
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  _snake_case   (	UpperCamelCase_							):
			UpperCamelCase__        =       ['image_processor', 'tokenizer']
			UpperCamelCase__        =       'LayoutLMv3ImageProcessor'
			UpperCamelCase__        =       ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')







			def __init__(							self		,  _a=None		,  _a=None		,  **_a						):
				__magic_name__						:						int        =	None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."		,  __SCREAMING_SNAKE_CASE		,  )
					__magic_name__						:						str        =	kwargs.pop("feature_extractor"						)

				__magic_name__						:						Optional[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__(__SCREAMING_SNAKE_CASE		,  __SCREAMING_SNAKE_CASE						)







			def __call__(							self		,  _a		,  _a = None		,  _a = None		,  _a = None		,  _a = None		,  _a = True		,  _a = False		,  _a = None		,  _a = None		,  _a = 0		,  _a = None		,  _a = None		,  _a = None		,  _a = False		,  _a = False		,  _a = False		,  _a = False		,  _a = True		,  _a = None		,  **_a		,  ):







				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
				__magic_name__						:						int        =	self.image_processor(images=__SCREAMING_SNAKE_CASE		,  return_tensors=__SCREAMING_SNAKE_CASE						)

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

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

				# add pixel values
				__magic_name__						:						str        =	features.pop("pixel_values"						)
				if return_overflowing_tokens is True:
					__magic_name__						:						Optional[Any]        =	self.get_overflowing_images(__SCREAMING_SNAKE_CASE		,  encoded_inputs["overflow_to_sample_mapping"]						)
				__magic_name__						:						Any        =	images

				return encoded_inputs







			def      SCREAMING_SNAKE_CASE       (							self		,  _a		,  _a						):
				__magic_name__						:						int        =	[]
				for sample_idx in overflow_to_sample_mapping:
					images_with_overflow.append(images[sample_idx]						)

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

				return images_with_overflow







			def      SCREAMING_SNAKE_CASE       (							self		,  *_a		,  **_a						):
				return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE		,  **__SCREAMING_SNAKE_CASE						)







			def      SCREAMING_SNAKE_CASE       (							self		,  *_a		,  **_a						):
				return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE		,  **__SCREAMING_SNAKE_CASE						)







			@property
			def      SCREAMING_SNAKE_CASE       (							self						):
				return ["input_ids", "bbox", "attention_mask", "pixel_values"]







			@property
			def      SCREAMING_SNAKE_CASE       (							self						):
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,  __SCREAMING_SNAKE_CASE		,  )
				return self.image_processor_class







			@property
			def      SCREAMING_SNAKE_CASE       (							self						):
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,  __SCREAMING_SNAKE_CASE		,  )
				return self.image_processor


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__ = 2_0_0_0_0_0_0		):
      UpperCamelCase__     : Dict											=					[0 for i in range(n + 1		)]
      UpperCamelCase__     : Any											=					1
      UpperCamelCase__     : int											=					1

      for i in range(2			,		int(n**0.5		) + 1		):
            if primality_list[i] == 0:
                  for j in range(i * i			,		n + 1			,		UpperCamelCase__		):
                        UpperCamelCase__     : Any											=					1
      UpperCamelCase__     : Optional[Any]											=					0
      for i in range(UpperCamelCase__		):
            if primality_list[i] == 0:
                  sum_of_primes += i
      return sum_of_primes


if __name__ == "__main__":
    print(F'''{solution() = }''')


