


"""simple docstring"""



import math







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  Optional[int] = 0						,							lowercase  :  Dict = 0			)     ->						str:


  """simple docstring"""





  snake_case   :				Tuple				 =   end or len(_lowercase			)
  for i in range(_lowercase						,							_lowercase			):
    snake_case   :				Dict				 =   i
    snake_case   :				Optional[int]				 =   array[i]
    while temp_index != start and temp_index_value < array[temp_index - 1]:
      snake_case   :				Union[str, Any]				 =   array[temp_index - 1]
      temp_index -= 1
    snake_case   :				int				 =   temp_index_value
  return array







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  str						,							lowercase  :  List[str]			)     ->						int:  # Max Heap


  """simple docstring"""





  snake_case   :				int				 =   index
  snake_case   :				Optional[int]				 =   2 * index + 1  # Left Node
  snake_case   :				str				 =   2 * index + 2  # Right Node

  if left_index < heap_size and array[largest] < array[left_index]:
    snake_case   :				int				 =   left_index

  if right_index < heap_size and array[largest] < array[right_index]:
    snake_case   :				Tuple				 =   right_index

  if largest != index:
    snake_case ,snake_case   :				Optional[int]				 =   array[largest], array[index]
    heapify(_lowercase						,							_lowercase						,							_lowercase			)







def   __lowerCAmelCase						(						lowercase  :  int			)     ->						Optional[int]:


  """simple docstring"""





  snake_case   :				Tuple				 =   len(_lowercase			)

  for i in range(n // 2						,							-1						,							-1			):
    heapify(_lowercase						,							_lowercase						,							_lowercase			)

  for i in range(n - 1						,							0						,							-1			):
    snake_case ,snake_case   :				str				 =   array[0], array[i]
    heapify(_lowercase						,							0						,							_lowercase			)

  return array







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  Dict						,							lowercase  :  Optional[int]						,							lowercase  :  Tuple			)     ->						Optional[Any]:


  """simple docstring"""





  if (array[first_index] > array[middle_index]) != (
      array[first_index] > array[last_index]
  ):
    return array[first_index]
  elif (array[middle_index] > array[first_index]) != (
      array[middle_index] > array[last_index]
  ):
    return array[middle_index]
  else:
    return array[last_index]







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Union[str, Any]						,							lowercase  :  Optional[Any]						,							lowercase  :  int			)     ->						Dict:


  """simple docstring"""





  snake_case   :				List[str]				 =   low
  snake_case   :				List[Any]				 =   high
  while True:
    while array[i] < pivot:
      i += 1
    j -= 1
    while pivot < array[j]:
      j -= 1
    if i >= j:
      return i
    snake_case ,snake_case   :				List[str]				 =   array[j], array[i]
    i += 1







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]			)     ->						List[Any]:


  """simple docstring"""





  if len(_lowercase			) == 0:
    return array
  snake_case   :				str				 =   2 * math.ceil(math.loga(len(_lowercase			)			)			)
  snake_case   :				List[Any]				 =   16
  return intro_sort(_lowercase						,							0						,							len(_lowercase			)						,							_lowercase						,							_lowercase			)







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Dict						,							lowercase  :  Any						,							lowercase  :  Optional[int]						,							lowercase  :  List[str]			)     ->						str:


  """simple docstring"""





  while end - start > size_threshold:
    if max_depth == 0:
      return heap_sort(_lowercase			)
    max_depth -= 1
    snake_case   :				str				 =   median_of_a(_lowercase						,							_lowercase						,							start + ((end - start) // 2) + 1						,							end - 1			)
    snake_case   :				Tuple				 =   partition(_lowercase						,							_lowercase						,							_lowercase						,							_lowercase			)
    intro_sort(_lowercase						,							_lowercase						,							_lowercase						,							_lowercase						,							_lowercase			)
    snake_case   :				Tuple				 =   p
  return insertion_sort(_lowercase						,							_lowercase						,							_lowercase			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __snake_case             =   input("""Enter numbers separated by a comma : """).strip()
  __snake_case             =   [float(item) for item in user_input.split(""",""")]
  print(sort(unsorted))









"""simple docstring"""



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	List[str]							  =  ['''image_processor''', '''tokenizer''']
   __UpperCAmelCase			:	str							  =  '''LayoutLMv2ImageProcessor'''
   __UpperCAmelCase			:	Dict							  =  ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     **UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."						,     UpperCamelCase__						,     )
           snake_case   :				Dict				 =   kwargs.pop("feature_extractor"						)

       snake_case   :				int				 =   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."						)
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."						)

       super().__init__(UpperCamelCase__						,     UpperCamelCase__						)







   def __call__(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     UpperCamelCase__ = False						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = 0						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		BatchEncoding:

       '''simple docstring'''




       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes "
               "if you initialized the image processor with apply_ocr set to True."						)

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."						)

       # first, apply the image processor
       snake_case   :				Any				 =   self.image_processor(images=UpperCamelCase__						,     return_tensors=UpperCamelCase__						)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(UpperCamelCase__						,     UpperCamelCase__						):
               snake_case   :				Optional[int]				 =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
           snake_case   :				Optional[Any]				 =   features["words"]

       snake_case   :				Dict				 =   self.tokenizer(
           text=text if text is not None else features["words"]						,     text_pair=text_pair if text_pair is not None else None						,     boxes=boxes if boxes is not None else features["boxes"]						,     word_labels=UpperCamelCase__						,     add_special_tokens=UpperCamelCase__						,     padding=UpperCamelCase__						,     truncation=UpperCamelCase__						,     max_length=UpperCamelCase__						,     stride=UpperCamelCase__						,     pad_to_multiple_of=UpperCamelCase__						,     return_token_type_ids=UpperCamelCase__						,     return_attention_mask=UpperCamelCase__						,     return_overflowing_tokens=UpperCamelCase__						,     return_special_tokens_mask=UpperCamelCase__						,     return_offsets_mapping=UpperCamelCase__						,     return_length=UpperCamelCase__						,     verbose=UpperCamelCase__						,     return_tensors=UpperCamelCase__						,     **UpperCamelCase__						,     )

       # add pixel values
       snake_case   :				Dict				 =   features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           snake_case   :				Any				 =   self.get_overflowing_images(UpperCamelCase__						,     encoded_inputs["overflow_to_sample_mapping"]						)
       snake_case   :				str				 =   images

       return encoded_inputs







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

       if len(UpperCamelCase__						) != len(UpperCamelCase__						):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F' {len(UpperCamelCase__						)} and {len(UpperCamelCase__						)}'						)

       return images_with_overflow







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       return self.tokenizer.batch_decode(*UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       return self.tokenizer.decode(*UpperCamelCase__						,     **UpperCamelCase__						)







   @property
   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       return ["input_ids", "bbox", "attention_mask", "image"]







   @property
   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,     UpperCamelCase__						,     )
       return self.image_processor_class







   @property
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,     UpperCamelCase__						,     )
       return self.image_processor





