





'''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   _lowercase						(							a				):
   _UpperCamelCase       		=   ["""image_processor""", """tokenizer"""]
   _UpperCamelCase       		=   """LayoutLMv3ImageProcessor"""
   _UpperCamelCase       		=   ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")


   def __init__( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase					):
       A       :       Union[str, Any]							=						None
       if "feature_extractor" in kwargs:
           warnings.warn(
               '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
               ''' instead.'''    ,   _UpperCAmelCase    ,   )
           A       :       List[str]							=						kwargs.pop('''feature_extractor'''					)

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

       A       :       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=_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
       A       :       List[str]							=						features.pop('''pixel_values'''					)
       if return_overflowing_tokens is True:
           A       :       Any							=						self.get_overflowing_images(_UpperCAmelCase    ,   encoded_inputs['''overflow_to_sample_mapping''']					)
       A       :       Any							=						images

       return encoded_inputs


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       A       :       str							=						[]
       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 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       return self.tokenizer.batch_decode(*_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       return self.tokenizer.decode(*_UpperCAmelCase    ,   **_UpperCAmelCase					)


   @property
   def 					snake_case				( self					):
       return ["input_ids", "bbox", "attention_mask", "pixel_values"]


   @property
   def 					snake_case				( self					):
       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 					snake_case				( self					):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,   _UpperCAmelCase    ,   )
       return self.image_processor







'''simple docstring'''






def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : str		)		->  Optional[Any]:
    A       :       Tuple							=						0
    while b > 0:
        if b & 1:
            res += a

        a += a
        b >>= 1

    return res
def 							_lowerCamelCase(  UpperCamelCase__    : Any    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : List[str]		)		->  Any:
    A       :       str							=						0
    while b > 0:
        if b & 1:
            A       :       List[Any]							=						((res % c) + (a % c)) % c

        a += a
        b >>= 1

    return res
