




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     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       ['''image_processor''', '''tokenizer''']
			snake_case_   		=       '''LayoutLMv2ImageProcessor'''
			snake_case_   		=       ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')


			def __init__(      self			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						**lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'			,						lowerCamelCase__			,						)
											__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__(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__			,						)    ->      BatchEncoding:



							'''simple docstring'''
							# 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=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__							):
															__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=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
							__lowerCamelCase		       =    features.pop('pixel_values'							)
							if return_overflowing_tokens is True:
											__lowerCamelCase		       =    self.get_overflowing_images(lowerCamelCase__			,						encoded_inputs['overflow_to_sample_mapping']							)
							__lowerCamelCase		       =    images

							return encoded_inputs


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Optional[int]:



							'''simple docstring'''
							# 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(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       lowercase_    (      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							return self.tokenizer.batch_decode(*lowerCamelCase__			,						**lowerCamelCase__							)


			def       lowercase_    (      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__			,						**lowerCamelCase__							)


			@property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							return ["input_ids", "bbox", "attention_mask", "image"]


			@property
			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							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       lowercase_    (      self							)    ->      List[Any]:



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










def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[1]

				lowercase__ ,						lowercase__ ,						lowercase__    =			0, 0, 0
				lowercase__    =			ugly_nums[ia] * 2
				lowercase__    =			ugly_nums[ia] * 3
				lowercase__    =			ugly_nums[ia] * 5

				for _ in range(1		,   lowerCamelCase_				):
								lowercase__    =			min(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				)
								ugly_nums.append(lowerCamelCase_				)
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 2
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 3
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 5
				return ugly_nums[-1]


if __name__ == "__main__":
							from doctest import testmod

							testmod(verbose=True)
							print(F"{ugly_numbers(2_00) = }")




