


'''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      ):







				"""simple docstring"""



				__UpperCamelCase    :		Optional[Any]	=	['image_processor', 'tokenizer']
				__UpperCamelCase    :		Tuple	=	'LayoutLMv2ImageProcessor'
				__UpperCamelCase    :		Any	=	('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')




				def __init__(self       ,			__lowercase=None       ,			__lowercase=None       ,			**__lowercase		):
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''       ,			__lowercase       ,			)
																__lowerCAmelCase							       =				kwargs.pop('''feature_extractor'''		)

										__lowerCAmelCase							       =				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__(__lowercase       ,			__lowercase		)




				def __call__(self       ,			__lowercase       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = True       ,			__lowercase = False       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = 0       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = False       ,			__lowercase = False       ,			__lowercase = False       ,			__lowercase = False       ,			__lowercase = True       ,			__lowercase = None       ,			**__lowercase       ,			):
										# 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							       =				self.image_processor(images=__lowercase       ,			return_tensors=__lowercase		)

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

										__lowerCAmelCase							       =				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=__lowercase       ,			add_special_tokens=__lowercase       ,			padding=__lowercase       ,			truncation=__lowercase       ,			max_length=__lowercase       ,			stride=__lowercase       ,			pad_to_multiple_of=__lowercase       ,			return_token_type_ids=__lowercase       ,			return_attention_mask=__lowercase       ,			return_overflowing_tokens=__lowercase       ,			return_special_tokens_mask=__lowercase       ,			return_offsets_mapping=__lowercase       ,			return_length=__lowercase       ,			verbose=__lowercase       ,			return_tensors=__lowercase       ,			**__lowercase       ,			)

										# add pixel values
										__lowerCAmelCase							       =				features.pop('''pixel_values'''		)
										if return_overflowing_tokens is True:
																__lowerCAmelCase							       =				self.get_overflowing_images(__lowercase       ,			encoded_inputs['''overflow_to_sample_mapping''']		)
										__lowerCAmelCase							       =				images

										return encoded_inputs




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase		):
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										__lowerCAmelCase							       =				[]
										for sample_idx in overflow_to_sample_mapping:
																images_with_overflow.append(images[sample_idx]		)

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

										return images_with_overflow




				def 					_snake_case  (self       ,			*__lowercase       ,			**__lowercase		):
										return self.tokenizer.batch_decode(*__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			*__lowercase       ,			**__lowercase		):
										return self.tokenizer.decode(*__lowercase       ,			**__lowercase		)




				@property
				def 					_snake_case  (self		):
										return ["input_ids", "bbox", "attention_mask", "image"]




				@property
				def 					_snake_case  (self		):
										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,			__lowercase       ,			)
										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.'''       ,			__lowercase       ,			)
										return self.image_processor





'''simple docstring'''

def 							__magic_name__(						):
						return [
						    a * b * (1_0_0_0 - a - b)
						    for a in range(1,				9_9_9)
						    for b in range(lowerCamelCase,				9_9_9)
						    if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
						][0]


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

