
"""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 			_lowercase  (							__UpperCAmelCase						):
	lowercase_								=      ['image_processor', 'tokenizer']
	lowercase_								=      'LayoutLMv2ImageProcessor'
	lowercase_								=      ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')




	def __init__(					self						,					UpperCAmelCase_=None						,					UpperCAmelCase_=None						,					**UpperCAmelCase_   )						->				Any:
			lowerCamelCase	:     Any            =       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_						,					)
					lowerCamelCase	:     str            =       kwargs.pop('feature_extractor'   )

			lowerCamelCase	:     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__(UpperCAmelCase_						,					UpperCAmelCase_   )




	def __call__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = True						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = 0						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = True						,					UpperCAmelCase_ = None						,					**UpperCAmelCase_						,					)						->				BatchEncoding:
			# 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	:     Optional[Any]            =       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_   ):
							lowerCamelCase	:     Dict            =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
					lowerCamelCase	:     List[Any]            =       features['words']

			lowerCamelCase	:     int            =       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
			lowerCamelCase	:     Tuple            =       features.pop('pixel_values'   )
			if return_overflowing_tokens is True:
					lowerCamelCase	:     Any            =       self.get_overflowing_images(UpperCAmelCase_						,					encoded_inputs['overflow_to_sample_mapping']   )
			lowerCamelCase	:     Dict            =       images

			return encoded_inputs




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Optional[int]:
			# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
			lowerCamelCase	:     Dict            =       []
			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      _UpperCamelCase       (					self						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				Any:
			return self.tokenizer.batch_decode(*UpperCAmelCase_						,					**UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				List[str]:
			return self.tokenizer.decode(*UpperCAmelCase_						,					**UpperCAmelCase_   )




	@property
	def      _UpperCamelCase       (					self   )						->				Any:
			return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




	@property
	def      _UpperCamelCase       (					self   )						->				Any:
			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      _UpperCamelCase       (					self   )						->				Dict:
			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   (						a_     ):





		'''simple docstring'''


		if not isinstance(a_,		a_     ):
				raise TypeError('recursive_lucas_number accepts only integer arguments.'     )
		if n_th_number == 0:
				return 2
		if n_th_number == 1:
				return 1

		return recursive_lucas_number(n_th_number - 1     ) + recursive_lucas_number(
		    n_th_number - 2     )
def 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


		if not isinstance(a_,		a_     ):
				raise TypeError('dynamic_lucas_number accepts only integer arguments.'     )
		lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       2, 1
		for _ in range(a_     ):
				lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       b, a + b
		return a


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

					testmod()
					_A				=					int(input('Enter the number of terms in lucas series:\n').strip())
					print('Using recursive function to calculate lucas series:')
					print(' '.join(str(recursive_lucas_number(i)) for i in range(n)))
					print('\nUsing dynamic function to calculate lucas series:')
					print(' '.join(str(dynamic_lucas_number(i)) for i in range(n)))






