
def     a						(							snake_case__: list ):


				'''simple docstring'''

				for insert_index, insert_value in enumerate(collection[1:] ):
								lowercase_					  = insert_index
								while insert_index >= 0 and insert_value < collection[insert_index]:
												lowercase_					  = collection[insert_index]
												insert_index -= 1
								if insert_index != temp_index:
												lowercase_					  = insert_value
				return collection


if __name__ == "__main__":
						from doctest import testmod

						testmod()

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






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 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :Any   					=     ['image_processor', 'tokenizer']
	a      :Tuple   					=     'LayoutLMv2ImageProcessor'
	a      :List[Any]   					=     ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')
	def __init__(	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							**SCREAMING_SNAKE_CASE_  :      List[str]	)	->					Optional[Any]:
					lowercase_					  = 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_	,							)
									lowercase_					  = kwargs.pop('''feature_extractor'''	)

					lowercase_					  = 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  :      Any	,							SCREAMING_SNAKE_CASE_  :      int	,							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] = False	,							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_  :      List[Any]	,							)	->					BatchEncoding:
					# verify input
					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_					  = 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_	):
													lowercase_					  = [text]  # add batch dimension (as the image processor always adds a batch dimension)
									lowercase_					  = features['''words''']

					lowercase_					  = 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
					lowercase_					  = features.pop('''pixel_values'''	)
					if return_overflowing_tokens is True:
									lowercase_					  = self.get_overflowing_images(SCREAMING_SNAKE_CASE_	,							encoded_inputs['''overflow_to_sample_mapping''']	)
					lowercase_					  = images

					return encoded_inputs
	def 						_lowercase  (	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	)	->					List[str]:
					# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
					lowercase_					  = []
					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 						_lowercase  (	self  :      List[Any]	,							*SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							**SCREAMING_SNAKE_CASE_  :      Tuple	)	->					List[str]:
					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Optional[Any]	,							*SCREAMING_SNAKE_CASE_  :      Dict	,							**SCREAMING_SNAKE_CASE_  :      List[str]	)	->					List[Any]:
					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	)
	@property
	def 						_lowercase  (	self  :      List[Any]	)	->					List[Any]:
					return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
	@property
	def 						_lowercase  (	self  :      Dict	)	->					str:
					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 						_lowercase  (	self  :      int	)	->					List[str]:
					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,							SCREAMING_SNAKE_CASE_	,							)
					return self.image_processor




