
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 	__A(				snake_case_ ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			['''image_processor''', '''tokenizer''']
  SCREAMING_SNAKE_CASE__			    =			'''LayoutLMv3ImageProcessor'''
  SCREAMING_SNAKE_CASE__			    =			('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')







  def __init__(self       ,			SCREAMING_SNAKE_CASE_=None       ,			SCREAMING_SNAKE_CASE_=None       ,			**SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				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__       ,			)
            UpperCamelCase__				=				kwargs.pop("""feature_extractor"""						)

       UpperCamelCase__				=				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       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = True       ,			SCREAMING_SNAKE_CASE_ = False       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = 0       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = False       ,			SCREAMING_SNAKE_CASE_ = False       ,			SCREAMING_SNAKE_CASE_ = False       ,			SCREAMING_SNAKE_CASE_ = False       ,			SCREAMING_SNAKE_CASE_ = True       ,			SCREAMING_SNAKE_CASE_ = None       ,			**SCREAMING_SNAKE_CASE_       ,			):




       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
       UpperCamelCase__				=				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__						):
                 UpperCamelCase__				=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
            UpperCamelCase__				=				features["words"]

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

       return encoded_inputs







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				[]
       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        UpperCAmelCase_					(self       ,			*SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						):
       return self.tokenizer.batch_decode(*UpperCamelCase__       ,			**UpperCamelCase__						)







  def        UpperCAmelCase_					(self       ,			*SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						):
       return self.tokenizer.decode(*UpperCamelCase__       ,			**UpperCamelCase__						)







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







  @property
  def        UpperCAmelCase_					(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        UpperCAmelCase_					(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						(						)	->						Union[str, Any]:


    """simple docstring"""





    snake_case    :      Dict      					=    []
    snake_case    :      List[Any]      					=    1

    while len(lowercase ) < 1e6:
        constant.append(str(lowercase ) )
        i += 1

    snake_case    :      Tuple      					=    "".join(lowercase )

    return (
        int(constant[0] )
        * int(constant[9] )
        * int(constant[99] )
        * int(constant[999] )
        * int(constant[9999] )
        * int(constant[9_9999] )
        * int(constant[99_9999] )
    )


if __name__ == "__main__":
  print(solution())





