
"""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 						a__		( a_		):
   __lowerCAmelCase				=							["""image_processor""", """tokenizer"""]
   __lowerCAmelCase				=							"""LayoutLMv3ImageProcessor"""
   __lowerCAmelCase				=							("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")
   def __init__(     self      ,       _a=None      ,       _a=None      ,       **_a							):
          lowercase       :							List[str]									=				None
          if "feature_extractor" in kwargs:
                 warnings.warn(
                     "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                     " instead."      ,       _a      ,       )
                 lowercase       :							List[str]									=				kwargs.pop("feature_extractor"							)

          lowercase       :							Tuple									=				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__(_a      ,       _a							)
   def __call__(     self      ,       _a      ,       _a = None      ,       _a = None      ,       _a = None      ,       _a = None      ,       _a = True      ,       _a = False      ,       _a = None      ,       _a = None      ,       _a = 0      ,       _a = None      ,       _a = None      ,       _a = None      ,       _a = False      ,       _a = False      ,       _a = False      ,       _a = False      ,       _a = True      ,       _a = None      ,       **_a      ,       ):
          # 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
          lowercase       :							Optional[Any]									=				self.image_processor(images=_a      ,       return_tensors=_a							)

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

          lowercase       :							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=_a      ,       add_special_tokens=_a      ,       padding=_a      ,       truncation=_a      ,       max_length=_a      ,       stride=_a      ,       pad_to_multiple_of=_a      ,       return_token_type_ids=_a      ,       return_attention_mask=_a      ,       return_overflowing_tokens=_a      ,       return_special_tokens_mask=_a      ,       return_offsets_mapping=_a      ,       return_length=_a      ,       verbose=_a      ,       return_tensors=_a      ,       **_a      ,       )

          # add pixel values
          lowercase       :							Dict									=				features.pop("pixel_values"							)
          if return_overflowing_tokens is True:
                 lowercase       :							Tuple									=				self.get_overflowing_images(_a      ,       encoded_inputs["overflow_to_sample_mapping"]							)
          lowercase       :							Optional[int]									=				images

          return encoded_inputs
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          lowercase       :							List[Any]									=				[]
          for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx]							)

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

          return images_with_overflow
   def      __magic_name__     (     self      ,       *_a      ,       **_a							):
          return self.tokenizer.batch_decode(*_a      ,       **_a							)
   def      __magic_name__     (     self      ,       *_a      ,       **_a							):
          return self.tokenizer.decode(*_a      ,       **_a							)
   @property
   def      __magic_name__     (     self							):
          return ["input_ids", "bbox", "attention_mask", "pixel_values"]
   @property
   def      __magic_name__     (     self							):
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,       _a      ,       )
          return self.image_processor_class

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




"""simple docstring"""




from __future__ import annotations

from math import pow, sqrt


def     __magic_name__      ( __snake_case							:			float	,					__snake_case							:			float	,					__snake_case							:			float    )					->							dict[str, float]:
       if (resistance, reactance, impedance).count(0    ) != 1:
              raise ValueError("One and only one argument must be 0"    )
       if resistance == 0:
              return {"resistance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif reactance == 0:
              return {"reactance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif impedance == 0:
              return {"impedance": sqrt(pow(__snake_case	,					2    ) + pow(__snake_case	,					2    )    )}
       else:
              raise ValueError("Exactly one argument must be 0"    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


