
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			):
				snake_case_						   =    ['''image_processor''', '''tokenizer''']
				snake_case_						   =    '''LayoutLMv3ImageProcessor'''
				snake_case_						   =    ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								__a										=    None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''		,     _snake_case		,     )
												__a										=    kwargs.pop('''feature_extractor'''				)

								__a										=    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__(_snake_case		,     _snake_case				)




				def __call__(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = True		,     _snake_case = False		,     _snake_case = None		,     _snake_case = None		,     _snake_case = 0		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = False		,     _snake_case = False		,     _snake_case = False		,     _snake_case = False		,     _snake_case = True		,     _snake_case = None		,     **_snake_case		,     )    ->      BatchEncoding:


								'''simple docstring'''




								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
								__a										=    self.image_processor(images=_snake_case		,     return_tensors=_snake_case				)

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

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

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

								return encoded_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								__a										=    []
								for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx]				)

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

								return images_with_overflow




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      int:


								'''simple docstring'''




								return self.tokenizer.batch_decode(*_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								return self.tokenizer.decode(*_snake_case		,     **_snake_case				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								return ["input_ids", "bbox", "attention_mask", "pixel_values"]




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,     _snake_case		,     )
								return self.image_processor_class




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		,     _snake_case		,     )
								return self.image_processor

from __future__ import annotations

import typing
from collections import Counter


def     __lowerCAmelCase			(			a__  )       ->  typing.Counter[int]:
				__a										=    Counter()
				for base in range(1						,    max_perimeter + 1  ):
								for perpendicular in range(a__						,    max_perimeter + 1  ):
												__a										=    (base * base + perpendicular * perpendicular) ** 0.5
												if hypotenuse == int(a__  ):
																__a										=    int(base + perpendicular + hypotenuse  )
																if perimeter > max_perimeter:
																				continue
																triplets[perimeter] += 1
				return triplets


def     __lowerCAmelCase			(			a__ = 1000  )       ->  int:
				__a										=    pythagorean_triple(a__  )
				return triplets.most_common(1  )[0][0]


if __name__ == "__main__":
					print(F"Perimeter {solution()} has maximum solutions")