"""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        UpperCamelCase		(			lowercase_  ):
   lowercase							=  ['image_processor', 'tokenizer']
   lowercase							=  'LayoutLMv2ImageProcessor'
   lowercase							=  ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')
   def __init__(			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase			)	->      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     ,)
             lowercase_	:				Any													=	kwargs.pop('feature_extractor'			)

        lowercase_	:				Any													=	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
        lowercase_	:				Optional[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			):
                  lowercase_	:				Union[str, Any]													=	[text]  # add batch dimension (as the image processor always adds a batch dimension)
             lowercase_	:				Union[str, Any]													=	features['words']

        lowercase_	:				int													=	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
        lowercase_	:				Union[str, Any]													=	features.pop('pixel_values'			)
        if return_overflowing_tokens is True:
             lowercase_	:				Dict													=	self.get_overflowing_images(__UpperCamelCase     ,encoded_inputs['overflow_to_sample_mapping']			)
        lowercase_	:				int													=	images

        return encoded_inputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				Optional[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 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        return self.tokenizer.batch_decode(*__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      int:


        '''simple docstring'''


        return self.tokenizer.decode(*__UpperCamelCase     ,**__UpperCamelCase			)
   @property
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        return ["input_ids", "bbox", "attention_mask", "image"]
   @property
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''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 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


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


"""simple docstring"""







def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list      ):

     if len(__SCREAMING_SNAKE_CASE      ) < 2:
          return collection

     def circle_sort_util(__SCREAMING_SNAKE_CASE							:     list     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ) -> bool:
          lowercase_	:				Any													=	False

          if low == high:
               return swapped

          lowercase_	:				str													=	low
          lowercase_	:				int													=	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_	:				Dict													=	(
                   collection[right + 1],
                   collection[left],
               )

               lowercase_	:				str													=	True

          lowercase_	:				Optional[Any]													=	low + int((high - low) / 2      )
          lowercase_	:				str													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
          lowercase_	:				List[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			mid + 1     ,			__SCREAMING_SNAKE_CASE      )

          return swapped or left_swap or right_swap

     lowercase_	:				Dict													=	True

     while is_not_sorted is True:
          lowercase_	:				Optional[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			0     ,			len(__SCREAMING_SNAKE_CASE      ) - 1      )

     return collection


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

