



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__	(       _A):



 """simple docstring"""

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

 def __init__(    self					:					str			,						__lowerCAmelCase					:					Any=None			,						__lowerCAmelCase					:					Any=None			,						**__lowerCAmelCase					:					Optional[int]      )   ->	Any:
    _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.'''			,						__lowerCAmelCase			,						)
       _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__(__lowerCAmelCase			,						__lowerCAmelCase      )

 def __call__(    self					:					List[Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,						__lowerCAmelCase					:					Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,						__lowerCAmelCase					:					Union[List[List[int]], List[List[List[int]]]] = None			,						__lowerCAmelCase					:					Optional[Union[List[int], List[List[int]]]] = None			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					Union[bool, str, PaddingStrategy] = False			,						__lowerCAmelCase					:					Union[bool, str, TruncationStrategy] = None			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					int = 0			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					Optional[bool] = None			,						__lowerCAmelCase					:					Optional[bool] = None			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					Optional[Union[str, TensorType]] = None			,						**__lowerCAmelCase					:					int			,						)   ->	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.'''      )

    # first, apply the image processor
    _A      =       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      =       [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=__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      =       features.pop('''pixel_values'''      )
    if return_overflowing_tokens is True:
       _A      =       self.get_overflowing_images(__lowerCAmelCase			,						encoded_inputs['''overflow_to_sample_mapping''']      )
    _A      =       images

    return encoded_inputs

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Optional[Any]      )   ->	int:
    # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
    _A      =       []
    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     snake_case_      (    self					:					List[Any]			,						*__lowerCAmelCase					:					str			,						**__lowerCAmelCase					:					str      )   ->	Optional[int]:
    return self.tokenizer.batch_decode(*__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[int]			,						*__lowerCAmelCase					:					Optional[Any]			,						**__lowerCAmelCase					:					Optional[Any]      )   ->	str:
    return self.tokenizer.decode(*__lowerCAmelCase			,						**__lowerCAmelCase      )

 @property
 def     snake_case_      (    self					:					List[str]      )   ->	Any:
    return ["input_ids", "bbox", "attention_mask", "pixel_values"]

 @property
 def     snake_case_      (    self					:					Optional[Any]      )   ->	List[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     snake_case_      (    self					:					List[Any]      )   ->	Tuple:
    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_							(	_snake_case	:int				)  ->     int:
   if not isinstance(_snake_case	,   _snake_case				):
      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()



