"""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 			_snake_case  (       a__		):
						snake_case__      	=      ["image_processor", "tokenizer"]
						snake_case__      	=      "LayoutLMv2ImageProcessor"
						snake_case__      	=      ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")






						def __init__(							self   :      Any				,       UpperCAmelCase   :      Optional[Any]=None				,       UpperCAmelCase   :      Dict=None				,       **UpperCAmelCase   :      Any     ):
													if "feature_extractor" in kwargs:
																				warnings.warn(
																				    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																				    " instead."				,       A_				,       )
																				__lowerCamelCase       :		Optional[int]		 =  kwargs.pop("feature_extractor"     )

													__lowerCamelCase       :		List[Any]		 =  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   :      List[str]				,       UpperCAmelCase   :      Any				,       UpperCAmelCase   :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,       UpperCAmelCase   :      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				,       UpperCAmelCase   :      Union[List[List[int]], List[List[List[int]]]] = None				,       UpperCAmelCase   :      Optional[Union[List[int], List[List[int]]]] = None				,       UpperCAmelCase   :      bool = True				,       UpperCAmelCase   :      Union[bool, str, PaddingStrategy] = False				,       UpperCAmelCase   :      Union[bool, str, TruncationStrategy] = None				,       UpperCAmelCase   :      Optional[int] = None				,       UpperCAmelCase   :      int = 0				,       UpperCAmelCase   :      Optional[int] = None				,       UpperCAmelCase   :      Optional[bool] = None				,       UpperCAmelCase   :      Optional[bool] = None				,       UpperCAmelCase   :      bool = False				,       UpperCAmelCase   :      bool = False				,       UpperCAmelCase   :      bool = False				,       UpperCAmelCase   :      bool = False				,       UpperCAmelCase   :      bool = True				,       UpperCAmelCase   :      Optional[Union[str, TensorType]] = None				,       **UpperCAmelCase   :      Any				,       ):
													# 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."     )

													if return_overflowing_tokens is True and return_offsets_mapping is False:
																				raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."     )

													# first, apply the image processor
													__lowerCamelCase       :		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_     ):
																											__lowerCamelCase       :		Dict		 =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
																				__lowerCamelCase       :		Dict		 =  features["words"]

													__lowerCamelCase       :		str		 =  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
													__lowerCamelCase       :		Tuple		 =  features.pop("pixel_values"     )
													if return_overflowing_tokens is True:
																				__lowerCamelCase       :		Union[str, Any]		 =  self.get_overflowing_images(A_				,       encoded_inputs["overflow_to_sample_mapping"]     )
													__lowerCamelCase       :		int		 =  images

													return encoded_inputs






						def    lowerCamelCase__  (							self   :      Optional[Any]				,       UpperCAmelCase   :      Any				,       UpperCAmelCase   :      Tuple     ):
													# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
													__lowerCamelCase       :		Dict		 =  []
													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    lowerCamelCase__  (							self   :      Optional[Any]				,       *UpperCAmelCase   :      str				,       **UpperCAmelCase   :      Dict     ):
													return self.tokenizer.batch_decode(*A_				,       **A_     )






						def    lowerCamelCase__  (							self   :      Union[str, Any]				,       *UpperCAmelCase   :      Optional[Any]				,       **UpperCAmelCase   :      Tuple     ):
													return self.tokenizer.decode(*A_				,       **A_     )






						@property
						def    lowerCamelCase__  (							self   :      Union[str, Any]     ):
													return ["input_ids", "bbox", "attention_mask", "image"]






						@property
						def    lowerCamelCase__  (							self   :      Optional[Any]     ):
													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    lowerCamelCase__  (							self   :      List[Any]     ):
													warnings.warn(
													    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,       A_				,       )
													return self.image_processor



"""simple docstring"""




def       a_ (	lowercase__ :int = 10**9     ):
			__lowerCamelCase          =      1
			__lowerCamelCase          =      2

			__lowerCamelCase          =      0
			__lowerCamelCase          =      0
			__lowerCamelCase          =      0
			while perimeter <= max_perimeter:
						perimeters_sum += perimeter

						prev_value += 2 * value
						value += prev_value

						__lowerCamelCase          =      2 * value + 2 if i % 2 == 0 else 2 * value - 2
						i += 1

			return perimeters_sum


if __name__ == "__main__":
					print(f"""{solution() = }""")




