


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 			SCREAMING_SNAKE_CASE__  (    _a      ):
      snake_case__    :					Union[str, Any]					       =						["""image_processor""", """tokenizer"""]
      snake_case__    :					Union[str, Any]					       =						"""LayoutLMv3ImageProcessor"""
      snake_case__    :					Any					       =						("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")
      def __init__(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=None					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=None					,							**SCREAMING_SNAKE_CASE__    :	Any					)		->      int:
             a_ :		int							=  None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                        ' instead.'					,							__lowerCAmelCase					,							)
                    a_ :		Union[str, Any]							=  kwargs.pop('feature_extractor'					)

             a_ :		Optional[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__(__lowerCAmelCase					,							__lowerCAmelCase					)
      def __call__(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Dict = None					,							SCREAMING_SNAKE_CASE__    :	Any = None					,							SCREAMING_SNAKE_CASE__    :	Tuple = None					,							SCREAMING_SNAKE_CASE__    :	int = None					,							SCREAMING_SNAKE_CASE__    :	str = True					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = False					,							SCREAMING_SNAKE_CASE__    :	List[Any] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = 0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any] = None					,							SCREAMING_SNAKE_CASE__    :	List[str] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = None					,							SCREAMING_SNAKE_CASE__    :	List[Any] = False					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = False					,							SCREAMING_SNAKE_CASE__    :	Optional[Any] = False					,							SCREAMING_SNAKE_CASE__    :	Optional[Any] = False					,							SCREAMING_SNAKE_CASE__    :	List[Any] = True					,							SCREAMING_SNAKE_CASE__    :	Dict = None					,							**SCREAMING_SNAKE_CASE__    :	int					,							)		->      Union[str, Any]:
             # 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.'					)

             # first, apply the image processor
             a_ :		Union[str, Any]							=  self.image_processor(images=__lowerCAmelCase					,							return_tensors=__lowerCAmelCase					)

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

             a_ :		List[Any]							=  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=__lowerCAmelCase					,							add_special_tokens=__lowerCAmelCase					,							padding=__lowerCAmelCase					,							truncation=__lowerCAmelCase					,							max_length=__lowerCAmelCase					,							stride=__lowerCAmelCase					,							pad_to_multiple_of=__lowerCAmelCase					,							return_token_type_ids=__lowerCAmelCase					,							return_attention_mask=__lowerCAmelCase					,							return_overflowing_tokens=__lowerCAmelCase					,							return_special_tokens_mask=__lowerCAmelCase					,							return_offsets_mapping=__lowerCAmelCase					,							return_length=__lowerCAmelCase					,							verbose=__lowerCAmelCase					,							return_tensors=__lowerCAmelCase					,							**__lowerCAmelCase					,							)

             # add pixel values
             a_ :		Union[str, Any]							=  features.pop('pixel_values'					)
             if return_overflowing_tokens is True:
                    a_ :		Union[str, Any]							=  self.get_overflowing_images(__lowerCAmelCase					,							encoded_inputs['overflow_to_sample_mapping']					)
             a_ :		Optional[Any]							=  images

             return encoded_inputs
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					)		->      Any:
             # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
             a_ :		Optional[int]							=  []
             for sample_idx in overflow_to_sample_mapping:
                    images_with_overflow.append(images[sample_idx]					)

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

             return images_with_overflow
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							*SCREAMING_SNAKE_CASE__    :	Tuple					,							**SCREAMING_SNAKE_CASE__    :	Dict					)		->      Any:
             return self.tokenizer.batch_decode(*__lowerCAmelCase					,							**__lowerCAmelCase					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					,							*SCREAMING_SNAKE_CASE__    :	int					,							**SCREAMING_SNAKE_CASE__    :	Any					)		->      Tuple:
             return self.tokenizer.decode(*__lowerCAmelCase					,							**__lowerCAmelCase					)
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      List[str]:
             return ["input_ids", "bbox", "attention_mask", "pixel_values"]
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      str:
             warnings.warn(
                 '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,							__lowerCAmelCase					,							)
             return self.image_processor_class

      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      int:
             warnings.warn(
                 '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,							__lowerCAmelCase					,							)
             return self.image_processor






def 	SCREAMING_SNAKE_CASE_						(      __A			:      int    )     ->		int:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input must be an integer'    )
       if input_num <= 0:
              raise ValueError('Input must be positive'    )
       return sum(
           divisor for divisor in range(1  ,   input_num // 2 + 1    ) if input_num % divisor == 0    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


