UpperCamelCase__         =    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.
         UpperCamelCase__         =    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}'      )
                 UpperCamelCase__         =    torch.load(snake_case_      )
             else:
                 logger.info(F'Creating features from dataset file at {data_dir}'      )
                 UpperCamelCase__         =    token_classification_task.read_examples_from_file(snake_case_ ,				snake_case_      )
                 # TODO clean up all this to leverage built-in features of tokenizers
                 UpperCamelCase__         =    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      )		->       int:
         return len(self.features      )







     def __getitem__(					self ,				snake_case_      )		->       InputFeatures:
         return self.features[i]


if is_tf_available():
  import tensorflow as tf
  class    __lowerCamelCase    :
     a      :				List[InputFeatures]
     a      :				int 						=-1_0_0

     def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ = None ,				snake_case_=False ,				snake_case_ = Split.train ,				)		->       List[Any]:
         UpperCamelCase__         =    token_classification_task.read_examples_from_file(snake_case_ ,				snake_case_      )
         # TODO clean up all this to leverage built-in features of tokenizers
         UpperCamelCase__         =    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:
             UpperCamelCase__         =    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:
             UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
         UpperCamelCase__         =    self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

         return self.dataset

     def __len__(					self      )		->       List[Any]:
         return len(self.features      )







     def __getitem__(					self ,				snake_case_      )		->       InputFeatures:
         return self.features[i]












"""simple docstring"""





def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	bool:
    """simple docstring"""

    UpperCamelCase__         =    (1 + 24 * n) ** 0.5
    return ((1 + root) / 6) % 1 == 0







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 50_00					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    [(i * (3 * i - 1)) // 2 for i in range(1 ,	SCREAMING_SNAKE_CASE					)]
    for i, pentagonal_i in enumerate(SCREAMING_SNAKE_CASE					):
        for j in range(SCREAMING_SNAKE_CASE ,	len(SCREAMING_SNAKE_CASE					)					):
            UpperCamelCase__         =    pentagonal_nums[j]
            UpperCamelCase__         =    pentagonal_i + pentagonal_j
            UpperCamelCase__         =    pentagonal_j - pentagonal_i
            if is_pentagonal(SCREAMING_SNAKE_CASE					) and is_pentagonal(SCREAMING_SNAKE_CASE					):
                return b

    return -1


if __name__ == "__main__":
  print(F"""{solution() = }""")




