





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__:    Union[str, Any]  	=	logging.getLogger(__name__)




@dataclass
class        _a    :

      """simple docstring"""


      UpperCamelCase__  		=       42
      UpperCamelCase__  		=       42
      UpperCamelCase__  		=       42




@dataclass
class        _a    :

      """simple docstring"""


      UpperCamelCase__  		=       42
      UpperCamelCase__  		=       42
      UpperCamelCase__  		=       None
      UpperCamelCase__  		=       None




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       """train"""
      UpperCamelCase__  		=       """dev"""
      UpperCamelCase__  		=       """test"""




class        _a    :

      """simple docstring"""


      @staticmethod
      def 			UpperCAmelCase_       (		__lowerCamelCase:			Union[str, Any]	,    __lowerCamelCase:			Union[Split, str]					):
          '''simple docstring'''


          raise NotImplementedError






      @staticmethod
      def 			UpperCAmelCase_       (		__lowerCamelCase:			str					):
          '''simple docstring'''


          raise NotImplementedError






      @staticmethod
      def 			UpperCAmelCase_       (		__lowerCamelCase:			List[InputExample]	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			int	,    __lowerCamelCase:			PreTrainedTokenizer	,    __lowerCamelCase:			Tuple=False	,    __lowerCamelCase:			str="[CLS]"	,    __lowerCamelCase:			Any=1	,    __lowerCamelCase:			Dict="[SEP]"	,    __lowerCamelCase:			List[Any]=False	,    __lowerCamelCase:			Dict=False	,    __lowerCamelCase:			str=0	,    __lowerCamelCase:			Tuple=0	,    __lowerCamelCase:			List[str]=-100	,    __lowerCamelCase:			Tuple=0	,    __lowerCamelCase:			List[Any]=True	,    ):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     {label: i for i, label in enumerate(__lowerCamelCase					)}

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

              UpperCamelCase__:	Tuple					=     []
              UpperCamelCase__:	List[str]					=     []
              for word, label in zip(example.words	,    example.labels					):
                  UpperCamelCase__:	Tuple					=     tokenizer.tokenize(__lowerCamelCase					)

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

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

              if cls_token_at_end:
                  tokens += [cls_token]
                  label_ids += [pad_token_label_id]
                  segment_ids += [cls_token_segment_id]
              else:
                  UpperCamelCase__:	List[str]					=     [cls_token] + tokens
                  UpperCamelCase__:	Optional[Any]					=     [pad_token_label_id] + label_ids
                  UpperCamelCase__:	List[str]					=     [cls_token_segment_id] + segment_ids

              UpperCamelCase__:	List[str]					=     tokenizer.convert_tokens_to_ids(__lowerCamelCase					)

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

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

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

              if "token_type_ids" not in tokenizer.model_input_names:
                  UpperCamelCase__:	str					=     None

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


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




   class        _a    (    UpperCamelCase__):

         """simple docstring"""


         UpperCamelCase__  		=       42
         UpperCamelCase__  		=       nn.CrossEntropyLoss().ignore_index






         def __init__(		self:			List[Any]	,    __lowerCamelCase:			TokenClassificationTask	,    __lowerCamelCase:			str	,    __lowerCamelCase:			PreTrainedTokenizer	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			str	,    __lowerCamelCase:			Optional[int] = None	,    __lowerCamelCase:			str=False	,    __lowerCamelCase:			Split = Split.train	,    ):
             '''simple docstring'''


             UpperCamelCase__:	int					=     os.path.join(
                 __lowerCamelCase	,    "cached_{}_{}_{}".format(mode.value	,    tokenizer.__class__.__name__	,    str(__lowerCamelCase					)					)	,    )

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






         def __len__(		self:			List[str]					):
             '''simple docstring'''


             return len(self.features					)






         def __getitem__(		self:			Optional[Any]	,    __lowerCamelCase:			List[Any]					):
             '''simple docstring'''


             return self.features[i]


if is_tf_available():
   import tensorflow as tf




   class        _a    :

         """simple docstring"""


         UpperCamelCase__  		=       42
         UpperCamelCase__  		=       -100






         def __init__(		self:			Optional[Any]	,    __lowerCamelCase:			TokenClassificationTask	,    __lowerCamelCase:			str	,    __lowerCamelCase:			PreTrainedTokenizer	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			str	,    __lowerCamelCase:			Optional[int] = None	,    __lowerCamelCase:			Optional[int]=False	,    __lowerCamelCase:			Split = Split.train	,    ):
             '''simple docstring'''


             UpperCamelCase__:	Dict					=     token_classification_task.read_examples_from_file(__lowerCamelCase	,    __lowerCamelCase					)
             # TODO clean up all this to leverage built-in features of tokenizers
             UpperCamelCase__:	List[Any]					=     token_classification_task.convert_examples_to_features(
                 __lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    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=__lowerCamelCase	,    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__:	List[Any]					=     tf.data.Dataset.from_generator(
                     __lowerCamelCase	,    ({"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__:	Optional[Any]					=     tf.data.Dataset.from_generator(
                     __lowerCamelCase	,    ({"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 			UpperCAmelCase_       (		self:			Union[str, Any]					):
             '''simple docstring'''


             UpperCamelCase__:	Any					=     self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features					)					)					)

             return self.dataset






         def __len__(		self:			List[str]					):
             '''simple docstring'''


             return len(self.features					)






         def __getitem__(		self:			Optional[Any]	,    __lowerCamelCase:			Union[str, Any]					):
             '''simple docstring'''


             return self.features[i]








import math



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	Dict					=     len(A_)
    UpperCamelCase__:	Optional[Any]					=     int(math.floor(math.sqrt(A_)))
    UpperCamelCase__:	Union[str, Any]					=     0
    while arr[min(A_    ,A_) - 1] < x:
        UpperCamelCase__:	Any					=     step
        step += int(math.floor(math.sqrt(A_)))
        if prev >= n:
            return -1

    while arr[prev] < x:
        UpperCamelCase__:	Dict					=     prev + 1
        if prev == min(A_    ,A_):
            return -1
    if arr[prev] == x:
        return prev
    return -1


if __name__ == "__main__":
   A__:    Tuple  	=	input('''Enter numbers separated by a comma:\n''').strip()
   A__:    List[Any]  	=	[int(item) for item in user_input.split(''',''')]
   A__:    int  	=	int(input('''Enter the number to be searched:\n'''))
   A__:    Any  	=	jump_search(arr, x)
   if res == -1:
      print('''Number not found!''')
   else:
      print(f"Number {x} is at index {res}")

