



'''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 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  ['image_processor', 'tokenizer']
			A__								=  'LayoutLMv2ImageProcessor'
			A__								=  ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



			def __init__(     self		:						str    ,			__UpperCamelCase		:						List[Any]=None    ,			__UpperCamelCase		:						Dict=None    ,			**__UpperCamelCase		:						int			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   str      =    None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''    ,			__UpperCamelCase    ,			)
											snake_case__      :   Optional[int]      =    kwargs.pop('''feature_extractor'''			)

							snake_case__      :   Union[str, 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__(__UpperCamelCase    ,			__UpperCamelCase			)



			def __call__(     self		:						Tuple    ,			__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] = False    ,			__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		:						Tuple    ,			)      ->      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.'''			)

							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
							snake_case__      :   Tuple      =    self.image_processor(images=__UpperCamelCase    ,			return_tensors=__UpperCamelCase			)

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

							snake_case__      :   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=__UpperCamelCase    ,			add_special_tokens=__UpperCamelCase    ,			padding=__UpperCamelCase    ,			truncation=__UpperCamelCase    ,			max_length=__UpperCamelCase    ,			stride=__UpperCamelCase    ,			pad_to_multiple_of=__UpperCamelCase    ,			return_token_type_ids=__UpperCamelCase    ,			return_attention_mask=__UpperCamelCase    ,			return_overflowing_tokens=__UpperCamelCase    ,			return_special_tokens_mask=__UpperCamelCase    ,			return_offsets_mapping=__UpperCamelCase    ,			return_length=__UpperCamelCase    ,			verbose=__UpperCamelCase    ,			return_tensors=__UpperCamelCase    ,			**__UpperCamelCase    ,			)

							# add pixel values
							snake_case__      :   Union[str, Any]      =    features.pop('''pixel_values'''			)
							if return_overflowing_tokens is True:
											snake_case__      :   Tuple      =    self.get_overflowing_images(__UpperCamelCase    ,			encoded_inputs['''overflow_to_sample_mapping''']			)
							snake_case__      :   List[Any]      =    images

							return encoded_inputs



			def 				lowerCAmelCase (     self		:						int    ,			__UpperCamelCase		:						List[str]    ,			__UpperCamelCase		:						Any			)      ->      Optional[int]:
							"""simple docstring"""





							snake_case__      :   Optional[int]      =    []
							for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]			)

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

							return images_with_overflow



			def 				lowerCAmelCase (     self		:						Tuple    ,			*__UpperCamelCase		:						Optional[int]    ,			**__UpperCamelCase		:						int			)      ->      List[Any]:
							"""simple docstring"""





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



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			*__UpperCamelCase		:						str    ,			**__UpperCamelCase		:						Any			)      ->      Union[str, Any]:
							"""simple docstring"""





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



			@property
			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Tuple:
							"""simple docstring"""





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



			@property
			def 				lowerCAmelCase (     self		:						List[str]			)      ->      str:
							"""simple docstring"""





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







			@property
			def 				lowerCAmelCase (     self		:						Dict			)      ->      str:
							"""simple docstring"""





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









'''simple docstring'''



def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:list			)		->						list:
				snake_case__      :   List[str]      =    sorted(UpperCamelCase__   ,				key=lambda UpperCamelCase__			: value[2]   ,				reverse=UpperCamelCase__			)

				# Create a list of size equal to the maximum deadline
				# and initialize it with -1
				snake_case__      :   Any      =    max(UpperCamelCase__   ,				key=lambda UpperCamelCase__			: value[1]			)[1]
				snake_case__      :   str      =    [-1] * max_deadline

				# Finding the maximum profit and the count of jobs
				snake_case__      :   List[str]      =    0
				snake_case__      :   Optional[Any]      =    0
				for job in jobs:
								# Find a free time slot for this job
								# (Note that we start from the last possible slot)
								for i in range(job[1] - 1   ,				-1   ,				-1			):
												if time_slots[i] == -1:
																snake_case__      :   Optional[Any]      =    job[0]
																count += 1
																max_profit += job[2]
																break
				return [count, max_profit]


if __name__ == "__main__":
				import doctest

				doctest.testmod()




