






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 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					['image_processor', 'tokenizer']
						A     :   Dict						=					'LayoutLMv2ImageProcessor'
						A     :   Optional[Any]						=					('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				**_SCREAMING_SNAKE_CASE				) ->				str:
								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		,				)
										snake_case_				:							Dict						 =       kwargs.pop("feature_extractor"				)

								snake_case_				:							Optional[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__(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = 0		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				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
								snake_case_				:							Union[str, Any]						 =       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				):
												snake_case_				:							Optional[Any]						 =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
										snake_case_				:							str						 =       features["words"]

								snake_case_				:							Optional[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
								snake_case_				:							Dict						 =       features.pop("pixel_values"				)
								if return_overflowing_tokens is True:
										snake_case_				:							List[Any]						 =       self.get_overflowing_images(_SCREAMING_SNAKE_CASE		,				encoded_inputs["overflow_to_sample_mapping"]				)
								snake_case_				:							List[str]						 =       images

								return encoded_inputs






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






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								return ["input_ids", "bbox", "attention_mask", "image"]






						@property
						def       _lowerCAmelCase       (	self				) ->				Dict:
								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       _lowerCAmelCase       (	self				) ->				Optional[int]:
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor








import math

def      lowerCAmelCase__	(    _a       :			int    ):

		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5  ,      int(math.sqrt(_a    ) + 1    )  ,      6    ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True

def      lowerCAmelCase__	(    _a       :			float = 0.1    ):
		snake_case_				:							int						 =       3
		snake_case_				:							Optional[Any]						 =       3

		while primes / (2 * j - 1) >= ratio:
				for i in range(j * j + j + 1  ,      (j + 2) * (j + 2)  ,      j + 1    ):
						primes += is_prime(_a    )
				j += 2
		return j


if __name__ == "__main__":
							import doctest

							doctest.testmod()
