
"""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			:       Optional[int]   		=       logging.getLogger(__name__)

@dataclass
class 		a__						:
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42


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


class 		a__						(      a_						):
   __lowerCAmelCase            =				"""train"""
   __lowerCAmelCase            =				"""dev"""
   __lowerCAmelCase            =				"""test"""


class 		a__						:
   @staticmethod
   def 			__magic_name__			(			_a	,    _a				):
          raise NotImplementedError
   @staticmethod
   def 			__magic_name__			(			_a				):
          raise NotImplementedError

   @staticmethod
   def 			__magic_name__			(			_a	,    _a	,    _a	,    _a	,    _a=False	,    _a="[CLS]"	,    _a=1	,    _a="[SEP]"	,    _a=False	,    _a=False	,    _a=0	,    _a=0	,    _a=-100	,    _a=0	,    _a=True	,    ):
          lowercase    :				Optional[Any]       		=							{label: i for i, label in enumerate(_a				)}

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

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

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

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

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

                 lowercase    :				Dict       		=							tokenizer.convert_tokens_to_ids(_a				)

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

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

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

                 if "token_type_ids" not in tokenizer.model_input_names:
                        lowercase    :				List[Any]       		=							None

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


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

   class 		a__						(      a_						):
      __lowerCAmelCase            =				42
      __lowerCAmelCase            =				nn.CrossEntropyLoss().ignore_index
      def __init__(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a = None	,    _a=False	,    _a = Split.train	,    ):
             # Load data features from cache or dataset file
             lowercase    :				Union[str, Any]       		=							os.path.join(
                 _a	,    "cached_{}_{}_{}".format(mode.value	,    tokenizer.__class__.__name__	,    str(_a				)				)	,    )

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

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


if is_tf_available():
   import tensorflow as tf

   class 		a__						:
      __lowerCAmelCase            =				42
      __lowerCAmelCase            =				-100
      def __init__(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a = None	,    _a=False	,    _a = Split.train	,    ):
             lowercase    :				Dict       		=							token_classification_task.read_examples_from_file(_a	,    _a				)
             # TODO clean up all this to leverage built-in features of tokenizers
             lowercase    :				Optional[Any]       		=							token_classification_task.convert_examples_to_features(
                 _a	,    _a	,    _a	,    _a	,    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=_a	,    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    :				str       		=							tf.data.Dataset.from_generator(
                        _a	,    ({"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    :				Any       		=							tf.data.Dataset.from_generator(
                        _a	,    ({"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 			__magic_name__			(			self				):
             lowercase    :				str       		=							self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features				)				)				)

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

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




"""simple docstring"""




def   __magic_name__	(      __snake_case				:       list  ) ->				list:

       if len(__snake_case  ) < 2:
              return collection

       def circle_sort_util(__snake_case				:       list     , __snake_case				:       int     , __snake_case				:       int  ) -> bool:
              lowercase    :				List[Any]       		=							False

              if low == high:
                     return swapped

              lowercase    :				Union[str, Any]       		=							low
              lowercase    :				str       		=							high

              while left < right:
                     if collection[left] > collection[right]:
                            lowercase		,    lowercase    :				Optional[Any]       		=							(
                                collection[right],
                                collection[left],
                            )
                            lowercase    :				Tuple       		=							True

                     left += 1
                     right -= 1

              if left == right and collection[left] > collection[right + 1]:
                     lowercase		,    lowercase    :				str       		=							(
                         collection[right + 1],
                         collection[left],
                     )

                     lowercase    :				Union[str, Any]       		=							True

              lowercase    :				Any       		=							low + int((high - low) / 2  )
              lowercase    :				Tuple       		=							circle_sort_util(__snake_case     , __snake_case     , __snake_case  )
              lowercase    :				List[Any]       		=							circle_sort_util(__snake_case     , mid + 1     , __snake_case  )

              return swapped or left_swap or right_swap

       lowercase    :				int       		=							True

       while is_not_sorted is True:
              lowercase    :				int       		=							circle_sort_util(__snake_case     , 0     , len(__snake_case  ) - 1  )

       return collection


if __name__ == "__main__":
   _A			:       str   		=       input("""Enter numbers separated by a comma:\n""").strip()
   _A			:       Dict   		=       [int(item) for item in user_input.split(""",""")]
   print(circle_sort(unsorted))


