
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  (      __UpperCamelCase ):
      _A						:		Any    	=     ['image_processor', 'tokenizer']
      _A						:		Tuple    	=     'LayoutLMv3ImageProcessor'
      _A						:		int    	=     ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')
      def __init__(			self		,	lowerCamelCase=None		,	lowerCamelCase=None		,	**lowerCamelCase ):
          snake_case__						=				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		,	)
              snake_case__						=				kwargs.pop("feature_extractor" )

          snake_case__						=				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		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = True		,	lowerCamelCase = False		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = 0		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = False		,	lowerCamelCase = False		,	lowerCamelCase = False		,	lowerCamelCase = False		,	lowerCamelCase = True		,	lowerCamelCase = None		,	**lowerCamelCase		,	):
          # 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
          snake_case__						=				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 ):
                  snake_case__						=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
              snake_case__						=				features["words"]

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

          return encoded_inputs
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          snake_case__						=				[]
          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 A_			(			self		,	*lowerCamelCase		,	**lowerCamelCase ):
          return self.tokenizer.batch_decode(*lowerCamelCase		,	**lowerCamelCase )
      def A_			(			self		,	*lowerCamelCase		,	**lowerCamelCase ):
          return self.tokenizer.decode(*lowerCamelCase		,	**lowerCamelCase )
      @property
      def A_			(			self ):
          return ["input_ids", "bbox", "attention_mask", "pixel_values"]
      @property
      def A_			(			self ):
          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 A_			(			self ):
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,	lowerCamelCase		,	)
          return self.image_processor





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    if (voltage, current, resistance).count(0							) != 1:
        raise ValueError("One and only one argument must be 0"							)
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"							)
    if voltage == 0:
        return {"voltage": float(current * resistance							)}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



