"""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  _lowerCamelCase			(__lowerCamelCase     ):
							_snake_case     =  ["image_processor", "tokenizer"]
							_snake_case     =  "LayoutLMv2ImageProcessor"
							_snake_case     =  ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")







							def __init__( self    :       Dict			,					lowerCamelCase_    :       List[Any]=None			,					lowerCamelCase_    :       int=None			,					**lowerCamelCase_    :       Dict						):

								"""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_			,					)
									_lowercase :    int							=							kwargs.pop('feature_extractor'						)

								_lowercase :    int							=							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    :       Optional[Any]			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,					lowerCamelCase_    :       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,					lowerCamelCase_    :       Union[List[List[int]], List[List[List[int]]]] = None			,					lowerCamelCase_    :       Optional[Union[List[int], List[List[int]]]] = None			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Union[bool, str, PaddingStrategy] = False			,					lowerCamelCase_    :       Union[bool, str, TruncationStrategy] = None			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       int = 0			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Optional[Union[str, TensorType]] = None			,					**lowerCamelCase_    :       Tuple			,					):

								"""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.'						)

								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
								_lowercase :    Optional[int]							=							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_						):
										_lowercase :    Tuple							=							[text]  # add batch dimension (as the image processor always adds a batch dimension)
									_lowercase :    Tuple							=							features['words']

								_lowercase :    List[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=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
								_lowercase :    Tuple							=							features.pop('pixel_values'						)
								if return_overflowing_tokens is True:
									_lowercase :    Dict							=							self.get_overflowing_images(lowerCamelCase_			,					encoded_inputs['overflow_to_sample_mapping']						)
								_lowercase :    Optional[Any]							=							images

								return encoded_inputs







							def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       List[str]						):

								"""simple docstring"""






								_lowercase :    Any							=							[]
								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 							__UpperCAmelCase					( self    :       Optional[int]			,					*lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       List[Any]						):

								"""simple docstring"""






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







							def 							__UpperCAmelCase					( self    :       Optional[Any]			,					*lowerCamelCase_    :       str			,					**lowerCamelCase_    :       Optional[Any]						):

								"""simple docstring"""






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







							@property
							def 							__UpperCAmelCase					( self    :       str						):

								"""simple docstring"""






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







							@property
							def 							__UpperCAmelCase					( self    :       Any						):

								"""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 							__UpperCAmelCase					( self    :       str						):

								"""simple docstring"""






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




"""simple docstring"""







import functools


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if not isinstance(__UpperCAmelCase     ,__UpperCAmelCase			) or not all(isinstance(__UpperCAmelCase     ,__UpperCAmelCase			) for day in days			):
		raise ValueError('The parameter days should be a list of integers'			)

	if len(__UpperCAmelCase			) != 3 or not all(isinstance(__UpperCAmelCase     ,__UpperCAmelCase			) for cost in costs			):
		raise ValueError('The parameter costs should be a list of three integers'			)

	if len(__UpperCAmelCase			) == 0:
		return 0

	if min(__UpperCAmelCase			) <= 0:
		raise ValueError('All days elements should be greater than 0'			)

	if max(__UpperCAmelCase			) >= 366:
		raise ValueError('All days elements should be less than 366'			)

	_lowercase :    List[Any]							=							set(__UpperCAmelCase			)

	@functools.cache
	def dynamic_programming(__UpperCAmelCase			) -> int:
		if index > 365:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1			)

		return min(
		    costs[0] + dynamic_programming(index + 1			)     ,costs[1] + dynamic_programming(index + 7			)     ,costs[2] + dynamic_programming(index + 30			)     ,)

	return dynamic_programming(1			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



