


'''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__      ):





		"""simple docstring"""



		a_       :Any												=["""image_processor""", """tokenizer"""]
		a_       :Union[str, Any]												="""LayoutLMv3ImageProcessor"""
		a_       :List[Any]												=("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")

		def __init__(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=None				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=None				,							**SCREAMING_SNAKE_CASE__     :							List[str]						):





							'''simple docstring'''

							__a       =   None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
												    """ instead."""				,							SCREAMING_SNAKE_CASE__				,							)
												__a       =   kwargs.pop("""feature_extractor"""						)

							__a       =   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__(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

		def __call__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				,							SCREAMING_SNAKE_CASE__     :							Union[List[List[int]], List[List[List[int]]]] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[List[int], List[List[int]]]] = None				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							Union[bool, str, PaddingStrategy] = False				,							SCREAMING_SNAKE_CASE__     :							Union[bool, str, TruncationStrategy] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[int] = None				,							SCREAMING_SNAKE_CASE__     :							int = 0				,							SCREAMING_SNAKE_CASE__     :							Optional[int] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[bool] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[bool] = None				,							SCREAMING_SNAKE_CASE__     :							bool = False				,							SCREAMING_SNAKE_CASE__     :							bool = False				,							SCREAMING_SNAKE_CASE__     :							bool = False				,							SCREAMING_SNAKE_CASE__     :							bool = False				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[str, TensorType]] = None				,							**SCREAMING_SNAKE_CASE__     :							Any				,							):





							'''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."""						)

							# first, apply the image processor
							__a       =   self.image_processor(images=SCREAMING_SNAKE_CASE__				,							return_tensors=SCREAMING_SNAKE_CASE__						)

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

							__a       =   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=SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__				,							padding=SCREAMING_SNAKE_CASE__				,							truncation=SCREAMING_SNAKE_CASE__				,							max_length=SCREAMING_SNAKE_CASE__				,							stride=SCREAMING_SNAKE_CASE__				,							pad_to_multiple_of=SCREAMING_SNAKE_CASE__				,							return_token_type_ids=SCREAMING_SNAKE_CASE__				,							return_attention_mask=SCREAMING_SNAKE_CASE__				,							return_overflowing_tokens=SCREAMING_SNAKE_CASE__				,							return_special_tokens_mask=SCREAMING_SNAKE_CASE__				,							return_offsets_mapping=SCREAMING_SNAKE_CASE__				,							return_length=SCREAMING_SNAKE_CASE__				,							verbose=SCREAMING_SNAKE_CASE__				,							return_tensors=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__				,							)

							# add pixel values
							__a       =   features.pop("""pixel_values"""						)
							if return_overflowing_tokens is True:
												__a       =   self.get_overflowing_images(SCREAMING_SNAKE_CASE__				,							encoded_inputs["""overflow_to_sample_mapping"""]						)
							__a       =   images

							return encoded_inputs

		def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							str						):





							'''simple docstring'''

							__a       =   []
							for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx]						)

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

							return images_with_overflow

		def __a						(					self     :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE__     :							int				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





							'''simple docstring'''

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

		def __a						(					self     :							Tuple				,							*SCREAMING_SNAKE_CASE__     :							Dict				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





							'''simple docstring'''

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

		@property
		def __a						(					self     :							Optional[Any]						):





							'''simple docstring'''

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

		@property
		def __a						(					self     :							Any						):





							'''simple docstring'''

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


		@property
		def __a						(					self     :							int						):





							'''simple docstring'''

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










'''simple docstring'''







SCREAMING_SNAKE_CASE_      =			0  # The first color of the flag.
SCREAMING_SNAKE_CASE_      =			1  # The second color of the flag.
SCREAMING_SNAKE_CASE_      =			2  # The third color of the flag.
SCREAMING_SNAKE_CASE_      =			(red, white, blue)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					list:
     """simple docstring"""




     if not sequence:
          return []
     if len(__SCREAMING_SNAKE_CASE		) == 1:
          return list(__SCREAMING_SNAKE_CASE		)
     __a       =   0
     __a       =   len(__SCREAMING_SNAKE_CASE		) - 1
     __a       =   0
     while mid <= high:
          if sequence[mid] == colors[0]:
               __a ,						__a       =   sequence[mid], sequence[low]
               low += 1
               mid += 1
          elif sequence[mid] == colors[1]:
               mid += 1
          elif sequence[mid] == colors[2]:
               __a ,						__a       =   sequence[high], sequence[mid]
               high -= 1
          else:
               __a       =   F'''The elements inside the sequence must contains only {colors} values'''
               raise ValueError(__SCREAMING_SNAKE_CASE		)
     return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 SCREAMING_SNAKE_CASE_      =			input('Enter numbers separated by commas:\n').strip()
 SCREAMING_SNAKE_CASE_      =			[int(item.strip()) for item in user_input.split(',')]
 print(f"""{dutch_national_flag_sort(unsorted)}""")






