
'''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 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Tuple				       =       ['image_processor', 'tokenizer']
	snake_case__ :Union[str, Any]				       =       'LayoutLMv2ImageProcessor'
	snake_case__ :Optional[Any]				       =       ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')





	def __init__(   self     :    List[Any] ,							__magic_name__     :    Any=None ,							__magic_name__     :    List[Any]=None ,							**__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							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__									=							kwargs.pop("feature_extractor"							)

				lowerCAmelCase__									=							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     :    int ,							__magic_name__     :    List[Any] ,							__magic_name__     :    List[Any] = None ,							__magic_name__     :    List[Any] = None ,							__magic_name__     :    List[Any] = None ,							__magic_name__     :    str = None ,							__magic_name__     :    int = True ,							__magic_name__     :    Dict = False ,							__magic_name__     :    List[str] = False ,							__magic_name__     :    str = None ,							__magic_name__     :    List[str] = 0 ,							__magic_name__     :    List[Any] = None ,							__magic_name__     :    str = None ,							__magic_name__     :    str = None ,							__magic_name__     :    List[Any] = False ,							__magic_name__     :    Optional[int] = False ,							__magic_name__     :    Union[str, Any] = False ,							__magic_name__     :    int = False ,							__magic_name__     :    str = True ,							__magic_name__     :    Union[str, Any] = None ,							**__magic_name__     :    str ,							):


				"""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
				lowerCAmelCase__									=							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__									=							[text]  # add batch dimension (as the image processor always adds a batch dimension)
							lowerCAmelCase__									=							features["words"]

				lowerCAmelCase__									=							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__									=							features.pop("pixel_values"							)
				if return_overflowing_tokens is True:
							lowerCAmelCase__									=							self.get_overflowing_images(_UpperCAmelCase ,							encoded_inputs["overflow_to_sample_mapping"]							)
				lowerCAmelCase__									=							images

				return encoded_inputs





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    Optional[int] ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							[]
				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 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any] ,							*__magic_name__     :    List[str] ,							**__magic_name__     :    Any							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							*__magic_name__     :    Dict ,							**__magic_name__     :    Optional[int]							):


				"""simple docstring"""

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





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

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





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""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 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""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'''

import math







def 					A       ( UpperCamelCase_   :       int )   ->    list:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive integers have prime factors" )
			lowerCAmelCase__									=							[]
			while n % 2 == 0:
						pf.append(2 )
						lowerCAmelCase__									=							int(n / 2 )
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						while n % i == 0:
									pf.append(UpperCamelCase_ )
									lowerCAmelCase__									=							int(n / i )
			if n > 2:
						pf.append(UpperCamelCase_ )
			return pf







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			div *= temp
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						div *= temp
			if n > 1:
						div *= 2
			return div







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			if temp > 1:
						s *= (2**temp - 1) / (2 - 1)
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						if temp > 1:
									s *= (i**temp - 1) / (i - 1)
			return int(UpperCamelCase_ )







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			lowerCAmelCase__									=							n
			for x in set(prime_factors(UpperCamelCase_ ) ):
						s *= (x - 1) / x
			return int(UpperCamelCase_ )


if __name__ == "__main__":
		import doctest

		doctest.testmod()





