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






@dataclass
class    a__    :
   snake_case_							=      42
   snake_case_							=      42
   snake_case_							=      42






@dataclass
class    a__    :
   snake_case_							=      42
   snake_case_							=      42
   snake_case_							=      None
   snake_case_							=      None






class    a__    (   _a	):
   snake_case_							=      "train"
   snake_case_							=      "dev"
   snake_case_							=      "test"






class    a__    :


   @staticmethod
   def 	snake_case__	(     _UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        raise NotImplementedError


   @staticmethod
   def 	snake_case__	(     _UpperCAmelCase  ):
        '''simple docstring'''



        raise NotImplementedError


   @staticmethod
   def 	snake_case__	(     _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,				):
        '''simple docstring'''



        lowercase__				 =							{label: i for i, label in enumerate(_UpperCAmelCase  )}

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

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

             lowercase__				 =							tokenizer.convert_tokens_to_ids(_UpperCAmelCase  )

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

             # Zero-pad up to the sequence length.
             lowercase__				 =							max_seq_length - len(_UpperCAmelCase  )
             if pad_on_left:
                  lowercase__				 =							([pad_token] * padding_length) + input_ids
                  lowercase__				 =							([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                  lowercase__				 =							([pad_token_segment_id] * padding_length) + segment_ids
                  lowercase__				 =							([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:
                  lowercase__				 =							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    a__    (   _a	):
    snake_case_							=      42
    snake_case_							=      nn.CrossEntropyLoss().ignore_index


    def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase=False,				_UpperCAmelCase = Split.train,				):
         '''simple docstring'''



         lowercase__				 =							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.
         lowercase__				 =							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}'''  )
                   lowercase__				 =							torch.load(_UpperCAmelCase  )
              else:
                   logger.info(F'''Creating features from dataset file at {data_dir}'''  )
                   lowercase__				 =							token_classification_task.read_examples_from_file(_UpperCAmelCase,				_UpperCAmelCase  )
                   # TODO clean up all this to leverage built-in features of tokenizers
                   lowercase__				 =							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  ):
         '''simple docstring'''



         return len(self.features  )


    def __getitem__(     self,				_UpperCAmelCase  ):
         '''simple docstring'''



         return self.features[i]


if is_tf_available():
 import tensorflow as tf






 class    a__    :
    snake_case_							=      42
    snake_case_							=      -100


    def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase=False,				_UpperCAmelCase = Split.train,				):
         '''simple docstring'''



         lowercase__				 =							token_classification_task.read_examples_from_file(_UpperCAmelCase,				_UpperCAmelCase  )
         # TODO clean up all this to leverage built-in features of tokenizers
         lowercase__				 =							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:
              lowercase__				 =							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:
              lowercase__				 =							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 	snake_case__	(     self  ):
         '''simple docstring'''



         lowercase__				 =							self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

         return self.dataset


    def __len__(     self  ):
         '''simple docstring'''



         return len(self.features  )


    def __getitem__(     self,				_UpperCAmelCase  ):
         '''simple docstring'''



         return self.features[i]





"""simple docstring"""


import itertools
import math







def 					__a   (		A      ):






     '''simple docstring'''



     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5    ,				int(math.sqrt(A      ) + 1      )    ,				6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							2
     while True:
          if is_prime(A      ):
               yield num
          num += 1







def 					__a   (		A = 1_00_01      ):






     '''simple docstring'''



     return next(itertools.islice(prime_generator()    ,				nth - 1    ,				A      )      )


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




