



'''simple docstring'''






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def  UpperCamelCase__					(					_lowercase    :				Any   ,    _lowercase    :				Optional[Any]   ,    _lowercase    :				Optional[int]   ,    _lowercase    :				int     )							->	Union[str, Any]:
  if isinstance(_lowercase   ,    _lowercase     ):
    __UpperCAmelCase:					Optional[Any]		= np.full((len(_lowercase     ), sequence_length, 2)   ,    _lowercase     )
  else:
    __UpperCAmelCase:					Optional[Any]		= np.full((len(_lowercase     ), sequence_length)   ,    _lowercase     )

  for i, tensor in enumerate(_lowercase     ):
    if padding_side == "right":
      if isinstance(_lowercase   ,    _lowercase     ):
        __UpperCAmelCase:					Optional[Any]		= tensor[:sequence_length]
      else:
        __UpperCAmelCase:					List[str]		= tensor[:sequence_length]
    else:
      if isinstance(_lowercase   ,    _lowercase     ):
        __UpperCAmelCase:					Union[str, Any]		= tensor[:sequence_length]
      else:
        __UpperCAmelCase:					int		= tensor[:sequence_length]

  return out_tensor.tolist()






def  UpperCamelCase__					(					_lowercase    :				Dict     )							->	List[str]:
  __UpperCAmelCase:					Any		= ord(_lowercase     )
  if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
    return True
  __UpperCAmelCase:					Any		= unicodedata.category(_lowercase     )
  if cat.startswith("""P"""     ):
    return True
  return False







@dataclass
class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					4_2
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					-1_0_0
  __lowerCAmelCase				      =					"""pt"""







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    import torch

    __UpperCAmelCase:					List[str]		= """label""" if """label""" in features[0].keys() else """labels"""
    __UpperCAmelCase:					int		= [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __UpperCAmelCase:					List[Any]		= self.tokenizer.pad(
        snake_case_   ,    padding=self.padding   ,    max_length=self.max_length   ,    pad_to_multiple_of=self.pad_to_multiple_of   ,    return_tensors="""pt""" if labels is None else None   ,    )

    if labels is None:
      return batch

    __UpperCAmelCase:					Optional[int]		= torch.tensor(batch["""entity_ids"""]					).shape[1]
    __UpperCAmelCase:					int		= self.tokenizer.padding_side
    if padding_side == "right":
      __UpperCAmelCase:					Optional[Any]		= [
          list(snake_case_					) + [self.label_pad_token_id] * (sequence_length - len(snake_case_					)) for label in labels
      ]
    else:
      __UpperCAmelCase:					Optional[Any]		= [
          [self.label_pad_token_id] * (sequence_length - len(snake_case_					)) + list(snake_case_					) for label in labels
      ]

    __UpperCAmelCase:					Any		= [feature["""ner_tags"""] for feature in features]
    __UpperCAmelCase:					Optional[int]		= padding_tensor(snake_case_   ,    -1   ,    snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					Tuple		= [feature["""original_entity_spans"""] for feature in features]
    __UpperCAmelCase:					List[str]		= padding_tensor(snake_case_   ,    (-1, -1)   ,    snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					Optional[Any]		= {k: torch.tensor(snake_case_   ,    dtype=torch.intaa					) for k, v in batch.items()}

    return batch
'''simple docstring'''






from __future__ import annotations






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				int   ,    _lowercase    :				int   ,    _lowercase    :				int     )							->	None:
  if (direction == 1 and array[indexa] > array[indexa]) or (
      direction == 0 and array[indexa] < array[indexa]
  ):
    __UpperCAmelCase, __UpperCAmelCase:					str		= array[indexa], array[indexa]






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				int   ,    _lowercase    :				int   ,    _lowercase    :				int     )							->	None:
  if length > 1:
    __UpperCAmelCase:					List[str]		= int(length / 2     )
    for i in range(_lowercase   ,    low + middle     ):
      comp_and_swap(_lowercase   ,    _lowercase   ,    i + middle   ,    _lowercase     )
    bitonic_merge(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase     )
    bitonic_merge(_lowercase   ,    low + middle   ,    _lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				int   ,    _lowercase    :				int   ,    _lowercase    :				int     )							->	None:
  if length > 1:
    __UpperCAmelCase:					Any		= int(length / 2     )
    bitonic_sort(_lowercase   ,    _lowercase   ,    _lowercase   ,    1     )
    bitonic_sort(_lowercase   ,    low + middle   ,    _lowercase   ,    0     )
    bitonic_merge(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			input('Enter numbers separated by a comma:\n').strip()
    SCREAMING_SNAKE_CASE_	   =			[int(item.strip()) for item in user_input.split(',')]

    bitonic_sort(unsorted, 0, len(unsorted), 1)
    print('\nSorted array in ascending order is: ', end='')
    print(*unsorted, sep=', ')

    bitonic_merge(unsorted, 0, len(unsorted), 0)
    print('Sorted array in descending order is: ', end='')
    print(*unsorted, sep=', ')